Search results for: stochastic volatility model

Authors: Ethel E. Phiri, Lionel Hartzenberg, Percy Chimwamuromba, Emmanuel Nepolo, Jens Kossmann, James R. Lloyd

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Orphan crops are underresearched and underutilized food plant species that have not been categorized as major food crops, but have the potential to be economically and agronomically significant. They have been documented to have the ability to tolerate extreme environmental conditions. However, limited research has been conducted to uncover their potential as food crop species. The New Partnership for Africa’s Development (NEPAD) has classified Marama bean, Tylosema esculentum, as an orphan crop. The plant is one of the 101 African orphan crops that must have their genomes sequenced, assembled, and annotated in the foreseeable future. Marama bean is a perennial leguminous plant that primarily grows in poor, arid soils in southern Africa. The plants produce large tubers that can weigh as much as 200kg. While the foliage provides fodder, the tuber is carbohydrate rich and is a staple food source for rural communities in Namibia. Also, the edible seeds are protein- and oil-rich. Marama Bean plants respond rapidly to increased temperatures and severe water scarcity without extreme consequences. Advances in molecular biology and biotechnology have made it possible to effectively transfer technologies between model- and major crops to orphan crops. In this research, the aim was to assemble the first transcriptomic analysis of Marama Bean RNA-sequence data. Many model plant species have had their genomes sequenced and their transcriptomes assembled. Therefore the availability of transcriptome data for a non-model crop plant species will allow for gene identification and comparisons between various species. The data has been sequenced using the Ilumina Hiseq 2500 sequencing platform. Data analysis is underway. In essence, this research will eventually evaluate the potential use of Marama Bean as a crop species to improve its value in agronomy. data for a non-model crop plant species will allow for gene identification and comparisons between various species. The data has been sequenced using the Ilumina Hiseq 2500 sequencing platform. Data analysis is underway. In essence, this researc will eventually evaluate the potential use of Marama bean as a crop species to improve its value in agronomy.

Keywords: 101 African orphan crops, RNA-Seq, Tylosema esculentum, underutilised crop plants

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Authors: Isaac K. E. Ampomah, Seong-Bae Park, Sang-Jo Lee

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Over the past decade, there have been promising developments in Natural Language Processing (NLP) with several investigations of approaches focusing on Recognizing Textual Entailment (RTE). These models include models based on lexical similarities, models based on formal reasoning, and most recently deep neural models. In this paper, we present a sentence encoding model that exploits the sentence-to-sentence relation information for RTE. In terms of sentence modeling, Convolutional neural network (CNN) and recurrent neural networks (RNNs) adopt different approaches. RNNs are known to be well suited for sequence modeling, whilst CNN is suited for the extraction of n-gram features through the filters and can learn ranges of relations via the pooling mechanism. We combine the strength of RNN and CNN as stated above to present a unified model for the RTE task. Our model basically combines relation vectors computed from the phrasal representation of each sentence and final encoded sentence representations. Firstly, we pass each sentence through a convolutional layer to extract a sequence of higher-level phrase representation for each sentence from which the first relation vector is computed. Secondly, the phrasal representation of each sentence from the convolutional layer is fed into a Bidirectional Long Short Term Memory (Bi-LSTM) to obtain the final sentence representations from which a second relation vector is computed. The relations vectors are combined and then used in then used in the same fashion as attention mechanism over the Bi-LSTM outputs to yield the final sentence representations for the classification. Experiment on the Stanford Natural Language Inference (SNLI) corpus suggests that this is a promising technique for RTE.

Keywords: deep neural models, natural language inference, recognizing textual entailment (RTE), sentence-to-sentence relation

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Authors: Salman Iqbal

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Knowledge management (KM) literature has mainly focused on the antecedents of KM. The purpose of this study is to investigate the effect of specific human resource management (HRM) practices on employee knowledge sharing and its outcome as individual knowledge capability. Based on previous literature, a model is proposed for the study and hypotheses are formulated. The cross-sectional dataset comes from a sample of 19 knowledge intensive firms (KIFs). This study has run an item parceling technique followed by Confirmatory Factor Analysis (CFA) on the latent constructs of the research model. Employees’ collaboration and their interpersonal trust can help to improve their knowledge sharing behaviour and knowledge capability within organisations. This study suggests that in future, by using a larger sample, better statistical insight is possible. The findings of this study are beneficial for scholars, policy makers and practitioners. The empirical results of this study are entirely based on employees’ perceptions and make a significant research contribution, given there is a dearth of empirical research focusing on the subcontinent.

Keywords: employees’ collaboration, individual knowledge capability, knowledge sharing, monetary rewards, structural equation modelling

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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: Juliana Barcelos Cordeiro, Khashayar Mahani, Farbod Farzan, Mohsen A. Jafari

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Energy disaggregation has been focused by many energy companies since energy efficiency can be achieved when the breakdown of energy consumption is known. Companies have been investing in technologies to come up with software and/or hardware solutions that can provide this type of information to the consumer. On the other hand, not all people can afford to have these technologies. Therefore, in this paper, we present a methodology for breaking down the aggregate consumption and identifying the highdemanding end-uses profiles. These energy profiles will be used to build the forecast model for optimal control purpose. A facility with high cooling load is used as an illustrative case study to demonstrate the results of proposed methodology. We apply a high level energy disaggregation through a pattern recognition approach in order to extract the consumption profile of its rooftop packaged units (RTUs) and present a forecast model for the energy consumption.  

Keywords: energy consumption forecasting, energy efficiency, load disaggregation, pattern recognition approach

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Authors: Walid Fantazi

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The development of sensor technology (such as microelectromechanical systems (MEMS), wireless communications, embedded systems, distributed processing and wireless sensor applications) has contributed to a broad range of WSN applications which are capable of collecting a large amount of spatiotemporal data in real time. These systems require real-time data processing to manage storage in real time and query the data they process. In order to cover these needs, we propose in this paper a Snapshot spatiotemporal data model based on object-oriented concepts. This model allows saving storing and reducing data redundancy which makes it easier to execute spatiotemporal queries and save analyzes time. Further, to ensure the robustness of the system as well as the elimination of congestion from the main access memory we propose a spatiotemporal indexing technique in RAM called Captree *. As a result, we offer an RIA (Rich Internet Application) -based SOA application architecture which allows the remote monitoring and control.

Keywords: WSN, indexing data, SOA, RIA, geographic information system

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Authors: Ishay Wolf

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In this study, the financial position of pension actors in the market during the pension system transition toward a more funded capitalized scheme is explored, mainly via an option benefit model. This is enabled by not considering the economy as a single earning cohort. We analytically demonstrate a socio-economic anomaly in the funded pension system, which is in favor of high earning cohorts on at the expense of low earning cohorts. This anomaly is realized by a lack of insurance and exposure to financial and systemic risks. Furthermore, the anomaly might lead to pension re-reform back to unfunded scheme, mostly due to political pressure. We find that a minimum pension guarantee is a rebalance mechanism to this anomaly, which increases the probability to of the sustainable pension scheme. Specifically, we argue that implementing the guarantee with an intra-generational, risk-sharing mechanism is the most efficient way to reduce the effect of this abnormality. Moreover, we exhibit the convergence process toward implementing minimum pension guarantee in many countries which have capitalized their pension systems during the last three decades, particularly among Latin America and CEE countries.

Keywords: benefits, pension scheme, put option, social security

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Authors: Rominkumar Busa, Shahira K. C., Lijiya A.

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Text recognition is an important area in computer vision which deals with detecting and recognising text from an image. The Optical Character Recognition (OCR) is a saturated area these days and with very good text recognition accuracy. However the same OCR methods when applied on text with small font sizes like the text data of chart images, the recognition rate is less than 30%. In this work, aims to extract small text in images using the deep learning model, CRNN with CTC loss. The text recognition accuracy is found to improve by applying image enhancement by super resolution prior to CRNN model. We also observe the text recognition rate further increases by 18% by applying the proposed method, which involves super resolution and character segmentation followed by CRNN with CTC loss. The efficiency of the proposed method shows that further pre-processing on chart image text and other small text images will improve the accuracy further, thereby helping text extraction from chart images.

Keywords: small text extraction, OCR, scene text recognition, CRNN

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Authors: Ahmed Elsayed Ahmed, Ashraf Hafez, A. N. Ouda, Hossam Eldin Hussein Ahmed, Hala Mohamed ABD-Elkader

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Unmanned Aircraft Systems (UAS) are playing increasingly prominent roles in defense programs and defense strategies around the world. Technology advancements have enabled the development of it to do many excellent jobs as reconnaissance, surveillance, battle fighters, and communications relays. Simulating a small unmanned aerial vehicle (SUAV) dynamics and analyzing its behavior at the preflight stage is too important and more efficient. The first step in the UAV design is the mathematical modeling of the nonlinear equations of motion. In this paper, a survey with a standard method to obtain the full non-linear equations of motion is utilized,and then the linearization of the equations according to a steady state flight condition (trimming) is derived. This modeling technique is applied to an Ultrastick-25e fixed wing UAV to obtain the valued linear longitudinal and lateral models. At the end, the model is checked by matching between the behavior of the states of the non-linear UAV and the resulted linear model with doublet at the control surfaces.

Keywords: UAV, equations of motion, modeling, linearization

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Authors: Stephen L. Green, Alexander N. Gorban, Ivan Y. Tyukin

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Within the past decade, using Convolutional Neural Networks (CNN)’s to create Deep Learning systems capable of translating Sign Language into text has been a breakthrough in breaking the communication barrier for deaf-mute people. Conventional research on this subject has been concerned with training the network to recognize the fingerspelling gestures of a given language and produce their corresponding alphanumerics. One of the problems with the current developing technology is that images are scarce, with little variations in the gestures being presented to the recognition program, often skewed towards single skin tones and hand sizes that makes a percentage of the population’s fingerspelling harder to detect. Along with this, current gesture detection programs are only trained on one finger spelling language despite there being one hundred and forty-two known variants so far. All of this presents a limitation for traditional exploitation for the state of current technologies such as CNN’s, due to their large number of required parameters. This work aims to present a technology that aims to resolve this issue by combining a pretrained legacy AI system for a generic object recognition task with a corrector method to uptrain the legacy network. This is a computationally efficient procedure that does not require large volumes of data even when covering a broad range of sign languages such as American Sign Language, British Sign Language and Chinese Sign Language (Pinyin). Implementing recent results on method concentration, namely the stochastic separation theorem, an AI system is supposed as an operate mapping an input present in the set of images u ∈ U to an output that exists in a set of predicted class labels q ∈ Q of the alphanumeric that q represents and the language it comes from. These inputs and outputs, along with the interval variables z ∈ Z represent the system’s current state which implies a mapping that assigns an element x ∈ ℝⁿ to the triple (u, z, q). As all xi are i.i.d vectors drawn from a product mean distribution, over a period of time the AI generates a large set of measurements xi called S that are grouped into two categories: the correct predictions M and the incorrect predictions Y. Once the network has made its predictions, a corrector can then be applied through centering S and Y by subtracting their means. The data is then regularized by applying the Kaiser rule to the resulting eigenmatrix and then whitened before being split into pairwise, positively correlated clusters. Each of these clusters produces a unique hyperplane and if any element x falls outside the region bounded by these lines then it is reported as an error. As a result of this methodology, a self-correcting recognition process is created that can identify fingerspelling from a variety of sign language and successfully identify the corresponding alphanumeric and what language the gesture originates from which no other neural network has been able to replicate.

Keywords: convolutional neural networks, deep learning, shallow correctors, sign language

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Authors: Shreya Srivastava, Sagnik Dey

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Aerosol direct radiative forcing (ADRF) refers to the alteration of the Earth's energy balance from the scattering and absorption of solar radiation by aerosol particles. India experiences substantial ADRF due to high aerosol loading from various sources. These aerosols' radiative impact depends on their physical characteristics (such as size, shape, and composition) and atmospheric distribution. Quantifying ADRF is crucial for understanding aerosols’ impact on the regional climate and the Earth's radiative budget. In this study, we have taken radiation data from Clouds and the Earth’s Radiant Energy System (CERES, spatial resolution=1ox1o) for 22 years (2000-2021) over the Indian subcontinent. Except for a few locations, the short-wave DARF exhibits aerosol cooling at the TOA (values ranging from +2.5 W/m2 to -22.5W/m2). Cooling due to aerosols is more pronounced in the absence of clouds. Being an aerosol hotspot, higher negative ADRF is observed over the Indo-Gangetic Plain (IGP). Aerosol Forcing Efficiency (AFE) shows a decreasing seasonal trend in winter (DJF) over the entire study region while an increasing trend over IGP and western south India during the post-monsoon season (SON) in clear-sky conditions. Analysing atmospheric heating and AOD trends, we found that only the aerosol loading is not governing the change in atmospheric heating but also the aerosol composition and/or their vertical profile. We used a Multi-angle Imaging Spectro-Radiometer (MISR) Level-2 Version 23 aerosol products to look into aerosol composition. MISR incorporates 74 aerosol mixtures in its retrieval algorithm based on size, shape, and absorbing properties. This aerosol mixture information was used for analysing long-term changes in aerosol composition and dominating aerosol species corresponding to the aerosol forcing value. Further, ADRF derived from this method is compared with around 35 studies across India, where a plane parallel Radiative transfer model was used, and the model inputs were taken from the OPAC (Optical Properties of Aerosols and Clouds) utilizing only limited aerosol parameter measurements. The result shows a large overestimation of TOA warming by the latter (i.e., Model-based method).

Keywords: aerosol radiative forcing (ARF), aerosol composition, MISR, CERES, SBDART

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Authors: Trudon Kabangu Mpinga, Ruth Mutala, Shaloom Mbambu, Yvette Kalubi Kashama, Kabeya Mukeba Yakasham

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A mathematical model is developed to describe the contact dynamics between the landing gear wheels of an aircraft and a wet pavement during landing. The model is based on nonlinear partial differential equations, using the Yakam Matrix to account for the interaction between solid, liquid, and solid phases. This framework incorporates the influence of environmental factors, particularly water or rain on the runway, on braking performance and aircraft stability. Given the absence of exact analytical solutions, our approach enhances the understanding of key physical phenomena, including Coulomb friction forces, hydrodynamic effects, and the deformation of the pavement under the aircraft's load. Additionally, the dynamics of aquaplaning are simulated numerically to estimate the braking performance limits on wet surfaces, thereby contributing to strategies aimed at minimizing risk during landing on wet runways.

Keywords: aircraft, modeling, simulation, yakam matrix, contact, wet runway

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Authors: Isaac Kofi Mensah

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Public sector organizations, ministries, departments and local government agencies are adopting e-government as a means to provide efficient and quality service delivery to citizens. The purpose of this research paper is to examine the extent to which perceived usefulness (PU) of e-government services moderates between perceived ease-of-use (PEOU) of e-government services and intention to use (IU) e-government services in Ghana. A structured research questionnaire instrument was developed and administered to 700 potential respondents in Ghana, of which 693 responded, representing 99% of the questionnaires distributed. The Technology Acceptance Model (TAM) was used as the theoretical framework for the study. The Statistical Package for Social Science (SPSS) was used to capture and analyze the data. The results indicate that even though predictors such as PU and PEOU are main determiners of citizens’ intention to adopt and use e-government services in Ghana, it failed to show that PEOU and IU e-government services in Ghana is significantly moderated by the PU of e-government services. The implication of this finding on theory and practice is further discussed.

Keywords: e-government services, intention to use, moderating role, perceived ease of use, perceived usefulness, Ghana, technology acceptance model

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Authors: Dimitra Marini, Konstantinos Bakogiannis, Spyros Polychronopoulos, Georgios Kouroupetroglou

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Archaemusicology cannot entirely depend on the study of the excavated ancient musical instruments as most of the time their condition is not ideal (i.e., missing/eroded parts) and moreover, because of the concern damaging the originals during the experiments. Researchers, in order to overcome the above obstacles, build replicas. This technique is still the most popular one, although it is rather expensive and time-consuming. Throughout the last decades, the development of physical modeling techniques has provided tools that enable the study of musical instruments through their digitally simulated models. This is not only a more cost and time-efficient technique but also provides additional flexibility as the user can easily modify parameters such as their geometrical features and materials. This paper thoroughly describes the steps to create a physical model of a woodwind ancient Greek instrument, Plagiaulos. This instrument could be considered as the ancestor of the modern flute due to the common geometry and air-jet excitation mechanism. Plagiaulos is comprised of a single resonator with an open end and a number of tone holes. The combination of closed and open tone holes produces the pitch variations. In this work, the effects of all the instrument’s components are described by means of physics and then simulated based on digital waveguides. The synthesized sound of the proposed model complies with the theory, highlighting its validity. Further, the synthesized sound of the model simulating the Plagiaulos of Koile (2nd century BCE) was compared with its replica build in our laboratory by following the scientific methodologies of archeomusicology. The aforementioned results verify that robust dynamic digital tools can be introduced in the field of computational, experimental archaemusicology.

Keywords: archaeomusicology, digital waveguides, musical acoustics, physical modeling

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Authors: Elias K. Maragos, Petros E. Maravelakis

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In Dynamic Data Envelopment Analysis (DDEA), which is a subfield of Data Envelopment Analysis (DEA), the productivity of Decision Making Units (DMUs) is considered in relation to time. In this case, as it is accepted by the most of the researchers, there are outputs, which are produced by a DMU to be used as inputs in a future time. Those outputs are known as intermediates. The common models, in DDEA, do not take into account the shape of the distribution of those inputs, outputs or intermediates data, assuming that the distribution of the virtual value of them does not deviate from linearity. This weakness causes the limitation of the accuracy of the analytical power of the traditional DDEA models. In this paper, the authors, using the concept of piecewise linear inputs and outputs, propose an extended DDEA model. The proposed model increases the flexibility of the traditional DDEA models and improves the measurement of the dynamic performance of DMUs.

Keywords: Dynamic Data Envelopment Analysis, DDEA, piecewise linear inputs, piecewise linear outputs

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Authors: Farida Kaouah, Chahida Oussalah, Wassila Hachi, Salim Boumaza, Mohamed Trari

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A catalyst of ZnO nanoparticles was used in the photocatalytic process of treatment for potential use towards bisphenol A (BPA) degradation in an aqueous solution. To achieve this study, the effect of parameters such as the catalyst dose, initial concentration of BPA and pH on the photocatalytic degradation of BPA was studied. The results reveal that the maximum degradation (more than 93%) of BPA occurred with ZnO catalyst in 120 min of stirring at natural pH (7.1) under solar light irradiation. It was found that chemical oxygen demand (COD) reduction takes place at a faster rate under solar light as compared to that of UV light. The kinetic studies were achieved and revealed that the photocatalytic degradation process obeyed a Langmuir–Hinshelwood model and followed a pseudo-first order rate expression. This work envisages the great potential that sunlight mediated photocatalysis has in the removal of bisphenol A from wastewater.

Keywords: bisphenol A, photocatalytic degradation, sunlight, zinc oxide, Langmuir–Hinshelwood model, chemical oxygen demand

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Authors: Hossein Kheirollahi, Yunhua Luo

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Studying stress and strain trends in the femur and recognizing femur failure mechanism is very important for preventing hip fracture in the elderly. The aim of this study was to identify high stress and strain regions in the femur during normal walking and falling to find the mechanical behavior and failure mechanism of the femur. We developed a finite element model of the femur from the subject’s quantitative computed tomography (QCT) image and used it to identify potentially high stress and strain regions during the single-leg stance and the sideways fall. It was found that fracture may initiate from the superior region of femoral neck and propagate to the inferior region during a high impact force such as sideways fall. The results of this study showed that the femur bone is more sensitive to strain than stress which indicates the effect of strain, in addition to effect of stress, should be considered for failure analysis.

Keywords: finite element analysis, hip fracture, strain, stress

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Authors: Aadhya Kaul, Gautam Manocha, Preeti Nagrath

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Our environment comprises of a wide variety of plants that are similar to each other and sometimes the similarity between the plants makes the identification process tedious thus increasing the workload of the botanist all over the world. Now all the botanists cannot be accessible all the time for such laborious plant identification; therefore, there is an urge for a quick classification model. Also, along with the identification of the plants, it is also necessary to classify the plant as healthy or not as for a good lifestyle, humans require good food and this food comes from healthy plants. A large number of techniques have been applied to classify the plants as healthy or diseased in order to provide the solution. This paper proposes one such method known as anomaly detection using autoencoders using a set of collections of leaves. In this method, an autoencoder model is built using Keras and then the reconstruction of the original images of the leaves is done and the threshold loss is found in order to classify the plant leaves as healthy or diseased. A dataset of plant leaves is considered to judge the reconstructed performance by convolutional autoencoders and the average accuracy obtained is 71.55% for the purpose.

Keywords: convolutional autoencoder, anomaly detection, web application, FLASK

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Authors: Prathik Ranka, Sridhar K, Vasanth Daniel, Mithun Shankar

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With growing global food needs and climate uncertainties, informed crop choices are critical for increasing agricultural productivity. Here we propose a machine learning-based crop recommendation system to help farmers in choosing the most proper crops according to their geographical regions and soil properties. We can deploy algorithms like Decision Trees, Random Forests and Support Vector Machines on a broad dataset that consists of climatic factors, soil characteristics and historical crop yields to predict the best choice of crops. The approach includes first preprocessing the data after assessing them for missing values, unlike in previous jobs where we used all the available information and then transformed because there was no way such a model could have worked with missing data, and normalizing as throughput that will be done over a network to get best results out of our machine learning division. The model effectiveness is measured through performance metrics like accuracy, precision and recall. The resultant app provides a farmer-friendly dashboard through which farmers can enter their local conditions and receive individualized crop suggestions.

Keywords: crop recommendation, precision agriculture, crop, machine learning

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Authors: Malek Bendjaballah, Hacina Saidi, Sarra Hamidoud

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The aim of this study is to model and optimize the performance of a new electrocoagulation (E.C) process for the treatment of wastewater as well as the energy consumption in order to extrapolate it to the industrial scale. Through judicious application of an experimental design (DOE), it has been possible to evaluate the individual effects and interactions that have a significant influence on both objective functions (maximizing efficiency and minimizing energy consumption) by using aluminum electrodes as sacrificial anode. Preliminary experiments have shown that the pH of the medium, the applied potential and the treatment time with E.C are the main parameters. A factorial design 33 has been adopted to model performance and energy consumption. Under optimal conditions, the pollution reduction efficiency is 93%, combined with a minimum energy consumption of 2.60.10-3 kWh / mg-COD. The potential or current applied and the processing time and their interaction were the most influential parameters in the mathematical models obtained. The results of the modeling were also correlated with the experimental ones. The results offer promising opportunities to develop a clean process and inexpensive technology to eliminate or reduce wastewater,

Keywords: electrocoagulation, green process, experimental design, optimization

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Authors: Lunliang Zhong, Bin Duan

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The undergraduate graduation project is a vital part of the higher education curriculum, crucial for engineering accreditation. Current evaluations often summarize data without identifying underlying issues. This study applies the Peter-Clark algorithm to analyze causal relationships within the graduation project data of an Electronics and Information Engineering program, creating a causal model. Structural equation modeling confirmed the model's validity. The analysis reveals key teaching stages affecting project success, uncovering problems in the process. Introducing causal discovery and inference into project evaluation helps identify issues and propose targeted improvement measures. The effectiveness of these measures is validated by comparing the learning outcomes of two student cohorts, stratified by confounding factors, leading to improved teaching quality.

Keywords: causal discovery, causal inference, continuous improvement, Peter-Clark algorithm, structural equation modeling

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Authors: Fu Jia

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The effects of soil-structure interaction (SSI) are often studied using axial-symmetric three-dimensional (3D) models to avoid the high computational cost of the more realistic, fully 3D models, which require 2-3 orders of magnitude more computer time and storage. This paper analyzes the error and presents correction factors for system frequency, system damping, and peak amplitude of structural response computed by axisymmetric models, embedded in uniform or layered half-space. The results are compared with those for fully 3D rectangular foundations of different aspect ratios. Correction factors are presented for a range of the model parameters, such as fixed-base frequency, structure mass, height and length-to-width ratio, foundation embedment, soil-layer stiffness and thickness. It is shown that the errors are larger for stiffer, taller and heavier structures, deeper foundations and deeper soil layer. For example, for a stiff structure like Millikan Library (NS response; length-to-width ratio 1), the error is 6.5% in system frequency, 49% in system damping and 180% in peak amplitude. Analysis of a case study shows that the NEHRP-2015 provisions for reduction of base shear force due to SSI effects may be unsafe for some structures and need revision. The presented correction factor diagrams can be used in practical design and other applications.

Keywords: 3D soil-structure interaction, correction factors for axisymmetric models, length-to-width ratio, NEHRP-2015 provisions for reduction of base shear force, rectangular embedded foundations, SSI system frequency, SSI system damping

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Authors: Serges Mendomo Meye, Li Guowei, Shen Zhenzhong

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Earthquake is one of the main loads threatening dam safety. Once the dam is damaged, it will bring huge losses of life and property to the country and people. Therefore, it is very important to research the seismic safety of the dam. Due to the complex foundation conditions, high fortification intensity, and high scientific and technological content, it is necessary to adopt reasonable methods to evaluate the seismic safety performance of concrete arch dams built and under construction in strong earthquake areas. Structural seismic vulnerability analysis can predict the probability of structural failure at all levels under different intensity earthquakes, which can provide a scientific basis for reasonable seismic safety evaluation and decision-making. In this paper, the response surface method (RSM) is applied to the seismic vulnerability analysis of arch dams, which improves the efficiency of vulnerability analysis. Based on the central composite test design method, the material-seismic intensity samples are established. The response surface model (RSM) with arch crown displacement as performance index is obtained by finite element (FE) calculation of the samples, and then the accuracy of the response surface model (RSM) is verified. To obtain the seismic vulnerability curves, the seismic intensity measure ??(?1) is chosen to be 0.1~1.2g, with an interval of 0.1g and a total of 12 intensity levels. For each seismic intensity level, the arch crown displacement corresponding to 100 sets of different material samples can be calculated by algebraic operation of the response surface model (RSM), which avoids 1200 times of nonlinear dynamic calculation of arch dam; thus, the efficiency of vulnerability analysis is improved greatly.

Keywords: high concrete arch dam, performance index, response surface method, seismic vulnerability analysis, vector-valued intensity measure

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Authors: Ammar Alali, Mahmoud Abughaban, William Contreras Otalvora

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Optimizing the drilling process for cost and efficiency requires the optimization of the rate of penetration (ROP). ROP is the measurement of the speed at which the wellbore is created, in units of feet per hour. It is the primary indicator of measuring drilling efficiency. Maximization of the ROP can indicate fast and cost-efficient drilling operations; however, high ROPs may induce unintended events, which may lead to nonproductive time (NPT) and higher net costs. The proposed ROP optimization solution is a hybrid, data-driven system that aims to improve the drilling process, maximize the ROP, and minimize NPT. The system consists of two phases: (1) utilizing existing geological and drilling data to train the model prior, and (2) real-time adjustments of the controllable dynamic drilling parameters [weight on bit (WOB), rotary speed (RPM), and pump flow rate (GPM)] that direct influence on the ROP. During the first phase of the system, geological and historical drilling data are aggregated. After, the top-rated wells, as a function of high instance ROP, are distinguished. Those wells are filtered based on NPT incidents, and a cross-plot is generated for the controllable dynamic drilling parameters per ROP value. Subsequently, the parameter values (WOB, GPM, RPM) are calculated as a conditioned mean based on physical distance, following Inverse Distance Weighting (IDW) interpolation methodology. The first phase is concluded by producing a model of drilling best practices from the offset wells, prioritizing the optimum ROP value. This phase is performed before the commencing of drilling. Starting with the model produced in phase one, the second phase runs an automated drill-off test, delivering live adjustments in real-time. Those adjustments are made by directing the driller to deviate two of the controllable parameters (WOB and RPM) by a small percentage (0-5%), following the Constrained Random Search (CRS) methodology. These minor incremental variations will reveal new drilling conditions, not explored before through offset wells. The data is then consolidated into a heat-map, as a function of ROP. A more optimum ROP performance is identified through the heat-map and amended in the model. The validation process involved the selection of a planned well in an onshore oil field with hundreds of offset wells. The first phase model was built by utilizing the data points from the top-performing historical wells (20 wells). The model allows drillers to enhance decision-making by leveraging existing data and blending it with live data in real-time. An empirical relationship between controllable dynamic parameters and ROP was derived using Artificial Neural Networks (ANN). The adjustments resulted in improved ROP efficiency by over 20%, translating to at least 10% saving in drilling costs. The novelty of the proposed system lays is its ability to integrate historical data, calibrate based geological formations, and run real-time global optimization through CRS. Those factors position the system to work for any newly drilled well in a developing field event.

Keywords: drilling optimization, geological formations, machine learning, rate of penetration

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Authors: Ibrahim Masud, Yusheng Kong, Adam Diyawu Rahman

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Mobile payment variously referred to as mobile money, mobile money transfer, and mobile wallet refers to payment services operated under financial regulation and performed from or via a mobile device. Mobile payment systems have come to augment and to some extent try to replace the conventional payment methods like cash, cheque, or credit cards. This study examines mobile payment adoption factors among retailers in Ghana. A conceptual framework was adopted from the extant literature using the Technology Acceptance Model and the Theory of Reasoned action as the theoretical bases. Data for the study was obtained from a sample of 240 respondents through a structured questionnaire. The PLS-SEM was used to analyze the data through SPSS v.22 and SmartPLS v.3. The findings indicate that factors such as perceived usefulness, perceived ease of use, perceived security, competitive pressure and facilitating conditions are the main determinants of mobile payment adoption among retailers in Ghana. The study contributes to the literature on mobile payment adoption from developing country context.

Keywords: mobile payment, retailers, structural equation modeling, technology acceptance model

Procedia PDF Downloads 178

Authors: Azadeh Jafari, Robert G. Owens

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In this study, a geometrical multiscale approach which means coupling together the 2-D Navier-Stokes equations, constitutive equations and 0-D lumped parameter models is investigated. A multiscale approach, suggest a natural way of coupling detailed local models (in the flow domain) with coarser models able to describe the dynamics over a large part or even the whole cardiovascular system at acceptable computational cost. In this study we introduce a new velocity correction scheme to decouple the velocity computation from the pressure one. To evaluate the capability of our new scheme, a comparison between the results obtained with Neumann outflow boundary conditions on the velocity and Dirichlet outflow boundary conditions on the pressure and those obtained using coupling with the lumped parameter model has been performed. Comprehensive studies have been done based on the sensitivity of numerical scheme to the initial conditions, elasticity and number of spectral modes. Improvement of the computational algorithm with stable convergence has been demonstrated for at least moderate Weissenberg number. We comment on mathematical properties of the reduced model, its limitations in yielding realistic and accurate numerical simulations, and its contribution to a better understanding of microvascular blood flow. We discuss the sophistication and reliability of multiscale models for computing correct boundary conditions at the outflow boundaries of a section of the cardiovascular system of interest. In this respect the geometrical multiscale approach can be regarded as a new method for solving a class of biofluids problems, whose application goes significantly beyond the one addressed in this work.

Keywords: geometrical multiscale models, haemorheology model, coupled 2-D navier-stokes 0-D lumped parameter modeling, computational fluid dynamics

Procedia PDF Downloads 361

Authors: Michael Postert

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The Studierendenwerke are legally independent public foundations with a one-century-old history in the German university community. Like the French CROUS, the Italian ANDISU or the Japanese University COOPs, they are set-up to serve the university and student needs. They are legally independent of their client institutions and student stakeholders. Initially set up as a support organization by students for students they have evolved to public business institutions with an annual turnover of EUR 100 Million or more. They are usually engaged in business areas such as student housing, restaurants, student grants, governmental scholarships and counselling services. These institutions are facing major changes over the next few years. The COVID19 pandemic and its impact on the educational system will unavoidably have an immense impact on the German student service organizations (Studierendenwerke). Issues such as digitalization and sustainability will have a huge impact on how the future business model of the Studierendenwerke will look like. The paper will discuss the aims and challenges of this development that started already before the COVID19 pandemic. In light of the way the educational system of the future will look like, the Studierendenwerke have to develop as well.

Keywords: business model, digitalization, education, student services

Procedia PDF Downloads 233

Authors: Avi Harel

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Most accidents are commonly attributed in hindsight to human errors, yet most methodologies for safety focus on technical issues. According to the Black Swan theory, this paradox is due to insufficient data about the ways systems fail. The article presents a study of the sources of errors, and proposes a methodology for utility-oriented design, comprising methods for coping with each of the sources identified. Accident analysis indicates that errors typically result from difficulties of operating in exceptional conditions. Therefore, following STAMP, the focus should be on preventing exceptions. Exception analysis indicates that typically they involve an improper account of the operational scenario, due to deficiencies in the system integration. The methodology proposes a model, which is a formal definition of the system operation, as well as principles and guidelines for safety-oriented system integration. The article calls to develop and integrate tools for recording and analysis of the system activity during the operation, required to implement validate the model.

Keywords: accidents, complexity, errors, exceptions, interaction, modeling, resilience, risks

Procedia PDF Downloads 196

Authors: Essam Gooda, Chadi Abdallah, Hamdi Seif, Safaa Baydoun, Rouya Hdeib, Hilal Obeid

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Floods are common natural disaster occurring in semi-arid regions in Lebanon. This results in damage to human life and deterioration of environment. Despite their destructive nature and their immense impact on the socio-economy of the region, flash floods have not received adequate attention from policy and decision makers. This is mainly because of poor understanding of the processes involved and measures needed to manage the problem. The current understanding of flash floods remains at the level of general concepts; most policy makers have yet to recognize that flash floods are distinctly different from normal riverine floods in term of causes, propagation, intensity, impacts, predictability, and management. Flash floods are generally not investigated as a separate class of event but are rather reported as part of the overall seasonal flood situation. As a result, Lebanon generally lacks policies, strategies, and plans relating specifically to flash floods. Main objective of this research is to improve flash flood prediction by providing new knowledge and better understanding of the hydrological processes governing flash floods in the East Catchments of El Assi River. This includes developing rainstorm time distribution curves that are unique for this type of study region; analyzing, investigating, and developing a relationship between arid watershed characteristics (including urbanization) and nearby villages flow flood frequency in Ras Baalbeck and Fekha. This paper discusses different levels of integration approach¬es between GIS and hydrological models (HEC-HMS & HEC-RAS) and presents a case study, in which all the tasks of creating model input, editing data, running the model, and displaying output results. The study area corresponds to the East Basin (Ras Baalbeck & Fakeha), comprising nearly 350 km2 and situated in the Bekaa Valley of Lebanon. The case study presented in this paper has a database which is derived from Lebanese Army topographic maps for this region. Using ArcMap to digitizing the contour lines, streams & other features from the topographic maps. The digital elevation model grid (DEM) is derived for the study area. The next steps in this research are to incorporate rainfall time series data from Arseal, Fekha and Deir El Ahmar stations to build a hydrologic data model within a GIS environment and to combine ArcGIS/ArcMap, HEC-HMS & HEC-RAS models, in order to produce a spatial-temporal model for floodplain analysis at a regional scale. In this study, HEC-HMS and SCS methods were chosen to build the hydrologic model of the watershed. The model then calibrated using flood event that occurred between 7th & 9th of May 2014 which considered exceptionally extreme because of the length of time the flows lasted (15 hours) and the fact that it covered both the watershed of Aarsal and Ras Baalbeck. The strongest reported flood in recent times lasted for only 7 hours covering only one watershed. The calibrated hydrologic model is then used to build the hydraulic model & assessing of flood hazards maps for the region. HEC-RAS Model is used in this issue & field trips were done for the catchments in order to calibrated both Hydrologic and Hydraulic models. The presented models are a kind of flexible procedures for an ungaged watershed. For some storm events it delivers good results, while for others, no parameter vectors can be found. In order to have a general methodology based on these ideas, further calibration and compromising of results on the dependence of many flood events parameters and catchment properties is required.

Keywords: flood risk management, flash flood, semi arid region, El Assi River, hazard maps

Procedia PDF Downloads 478

Authors: Khader Titi

Abstract:

Information technology developments have changed the way how businesses are working. Organizations are required to become visible online and stay connected to take advantages of costs reduction and improved operation of existing resources. The approval and the application areas of the cloud computing has significantly increased since it was presented by Google in 2007. Internet Cloud computing has attracted the IT enterprise attention especially the e-business enterprise. At this time, there is a great issue of environmental costs during the enterprises apply the e- business, but with the coming of cloud computing, most of the problem will be solved. Organizations around the world are facing with the continued budget challenges and increasing in the size of their computational data so, they need to find a way to deliver their services to clients as economically as possible without negotiating the achievement of anticipated outcomes. E- business companies need to provide better services to satisfy their clients. In this research, the researcher proposed a paradigm that use and deploy cloud computing technology environment to be used for e-business in small enterprises. Cloud computing might be a suitable model for implementing e-business and e-commerce architecture to improve efficiency and user satisfaction.

Keywords: E-commerce, cloud computing, B2C, SaaS

Procedia PDF Downloads 317