Search results for: time series fractal analysis

Authors: Soheila Sadeghi

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Accurate forecasting of project performance metrics is crucial for successfully managing and delivering urban road reconstruction projects. Traditional methods often rely on static baseline plans and fail to consider the dynamic nature of project progress and external factors. This research proposes a machine learning-based approach to forecast project performance metrics, such as cost variance and earned value, for each Work Breakdown Structure (WBS) category in an urban road reconstruction project. The proposed model utilizes time series forecasting techniques, including Autoregressive Integrated Moving Average (ARIMA) and Long Short-Term Memory (LSTM) networks, to predict future performance based on historical data and project progress. The model also incorporates external factors, such as weather patterns and resource availability, as features to enhance the accuracy of forecasts. By applying the predictive power of machine learning, the performance forecasting model enables proactive identification of potential deviations from the baseline plan, which allows project managers to take timely corrective actions. The research aims to validate the effectiveness of the proposed approach using a case study of an urban road reconstruction project, comparing the model's forecasts with actual project performance data. The findings of this research contribute to the advancement of project management practices in the construction industry, offering a data-driven solution for improving project performance monitoring and control.

Keywords: project performance forecasting, machine learning, time series forecasting, cost variance, earned value management

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Authors: Mahmoud Amer, Ibrahim El-Sharkawy, Shinichi Ookawara, Ahmed Elwardany

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Gasification and carbonization are two of the most common ways for biomass utilization. Both processes are using part of the waste to be accomplished, either by incomplete combustion or for heating for both gasification and carbonization, respectively. The focus of this paper is to minimize the part of the waste that is used for heating biomass for gasification and carbonization. This will occur by combining both gasifiers and carbonization reactors in a single unit to utilize the heat in the product biogas to heating up the wastes in the carbonization reactors. Three different designs are proposed for the combined gasification/carbonization (CGC) reactor. These include a parallel combination of two gasifiers and carbonized syngas, carbonizer and combustion chamber, and one gasifier, carbonizer, and combustion chamber. They are tested numerically using ANSYS Fluent Computational Fluid Dynamics to ensure homogeneity of temperature distribution inside the carbonization part of the CGC reactor. 2D simulations are performed for the three cases after performing both mesh-size and time-step independent solutions. The carbonization part is common among the three different cases, and the difference among them is how this carbonization reactor is heated. The simulation results showed that the first design could provide only partial homogeneous temperature distribution, not across the whole reactor. This means that the produced carbonized biomass will be reduced as it will only fill a specified height of the reactor. To keep the carbonized product production high, a series combination is proposed. This series configuration resulted in a uniform temperature distribution across the whole reactor as it has only one source for heat with no temperature distribution on any surface of the carbonization section. The simulations provided a satisfactory result that either the first parallel combination of gasifier and carbonization reactor could be used with a reduced carbonized amount or a series configuration to keep the production rate high.

Keywords: numerical simulation, carbonization, gasification, biomass, reactor

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Authors: Alma Lucero Ortiz, Mario Gomez

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Technological advances contribute to the well-being of humanity by allowing man to perform in a more efficient way. Technology offers tangible advantages to countries with the adoption of information technologies, communication, and the Internet in all social and productive sectors. The Internet is a networking infrastructure that allows the communication of people throughout the world, exceeding the limits of time and space. Nowadays the internet has changed the way of doing business leading to a digital economy. In this way, e-commerce has emerged as a commercial transaction conducted over the Internet. For this inquiry e-commerce is seen as a source of economic growth for the country. Thereby, these research aims to answer the research question, which are the main variables that have affected the development of e-commerce in Mexico. The research includes a period of study from 1990 to 2017. This inquiry aims to get insight on how the independent variables influence the e-commerce development. The independent variables are information infrastructure construction, urbanization level, economic level, technology level, human capital level, educational level, standards of living, and price index. The results suggest that the independent variables have an impact on development of the e-commerce in Mexico. The present study is carried out in five parts. After the introduction, in the second part, a literature review about the main qualitative and quantitative studies to measure the variables subject to the study is presented. After, an empirical study is applied through time series data, and to process the data an econometric model is performed. In the fourth part, the analysis and discussion of results are presented, and finally, some conclusions are included.

Keywords: digital economy, e-commerce, econometric model, economic growth, internet

Procedia PDF Downloads 222

Authors: Bouthiba Amina

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The estimation of the statistics bound to the precipitation represents a vast domain, which puts numerous challenges to meteorologists and hydrologists. Sometimes, it is necessary, to approach in value the extreme events for sites where there is little, or no datum, as well as their periods of return. The search for a model of the frequency of the heights of daily rains dresses a big importance in operational hydrology: It establishes a basis for predicting the frequency and intensity of floods by estimating the amount of precipitation in past years. The most known and the most common approach is the statistical approach, It consists in looking for a law of probability that fits best the values observed by the random variable " daily maximal rain " after a comparison of various laws of probability and methods of estimation by means of tests of adequacy. Therefore, a frequent analysis of the annual series of daily maximal rains was realized on the data of 54 pluviometric stations of the pond of high and average. This choice was concerned with five laws usually applied to the study and the analysis of frequent maximal daily rains. The chosen period is from 1970 to 2013. It was of use to the forecast of quantiles. The used laws are the law generalized by extremes to three components, those of the extreme values to two components (Gumbel and log-normal) in two parameters, the law Pearson typifies III and Log-Pearson III in three parameters. In Algeria, Gumbel's law has been used for a long time to estimate the quantiles of maximum flows. However, and we will check and choose the most reliable law.

Keywords: return period, extreme flow, statistics laws, Gumbel, estimation

Procedia PDF Downloads 66

Authors: Atish Bagchi, Siva Chandrasekaran

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Background: According to World Bank national accounts data, the estimated global manufacturing value-added output in 2020 was 13.74 trillion USD. These manufacturing processes are monitored, modelled, and controlled by advanced, real-time, computer-based systems, e.g., Industrial IoT, PLC, SCADA, etc. These systems measure and manipulate a set of physical variables, e.g., temperature, pressure, etc. Despite the use of IoT, SCADA etc., in manufacturing, studies suggest that unplanned downtime leads to economic losses of approximately 864 billion USD each year. Therefore, real-time, accurate detection, classification and prediction of machine behaviour are needed to minimise financial losses. Although vast literature exists on time-series data processing using machine learning, the challenges faced by the industries that lead to unplanned downtimes are: The current algorithms do not efficiently handle the high-volume streaming data from industrial IoTsensors and were tested on static and simulated datasets. While the existing algorithms can detect significant 'point' outliers, most do not handle contextual outliers (e.g., values within normal range but happening at an unexpected time of day) or subtle changes in machine behaviour. Machines are revamped periodically as part of planned maintenance programmes, which change the assumptions on which original AI models were created and trained. Aim: This research study aims to deliver a Graph Neural Network(GNN)based hybrid forecasting model that interfaces with the real-time machine control systemand can detect, predict machine behaviour and behavioural changes (anomalies) in real-time. This research will help manufacturing industries and utilities, e.g., water, electricity etc., reduce unplanned downtimes and consequential financial losses. Method: The data stored within a process control system, e.g., Industrial-IoT, Data Historian, is generally sampled during data acquisition from the sensor (source) and whenpersistingin the Data Historian to optimise storage and query performance. The sampling may inadvertently discard values that might contain subtle aspects of behavioural changes in machines. This research proposed a hybrid forecasting and classification model which combines the expressive and extrapolation capability of GNN enhanced with the estimates of entropy and spectral changes in the sampled data and additional temporal contexts to reconstruct the likely temporal trajectory of machine behavioural changes. The proposed real-time model belongs to the Deep Learning category of machine learning and interfaces with the sensors directly or through 'Process Data Historian', SCADA etc., to perform forecasting and classification tasks. Results: The model was interfaced with a Data Historianholding time-series data from 4flow sensors within a water treatment plantfor45 days. The recorded sampling interval for a sensor varied from 10 sec to 30 min. Approximately 65% of the available data was used for training the model, 20% for validation, and the rest for testing. The model identified the anomalies within the water treatment plant and predicted the plant's performance. These results were compared with the data reported by the plant SCADA-Historian system and the official data reported by the plant authorities. The model's accuracy was much higher (20%) than that reported by the SCADA-Historian system and matched the validated results declared by the plant auditors. Conclusions: The research demonstrates that a hybrid GNN based approach enhanced with entropy calculation and spectral information can effectively detect and predict a machine's behavioural changes. The model can interface with a plant's 'process control system' in real-time to perform forecasting and classification tasks to aid the asset management engineers to operate their machines more efficiently and reduce unplanned downtimes. A series of trialsare planned for this model in the future in other manufacturing industries.

Keywords: GNN, Entropy, anomaly detection, industrial time-series, AI, IoT, Industry 4.0, Machine Learning

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Authors: S. Hosseini, M. Ahmadi Najafabadi, M. Akhlaghi

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The health of the journal bearings is very important in preventing unforeseen breakdowns in rotary machines, and poor lubrication is one of the most important factors for producing the bearing failures. Hydrodynamic lubrication (HL), mixed lubrication (ML), and boundary lubrication (BL) are three regimes of a journal bearing lubrication. This paper uses acoustic emission (AE) measurement technique to correlate features of the AE signals to the three lubrication regimes. The transitions from HL to ML based on operating factors such as rotating speed, load, inlet oil pressure by time domain and time-frequency domain signal analysis techniques are detected, and then metal-to-metal contacts between sliding surfaces of the journal and bearing are identified. It is found that there is a significant difference between theoretical and experimental operating values that are obtained for defining the lubrication regions.

Keywords: acoustic emission technique, pressure fed journal bearing, time and frequency signal analysis, metal-to-metal contact

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Authors: Sandrieli Afornali, Adriano Keijiro Maeda, Renato Fedatto Beraldo, Carlos Alberto Mattozo, Ricardo Nascimento Brito

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BACKGROUND: The most used variety in hydrocephalus treatment is the ventriculoperitoneal shunt (VPS). However, it may fails in 20 to 70% of cases. It makes necessary to have alternative cavities for the implantation of the distal catheter. Ventriculo-atrial shunting (VAS) is described as the second option. To our knowledge, there are 121 reported cases of VGB shunt in children until 2020 and a highly variable success rate, from 25 to 100%, with an average of 63% of patients presenting good long-term results. Our goal is to evaluate the epidemiological profile of patients submitted to ventriculo-gallbladder (VGB) shunt and, through a review of literature, to compare our results with others series. METHODS: a retrospective cross-sectional observational study of a case series of nine patients. The medical records of all patients were reviewed, who underwent VGB shunt at the Hospital Pequeno Príncipe from Curitiba, Paraná, Brazil, from January 2014 to October 2022. The inclusion criteria were: patients under 17 years of age with hydrocephalus of any etiology, currently using or prior to VGB shunt. RESULTS: There were 6 (66,7%) male and 3 (33,3%) female. The average age of 73.6 months or 6.1 years at the time of surgery. They were submitted on average 5.1 VPS reviews previous to VGB shunt. Five (55,5%) had complications of VGB shunt: infection (11.1%), atony (11.1%), hypodrainage due to kinking the distal catheter in the solution (11.1%) and ventriculoenteric fistula (22.2%); all these patients were cured at surgical reapproach, and in 2 of them the VGB shunt was reimplanted. Two patients died (22.2%), and five (55,5%) patients maintained the use of VGB shunt in the follow-up period; and in 4 (44.4%) there was never need for review. CONCLUSION: VGB shunt tends to be underestimated because it is still unconventional and little publicized in literature. Our article shows a lower risk of death and similar risk of complications when compared to others altenatives shunts. We emphasize VGB shunt as a safe procedure to be the second option when VPS fails or has contraindications.

Keywords: hydrocephalus, ventricular-gallbladder shunt, VGB shunt, VPS, ventriculoperitoneal shunt, ventriculoatrial shunt

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Authors: Qi Xiu, Hiromu Hota, Yohsuke Ishii, Takuya Oda

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Recently, collectable manufacturing data are rapidly increasing. On the other hand, mega recall is getting serious as a social problem. Under such circumstances, there are increasing needs for preventing mega recalls by defect analysis such as root cause analysis and abnormal detection utilizing manufacturing data. However, the time to classify strings in manufacturing data by traditional method is too long to meet requirement of quick defect analysis. Therefore, we present String Length Distribution Classification method (SLDC) to correctly classify strings in a short time. This method learns character features, especially string length distribution from Product ID, Machine ID in BOM and asset list. By applying the proposal to strings in actual manufacturing data, we verified that the classification time of strings can be reduced by 80%. As a result, it can be estimated that the requirement of quick defect analysis can be fulfilled.

Keywords: string classification, data quality, feature selection, probability distribution, string length

Procedia PDF Downloads 309

Authors: Revalin Herdianto

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Recent climate changes increase uncertainties in vegetation conditions such as health and biomass globally and locally. The detection is, however, difficult due to the spatial and temporal scale of vegetation coverage. Due to unique vegetation response to its environmental conditions such as water availability, the interplay between vegetation dynamics and hydrologic conditions leave a signature in their feedback relationship. Vegetation indices (VI) depict vegetation biomass and photosynthetic capacity that indicate vegetation dynamics as a response to variables including hydrologic conditions and microclimate factors such as rainfall characteristics and land surface temperature (LST). It is hypothesized that the signature may be depicted by VI in its relationship with other variables. To study this signature, several catchments in Asia, Australia, and Indonesia were analysed to assess the variations in hydrologic characteristics with vegetation types. Methods used in this study includes geographic identification and pixel marking for studied catchments, analysing time series of VI and LST of the marked pixels, smoothing technique using Savitzky-Golay filter, which is effective for large area and extensive data. Time series of VI, LST, and rainfall from satellite and ground stations coupled with digital elevation models were analysed and presented. This study found that the hydrologic response of vegetation to rainfall variations may be shown in one hydrologic year, in which a drought event can be detected a year later as a suppressed growth. However, an annual rainfall of above average do not promote growth above average as shown by VI. This technique is found to be a robust and tractable approach for assessing catchment dynamics in changing climates.

Keywords: vegetation indices, land surface temperature, vegetation dynamics, catchment

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Authors: Anca Radulescu, Danae Evans

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The relationship between a network’s hardwiring and its emergent dynamics are central to neuroscience. We study the principles of this correspondence in a canonical setup (in which network nodes exhibit well-studied complex quadratic dynamics), then test their universality in biological networks. By extending methods from discrete dynamics, we study the effects of network connectivity on temporal patterns, encapsulating long-term behavior into the rich topology of network Mandelbrot sets. Then elements of fractal geometry can be used to predict and classify network behavior.

Keywords: canonical model, complex dynamics, dynamic networks, fractals, Mandelbrot set, network connectivity

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Authors: Hayriye Anıl, Görkem Kar

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In addition to being rich in renewable energy resources, Turkey is one of the countries that promise potential in geothermal energy production with its high installed power, cheapness, and sustainability. Increasing imbalance penalties become an economic burden for organizations since geothermal generation plants cannot maintain the balance of supply and demand due to the inadequacy of the production forecasts given in the day-ahead market. A better production forecast reduces the imbalance penalties of market participants and provides a better imbalance in the day ahead market. In this study, using machine learning, deep learning, and, time series methods, the total generation of the power plants belonging to Zorlu Natural Electricity Generation, which has a high installed capacity in terms of geothermal, was estimated for the first one and two weeks of March, then the imbalance penalties were calculated with these estimates and compared with the real values. These modeling operations were carried out on two datasets, the basic dataset and the dataset created by extracting new features from this dataset with the feature engineering method. According to the results, Support Vector Regression from traditional machine learning models outperformed other models and exhibited the best performance. In addition, the estimation results in the feature engineering dataset showed lower error rates than the basic dataset. It has been concluded that the estimated imbalance penalty calculated for the selected organization is lower than the actual imbalance penalty, optimum and profitable accounts.

Keywords: machine learning, deep learning, time series models, feature engineering, geothermal energy production forecasting

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Authors: Ashneel Ajay Singh, Naoki Suzuki, Kazumi Sakuramoto,

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The North Pacific albacore (Thunnus alalunga) is a temperate tuna species distributed in the North Pacific which is of significant economic importance to the Pacific Island Nations and Territories. Despite its importance, the stock dynamics and ecological characteristics of albacore still, have gaps in knowledge. The stock-recruitment relationship of the North Pacific stock of albacore tuna was investigated for different density-dependent effects and a regime shift in the stock characteristics in response to changes in environmental and climatic conditions. Linear regression analysis for recruit per spawning biomass (RPS) and recruitment (R) against the female spawning stock biomass (SSB) were significant for the presence of different density-dependent effects and positive for a regime shift in the stock time series. Application of Deming regression to RPS against SSB with the assumption for the presence of observation and process errors in both the dependent and independent variables confirmed the results of simple regression. However, R against SSB results disagreed given variance level of < 3 and agreed with linear regression results given the assumption of variance ≥ 3. Assuming the presence of different density-dependent effects in the albacore tuna time series, environmental and climatic condition variables were compared with R, RPS, and SSB. The significant relationship of R, RPS and SSB were determined with the sea surface temperature (SST), Pacific Decadal Oscillation (PDO) and multivariate El Niño Southern Oscillation (ENSO) with SST being the principal variable exhibiting significantly similar trend with R and RPS. Recruitment is significantly influenced by the dynamics of the SSB as well as environmental conditions which demonstrates that the stock-recruitment relationship is multidimensional. Further investigation of the North Pacific albacore tuna age-class and structure is necessary for further support the results presented here. It is important for fishery managers and decision makers to be vigilant of regime shifts in environmental conditions relating to albacore tuna as it may possibly cause regime shifts in the albacore R and RPS which should be taken into account to effectively and sustainability formulate harvesting plans and management of the species in the North Pacific oceanic region.

Keywords: Albacore tuna, Thunnus alalunga, recruitment, spawning stock biomass, recruits per spawning biomass, sea surface temperature, pacific decadal oscillation, El Niño southern oscillation, density-dependent effects, regime shift

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Authors: Ioannis P. Panapakidis, Marios N. Moschakis

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The Greek Energy Market is structured as a mandatory pool where the producers make their bid offers in day-ahead basis. The System Operator solves an optimization routine aiming at the minimization of the cost of produced electricity. The solution of the optimization problem leads to the calculation of the System Marginal Price (SMP). Accurate forecasts of the SMP can lead to increased profits and more efficient portfolio management from the producer`s perspective. Aim of this study is to provide a comparative analysis of various machine learning models such as artificial neural networks and neuro-fuzzy models for the prediction of the SMP of the Greek market. Machine learning algorithms are favored in predictions problems since they can capture and simulate the volatilities of complex time series.

Keywords: deregulated energy market, forecasting, machine learning, system marginal price

Procedia PDF Downloads 201

Authors: Labar Hocine, Kelaiaia Mounia Samira, Mesbah Tarek, Kelaiaia Samia

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Maximum power point tracking (MPPT) techniques are used in photovoltaic (PV) systems to maximize the PV array output power by tracking continuously the maximum power point(MPP) which depends on panels temperature and on irradiance conditions. The main drawback of P&O is that, the operating point oscillates around the MPP giving rise to the waste of some amount of available energy; moreover, it is well known that the P&O algorithm can be confused during those time intervals characterized by rapidly changing atmospheric conditions. In this paper, it is shown that in order to limit the negative effects associated to the above drawbacks, the P&O MPPT parameters must be customized to the dynamic behavior of the specific converter adopted. A theoretical analysis allowing the optimal choice of such initial set parameters is also carried out. The fast convergence of the proposal is proven.

Keywords: P&O, Taylor’s series, MPPT, photovoltaic panel

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Authors: Daniel Hristov

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The paper describes the principle of operation, simulation and physical validation of method for simultaneous acquisition of gain and phase states of multiple antenna elements and the corresponding feed lines across a Phased Array Antenna (PAA). The derived values for gain and phase are used for PAA-calibration. The method utilizes the Rotating-Element Electric- Field Vector (REV) principle currently used for gain and phase state estimation of single antenna element across an active antenna aperture. A significant reduction of procedure execution time is achieved with simultaneous setting of different phase delays to multiple phase shifters, followed by a single power measurement. The initial gain and phase states are calculated using spectral and correlation analysis of the measured power series.

Keywords: antenna, antenna arrays, calibration, phase measurement, power measurement

Procedia PDF Downloads 126

Authors: Katyana A. Vert-Pre, James T. Thorson, Thomas Trancart, Eric Feunteun

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In marine ecosystems, spatial and temporal species structure is an important component of ecosystems’ response to anthropological and environmental factors. Although spatial distribution patterns and fish temporal series of abundance have been studied in the past, little research has been allocated to the joint dynamic spatio-temporal functional patterns in marine ecosystems and their use in multispecies management and conservation. Each species represents a function to the ecosystem, and the distribution of these species might not be random. A heterogeneous functional distribution will lead to a more resilient ecosystem to external factors. Applying a Vector-Autoregressive Spatio-Temporal (VAST) model for count data, we estimate the spatio-temporal distribution, shift in time, and abundance of 140 species of the Eastern English Chanel, Bay of Biscay and Mediterranean Sea. From the model outputs, we determined spatio-temporal clusters, calculating p-values for hierarchical clustering via multiscale bootstrap resampling. Then, we designed a functional map given the defined cluster. We found that the species distribution within the ecosystem was not random. Indeed, species evolved in space and time in clusters. Moreover, these clusters remained similar over time deriving from the fact that species of a same cluster often shifted in sync, keeping the overall structure of the ecosystem similar overtime. Knowing the co-existing species within these clusters could help with predicting data-poor species distribution and abundance. Further analysis is being performed to assess the ecological functions represented in each cluster.

Keywords: cluster distribution shift, European marine ecosystems, functional distribution, spatio-temporal model

Procedia PDF Downloads 183

Authors: Gabriela Teodorescu, Ioana Daniela Dulama

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In an ever-changing society, the education system needs to constantly evolve to meet market demands. During its 30 years of existence, Valahia University of Targoviste (VUT) tried to offer its students a series of teaching-learning schemes that would prepare them for a remarkable career. In VUT, the achievement of performance through innovation can be analyzed by reference to several key indicators (i.e., university climate, university resources, and innovative methods applied to classes), but it is possible to differentiate between activities in the classic format: participate to courses; interactive seminars and tutorials; laboratories, workshops, project-based learning; entrepreneurial activities, through simulated enterprises; mentoring activities. Thus, VUT has implemented over time a series of schemes and projects based on innovation and entrepreneurship, and in this paper, some of them will be briefly presented. All these schemes were implemented by facilitating an effective dialog with students and the opportunity to listen to their views at all levels of the University and in all fields of study, as well as by developing a partnership with students to set out priority areas. VUT demonstrates innovation and entrepreneurial capacity through its new activities for higher education, which will attract more partnerships and projects dedicated to students.

Keywords: Romania, project-based learning, entrepreneurial activities, simulated enterprises

Procedia PDF Downloads 149

Authors: H. Cano, P. Ferrer, N. Garcia, M. Popovic, J. Zapata

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Positive Reinforcement Training is a well-known methodology which has been reported frequently to be used in captive non-human primates. As a matter of fact, it is an invaluable tool for different purposes related with animal welfare, such as primate husbandry and environmental enrichment. It is also essential to perform some cognitive experiments. The main propose of this pilot study was to establish an efficient protocol to train captive olive baboons (Papio anubis). This protocol seems to be vital in the context of a larger research program in which it will be necessary to train a complete population of around 40 baboons. Baboons were studied at the Veterinary Research Farm of the University of Murcia. Temporally isolated animals were trained to perform three basic tasks. Firstly, they were required to take food prices directly from the researchers’ hands. Then a clicker sound or bridge stimulus was added each time the animal acceded to the reinforcement. Finally, they were trained to touch a target, consisted of a whip with a red ball in its end, with their hands or their nose. When the subject completed correctly this task, it was also exposed to the bridge stimulus and awarded with a food price, such as a portion of banana, orange, apple, peach or a raisin. Two protocols were tested during this experiment. In both of them, there were 6 series of 2min training periods each day. However, in the first protocol, the series consisted in 3 trials, whereas in the second one, in each series there were 5 trials. A reliable performance was obtained with only 6 days of training in the case of the 5-trials protocol. However, with the 3-trials one, 26 days of training were needed. As a result, the 5-trials protocol seems to be more effective than the 3-trials one, in order to teach these three basic tasks to olive baboons. In consequence, it will be used to train the rest of the colony.

Keywords: captive primates, olive baboon, positive reinforcement training, Papio anubis, training

Procedia PDF Downloads 111

Authors: Kehinde Peter Oyeduntan, Kayode Oshinubi

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The west African ports have been experiencing large inflow and outflow of containerized cargo in the last decades, and this has created a quest amongst the countries to attain the status of hub port for the sub-region. This study analyzed the relationship between the container throughput and Gross Domestic Products (GDP) of nine west African countries, using Simple Linear Regression (SLR), Polynomial Regression Model (PRM) and Support Vector Machines (SVM) with a time series of 20 years. The results showed that there exists a high correlation between the GDP and container throughput. The model also predicted the container throughput in west Africa for the next 20 years. The findings and recommendations presented in this research will guide policy makers and help improve the management of container ports and terminals in west Africa, thereby boosting the economy.

Keywords: container, ports, terminals, throughput

Procedia PDF Downloads 202

Authors: F. Ismael Roman Moreno, Francisca Alba Sanchez

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Over time, the plant landscape has changed as a result of the numerous events on a global and local scale that have happened. This is the case of the Mediterranean ecosystems, one of the most complex and rich in endemisms on the planet, subjected to anthropic pressures from the beginning of civilizations. The intervention in these systems together with climate changes has led to changes in diversity, tree cover, shrub, and ultimately in the structure and functioning of these ecosystems. Paleopalinology is used as a tool for analysis of pollen and non-pollen microfossils preserved in the flooded grasslands of the Middle Atlas (Morocco). This allows reconstructing the evolution of vegetation and climate, as well as providing data and reasoning to different ecological, cultural and historical processes. Although climatic and anthropic events are well documented in Europe, they are not so well documented in North Africa, which gives added value to the study area. The results obtained serve to predict the behavior and evolution of Mediterranean mountain ecosystems during the Holocene, their response to future changes, resilience, and recovery from climatic and anthropic disturbances. In the stratigraphic series analyzed, nine major events were detected, eight of which appeared to be of climatic and anthropic origin, and one unexpected, related to volcanic activity.

Keywords: anthropic, Holocene, Morocco, paleopalynology, resilience

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Authors: Yooncheol Lee, Jeongmin Kim, Kwang Ryel Ryu

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This paper deals with the establishment of a short-term operational plan for an air conditioner for efficient energy management of exhibition hall. The short-term operational plan is composed of a time series of operational schedules, which we have searched using genetic algorithms. Establishing operational schedule should be considered the future trends of the variables affecting the exhibition hall environment. To reflect continuously changing factors such as external temperature and occupant, short-term operational plans should be updated in real time. But it takes too much time to evaluate a short-term operational plan using EnergyPlus, a building emulation tool. For that reason, it is difficult to update the operational plan in real time. To evaluate the short-term operational plan, we designed prediction models based on machine learning with fast evaluation speed. This model, which was created by learning the past operational data, is accurate and fast. The collection of operational data and the verification of operational plans were made using EnergyPlus. Experimental results show that the proposed method can save energy compared to the reactive control method.

Keywords: exhibition hall, energy management, predictive model, simulation-based optimization

Procedia PDF Downloads 327

Authors: Xu Wang

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This research builds linear regressions of U.S. macroeconomic condition and volatility measures in the investment grade and high yield Credit Default Swap index spreads using monthly data from March 2009 to July 2016, to study the relationship between different dimensions of macroeconomy and overall credit risk quality. The most significant contribution of this research is systematically examining individual and joint effects of macroeconomic condition and volatility on CDX spreads by including macroeconomic time series that captures different dimensions of the U.S. economy. The industrial production index growth, non-farm payroll growth, consumer price index growth, 3-month treasury rate and consumer sentiment are introduced to capture the condition of real economic activity, employment, inflation, monetary policy and risk aversion respectively. The conditional variance of the macroeconomic series is constructed using ARMA-GARCH model and is used to measure macroeconomic volatility. The linear regression model is conducted to capture relationships between monthly average CDX spreads and macroeconomic variables. The Newey–West estimator is used to control for autocorrelation and heteroskedasticity in error terms. Furthermore, the sensitivity factor analysis and standardized coefficients analysis are conducted to compare the sensitivity of CDX spreads to different macroeconomic variables and to compare relative effects of macroeconomic condition versus macroeconomic uncertainty respectively. This research shows that macroeconomic condition can have a negative effect on CDX spread while macroeconomic volatility has a positive effect on determining CDX spread. Macroeconomic condition and volatility variables can jointly explain more than 70% of the whole variation of the CDX spread. In addition, sensitivity factor analysis shows that the CDX spread is the most sensitive to Consumer Sentiment index. Finally, the standardized coefficients analysis shows that both macroeconomic condition and volatility variables are important in determining CDX spread but macroeconomic condition category of variables have more relative importance in determining CDX spread than macroeconomic volatility category of variables. This research shows that the CDX spread can reflect the individual and joint effects of macroeconomic condition and volatility, which suggests that individual investors or government should carefully regard CDX spread as a measure of overall credit risk because the CDX spread is influenced by macroeconomy. In addition, the significance of macroeconomic condition and volatility variables, such as Non-farm Payroll growth rate and Industrial Production Index growth volatility suggests that the government, should pay more attention to the overall credit quality in the market when macroecnomy is low or volatile.

Keywords: autoregressive moving average model, credit spread puzzle, credit default swap spread, generalized autoregressive conditional heteroskedasticity model, macroeconomic conditions, macroeconomic uncertainty

Procedia PDF Downloads 154

Authors: Abhishek Kumar Chandra, Kaushal Kishor, Wasim Khan, Dhananjay Singh, M. S. Alam

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Single phase fluid flow through series of uniform microchannels connected via transition section (converging-diverging section with or without throat) was analytically and numerically studied to characterize the flow within the channel and in the transition sections. Three sets of microchannels of diameters 100, 184, and 249 μm were considered for investigation. Each set contains 10 numbers of microchannels of length 20 mm, connected to each other in series via transition sections. Transition section consists of either converging-diverging section with throat or without throat. The effect of non-uniformity in microchannels on pressure drop was determined by passing water/air through the set of channels for Reynolds number 50 to 1000. Compressibility and rarefaction effects in transition sections were also tested analytically and numerically for air flow. The analytical and numerical results show that these configurations can be used in enhancement of transport processes. However, converging-diverging section without throat shows superior performance over with throat configuration.

Keywords: contraction-expansion flow, integrated microchannel, microchannel network, single phase flow

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Authors: Santosh Kaware, Dnyandeo Patil, Moninder Modgil, Hemant Bhoir, Debendra Behera

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The Unified Field Theory has been an area of intensive research since many decades. This paper focuses on philosophy and metaphysics of unified field theory at Planck scale - and its relationship with super string theory and Quantum Vacuum Dynamic Physics. We examined the epistemology of questions such as - (1) what is the Unified Field of universe? (2) can it actually - (a) permeate the complete universe - or (b) be localized in bound regions of the universe - or, (c) extend into the extra dimensions? - -or (d) live only in extra dimensions? (3) What should be the emergent ontological properties of Unified field? (4) How the universe is manifesting through its Quantum Vacuum energies? (5) How is the space time metric coupled to the Unified field? We present a number of ansatz - which we outline below. It is proposed that the unified field possesses consciousness as well as a memory - a recording of past history - analogous to ‘Consistent Histories’ interpretation of quantum mechanics. We proposed Planck scale geometry of Unified Field with circle like topology and having 32 energy points on its periphery which are the connected to each other by 10 dimensional meta-strings which are sources for manifestation of different fundamentals forces and particles of universe through its Quantum Vacuum energies. It is also proposed that the sub energy levels of ‘Conscious Unified Field’ are used for the process of creation, preservation and rejuvenation of the universe over a period of time by means of negentropy. These epochs can be for the complete universe, or for localized regions such as galaxies or cluster of galaxies. It is proposed that Unified field operates through geometric patterns of its Quantum Vacuum energies - manifesting as various elementary particles by giving spins to zero point energy elements. Epistemological relationship between unified field theory and super-string theories is examined. Properties of ‘consciousness’ and 'memory' cascades from universe, into macroscopic objects - and further onto the elementary particles - via a fractal pattern. Other properties of fundamental particles - such as mass, charge, spin, iso-spin also spill out of such a cascade. The manifestations of the unified field can reach into the parallel universes or the ‘multi-verse’ and essentially have an existence independent of the space-time. It is proposed that mass, length, time scales of the unified theory are less than even the Planck scale - and can be called at a level which we call that of 'Super Quantum Gravity (SQG)'.

Keywords: super string theory, Planck scale geometry, negentropy, super quantum gravity

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Authors: Zouhair Haddi, Stephane Delliaux, Jean-Francois Pons, Ismail Kechaf, Jean-Claude De Haro, Mustapha Ouladsine

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Atrial fibrillation (AF) has been considered as the most common cardiac arrhythmia, and a major public health burden associated with significant morbidity and mortality. Nowadays, telemedical approaches targeting cardiac outpatients situate AF among the most challenged medical issues. The automatic, early, and fast AF detection is still a major concern for the healthcare professional. Several algorithms based on univariate analysis have been developed to detect atrial fibrillation. However, the published results do not show satisfactory classification accuracy. This work was aimed at resolving this shortcoming by proposing multivariate data analysis methods for automatic AF detection. Four publicly-accessible sets of clinical data (AF Termination Challenge Database, MIT-BIH AF, Normal Sinus Rhythm RR Interval Database, and MIT-BIH Normal Sinus Rhythm Databases) were used for assessment. All time series were segmented in 1 min RR intervals window and then four specific features were calculated. Two pattern recognition methods, i.e., Principal Component Analysis (PCA) and Learning Vector Quantization (LVQ) neural network were used to develop classification models. PCA, as a feature reduction method, was employed to find important features to discriminate between AF and Normal Sinus Rhythm. Despite its very simple structure, the results show that the LVQ model performs better on the analyzed databases than do existing algorithms, with high sensitivity and specificity (99.19% and 99.39%, respectively). The proposed AF detection holds several interesting properties, and can be implemented with just a few arithmetical operations which make it a suitable choice for telecare applications.

Keywords: atrial fibrillation, multivariate data analysis, automatic detection, telemedicine

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Authors: Mehdi Razeghi

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Background: The purpose of this case series was to quantify gait to study muscular dystrophy disease. In this research, the quantitative differences between normal and waddling gaits were assessed by force plate analysis. Methods: Nineteen myopathy patients and twenty normal subjects serving as the control group participated in this research. In this study, quantitative analyses of gait have been used to investigate the differences between the mobility of normal subjects and myopathy patients. This study was carried out at the Iranian Muscular Dystrophy Association in Boali Hospital, Tehran, Iran, from October 2015 to July 2020. Patient data were collected from Iranian Muscular Dystrophy Association members. individuals signed an informed consent form approved by the ethics committee of the Azad University. All of the gait tests were performed using a Kistler force platform. Participants walked at a self-selected speed, barefoot, independently, and without assistive devices. Results: Our findings indicate that there were no significant differences between the patients and the control group in the anterior-posterior components of the ground reaction forces; however, there were considerable differences in the force components between the groups in the medial-lateral and vertical directions of the ground reaction force. In addition, there were significant differences in the time parameters between the groups in the vertical and medial-lateral directions.

Keywords: biomechanics, force plate analysis, gait disorder, ground reaction force, kinetic analysis, myopathy disease, rehabilitation engineering

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Authors: Chui-Hsin Chen, Yu-Ting Chen

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Special concentrically braced frames is one of the seismic load resisting systems, which dissipates seismic energy when bracing members within the frames undergo yielding and buckling while sustaining their axial tension and compression load capacities. Most of the inelastic deformation of a buckling bracing member concentrates in the mid-length region. While experiencing cyclic loading, the region dissipates most of the seismic energy being input into the frame. Such a concentration makes the braces vulnerable to failure modes associated with low-cycle fatigue. In this research, a strategy to improve the cyclic behavior of the conventional steel bracing member is proposed by filling the Hollow Structural Shape (HSS) member with reinforcement. It prevents the local section from concentrating large plastic deformation caused by cyclic loading. The infill helps spread over the plastic hinge region into a wider area hence postpone the initiation of local buckling or even the rupture of the braces. The finite element method is introduced to simulate the complicated bracing member behavior and member-versus-infill interaction under cyclic loading. Fifteen 3-D-element-based models are built by ABAQUS software. The verification of the FEM model is done with unreinforced (UR) HSS bracing members’ cyclic test data and aluminum honeycomb plates’ bending test data. Numerical models include UR and filled HSS bracing members with various compactness ratios based on the specification of AISC-2016 and AISC-1989. The primary variables to be investigated include the relative bending stiffness and the material of the filling reinforcement. The distributions of von Mises stress and equivalent plastic strain (PEEQ) are used as indices to tell the strengths and shortcomings of each model. The result indicates that the change of relative bending stiffness of the infill is much more influential than the change of material in use to increase the energy dissipation capacity. Strengthen the relative bending stiffness of the reinforcement results in additional energy dissipation capacity to the extent of 24% and 46% in model based on AISC-2016 (16-series) and AISC-1989 (89-series), respectively. HSS members with infill show growth in 𝜂Local Buckling, normalized energy cumulated until the happening of local buckling, comparing to UR bracing members. The 89-series infill-reinforced members have more energy dissipation capacity than unreinforced 16-series members by 117% to 166%. The flexural rigidity of infills should be less than 29% and 13% of the member section itself for 16-series and 89-series bracing members accordingly, thereby guaranteeing the spread over of the plastic hinge and the happening of it within the reinforced section. If the parameters are properly configured, the ductility, energy dissipation capacity, and fatigue-life of HSS SCBF bracing members can be improved prominently by the infill-reinforced method.

Keywords: special concentrically braced frames, HSS, cyclic loading, infill reinforcement, finite element analysis, PEEQ

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Authors: Bernard Moeketsi Hlalele

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Drought is a complicated natural phenomenon that creates significant economic, social, and environmental problems. An analysis of paleoclimatic data indicates that severe and extended droughts are inevitable part of natural climatic circle. This study characterised drought in Lejweleputswa using both Standardised Precipitation Index (SPI) and neural networks (NN) to quantify and predict respectively. Monthly 37-year long time series precipitation data were obtained from online NASA database. Prior to the final analysis, this dataset was checked for outliers using SPSS. Outliers were removed and replaced by Expectation Maximum algorithm from SPSS. This was followed by both homogeneity and stationarity tests to ensure non-spurious results. A non-parametric Mann Kendall's test was used to detect monotonic trends present in the dataset. Two temporal scales SPI-3 and SPI-12 corresponding to agricultural and hydrological drought events showed statistically decreasing trends with p-value = 0.0006 and 4.9 x 10⁻⁷, respectively. The study area has been plagued with severe drought events on SPI-3, while on SPI-12, it showed approximately a 20-year circle. The concluded the analyses with a seasonal analysis that showed no significant trend patterns, and as such NN was used to predict possible SPI-3 for the last season of 2018/2019 and four seasons for 2020. The predicted drought intensities ranged from mild to extreme drought events to come. It is therefore recommended that farmers, agri-business owners, and other relevant stakeholders' resort to drought resistant crops as means of adaption.

Keywords: drought, risk, neural networks, agri-businesses, project, Lejweleputswa

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Authors: Justin Durham, Chanda Rooney, Robert Mather, Mickie Vanhoy

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The psychological construct of empathy is to understand a person’s cognitive perspective and experience the other person’s emotional state. Deciphering emotional states is conducive for interpreting vicarious pain. Observing others' physical pain activates neural networks related to the actual experience of pain itself. The study addresses empathy as a nonlinear dynamic process of simulation for individuals to understand the mental states of others and experience vicarious pain, exhibiting self-organized criticality. Such criticality follows from a combination of neural networks with an excitatory feedback loop generating bistability to resonate permutated empathy. Cortical networks exhibit diverse patterns of activity, including oscillations, synchrony and waves, however, the temporal dynamics of neurophysiological activities underlying empathic processes remain poorly understood. Mu rhythms are EEG oscillations with dominant frequencies of 8-13 Hz becoming synchronized when the body is relaxed with eyes open and when the sensorimotor system is in idle, thus, mu rhythm synchrony is expected to be highest in baseline conditions. When the sensorimotor system is activated either by performing or simulating action, mu rhythms become suppressed or desynchronize, thus, should be suppressed while observing video clips of painful injuries if previous research on mirror system activation holds. Twelve undergraduates contributed EEG data and survey responses to empathy and psychopathy scales in addition to watching consecutive video clips of sports injuries. Participants watched a blank, black image on a computer monitor before and after observing a video of consecutive sports injuries incidents. Each video condition lasted five-minutes long. A BIOPAC MP150 recorded EEG signals from sensorimotor and thalamocortical regions related to a complex neural network called the ‘pain matrix’. Physical and social pain are activated in this network to resonate vicarious pain responses to processing empathy. Five EEG single electrode locations were applied to regions measuring sensorimotor electrical activity in microvolts (μV) to monitor mu rhythms. EEG signals were sampled at a rate of 200 Hz. Mu rhythm desynchronization was measured via 8-13 Hz at electrode sites (F3 & F4). Data for each participant’s mu rhythms were analyzed via Fast Fourier Transformation (FFT) and multifractal time series analysis.

Keywords: desynchronization, dynamical systems theory, electroencephalography (EEG), empathy, multifractal time series analysis, mu waveform, neurophysiology, pain simulation, social cognition

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Authors: Onyinyechi Joy Kingdom

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Mergers and Acquisitions (M&A) have been proposed as a mechanism through which, problems associated with inefficiency or poor performance in financial institution could be addressed. The aim of this study is to examine the proposition that recapitalization of banks, which encouraged Mergers and Acquisitions in Nigeria banking system, would strengthen the domestic banks, improve financial deepening and the confidence of depositors. Hence, this study examines the impact of the 2005 M&A in the Nigerian-banking sector on financial deepening using mixed method (quantitative and qualitative approach). The quantitative process of this study utilised annual time series for financial deepening indicator for the period of 1997 to 2012. While, the qualitative aspect adopted semi-structured interview to collate data from three merged banks and three stand-alone banks to explore, understand and complement the quantitative results. Furthermore, a framework thematic analysis is employed to analyse the themes developed using NVivo 11 software. Using the quantitative approach, findings from the equality of mean test (EMT) used suggests that M&A have significant impact on financial deepening. However, this method is not robust enough given its weak validity as it does not control for other potential factors that may determine financial deepening. Thus, to control for other factors that may affect the level of financial deepening, a Multiple Regression Model (MRM) and Interrupted Times Series Analysis (ITSA) were applied. The coefficient for M&A dummy turned negative and insignificant using MRM. In addition, the estimated linear trend of the post intervention when ITSA was applied suggests that after M&A, the level of financial deepening decreased annually; however, this was statistically insignificant. Similarly, using the qualitative approach, the results from the interview supported the quantitative results from ITSA and MRM. The result suggests that interest rate should fall when capital base is increased to improve financial deepening. Hence, this study contributes to the existing literature the importance of other factors that may affect financial deepening and the economy when policies that will enhance bank performance and the economy are made. In addition, this study will enable the use of valuable policy instruments relevant to monetary authorities when formulating policies that will strengthen the Nigerian banking sector and the economy.

Keywords: mergers and acquisitions, recapitalization, financial deepening, efficiency, financial crisis

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