Search results for: cointegration and error correction

Authors: Fabienne Fel, Eric Griette

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Reshoring, whether concerning back-reshoring or near-reshoring, is a quite recent phenomenon. Despite the economic and political interest of this topic, academic research questioning determinants of reshoring remains rare. Our paper aims at contributing to fill this gap. In order to better understand the reasons for reshoring, we conducted a study among 280 French firms during spring 2016, three-quarters of which sourced, or source, in China. 105 firms in the sample have reshored all or part of their Chinese production or supply in recent years, and we aimed to establish a typology of the motives that drove them to this decision. We asked our respondents about the history of their Chinese supplies, their current reshoring strategies, and their motivations. Statistical analysis was performed with SPSS 22 and SPAD 8. Our results show that change in commercial and financial terms with China is the first motive explaining the current reshoring movement from this country (it applies to 54% of our respondents). A change in corporate strategy is the second motive (30% of our respondents); the reshoring decision follows a change in companies’ strategies (upgrading, implementation of a CSR policy, or a 'lean management' strategy). The third motive (14% of our sample) is a mere correction of the initial offshoring decision, considered as a mistake (under-estimation of hidden costs, non-quality and non-responsiveness problems). Some authors emphasize that developing a short supply chain, involving geographic proximity between design and production, gives a competitive advantage to companies wishing to offer innovative products. Admittedly 40% of our respondents indicate that this motive could have played a part in their decision to reshore, but this reason was not enough for any of them and is not an intrinsic motive leading to leaving Chinese suppliers. Having questioned our respondents about the importance given to various problems leading them to reshore, we then performed a Principal Components Analysis (PCA), associated with an Ascending Hierarchical Classification (AHC), based on Ward criterion, so as to point out more specific motivations. Three main classes of companies should be distinguished: -The 'Cost Killers' (23% of the sample), which reshore their supplies from China only because of higher procurement costs and so as to find lower costs elsewhere. -The 'Realists' (50% of the sample), giving equal weight or importance to increasing procurement costs in China and to the quality of their supplies (to a large extend). Companies being part of this class tend to take advantage of this changing environment to change their procurement strategy, seeking suppliers offering better quality and responsiveness. - The 'Voluntarists' (26% of the sample), which choose to reshore their Chinese supplies regardless of higher Chinese costs, to obtain better quality and greater responsiveness. We emphasize that if the main driver for reshoring from China is indeed higher local costs, it is should not be regarded as an exclusive motivation; 77% of the companies in the sample, are also seeking, sometimes exclusively, more reactive suppliers, liable to quality, respect for the environment and intellectual property.

Keywords: China, procurement, reshoring, strategy, supplies

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Authors: M. Govindarajan

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One of the major developments in machine learning in the past decade is the ensemble method, which finds highly accurate classifier by combining many moderately accurate component classifiers. In this research work, new ensemble classification methods are proposed with homogeneous ensemble classifier using bagging and heterogeneous ensemble classifier using arcing and their performances are analyzed in terms of accuracy. A Classifier ensemble is designed using Radial Basis Function (RBF) and Support Vector Machine (SVM) as base classifiers. The feasibility and the benefits of the proposed approaches are demonstrated by the means of standard datasets of intrusion detection. The main originality of the proposed approach is based on three main parts: preprocessing phase, classification phase, and combining phase. A wide range of comparative experiments is conducted for standard datasets of intrusion detection. The performance of the proposed homogeneous and heterogeneous ensemble classifiers are compared to the performance of other standard homogeneous and heterogeneous ensemble methods. The standard homogeneous ensemble methods include Error correcting output codes, Dagging and heterogeneous ensemble methods include majority voting, stacking. The proposed ensemble methods provide significant improvement of accuracy compared to individual classifiers and the proposed bagged RBF and SVM performs significantly better than ECOC and Dagging and the proposed hybrid RBF-SVM performs significantly better than voting and stacking. Also heterogeneous models exhibit better results than homogeneous models for standard datasets of intrusion detection. 

Keywords: data mining, ensemble, radial basis function, support vector machine, accuracy

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Authors: Shveta Puri, Shiv Jyoti Pandey, G. M. Bhat, Neha Raina

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TheJammu and Kashmir region of the Northwest Himalaya had witnessed many devastating earthquakes in the recent past and has remained unexplored for any kind of seismic investigations except scanty records of the earthquakes that occurred in this region in the past. In this study, we have used local seismic data of year 2013 that was recorded by the network of Broadband Seismographs in J&K. During this period, our seismic stations recorded about 207 earthquakes including two moderate events of Mw 5.7 on 1st May, 2013 and Mw 5.1 of 2nd August, 2013.We analyzed the events of Mw 3-4.6 and the main events only (for minimizing the error) for source parameters, b value and sense of movement through waveform modeling for understanding seismotectonic and seismic hazard of the region. It has been observed that most of the events are bounded between 32.9° N – 33.3° N latitude and 75.4° E – 76.1° E longitudes, Moment Magnitude (Mw) ranges from Mw 3 to 5.7, Source radius (r), from 0.21 to 3.5 km, stress drop, from 1.90 bars to 71.1 bars and Corner frequency, from 0.39 – 6.06 Hz. The b-value for this region was found to be 0.83±0 from these events which are lower than the normal value (b=1), indicating the area is under high stress. The travel time inversion and waveform inversion method suggest focal depth up to 10 km probably above the detachment depth of the Himalayan region. Moment tensor solution of the (Mw 5.1, 02:32:47 UTC) main event of 2ndAugust suggested that the source fault is striking at 295° with dip of 33° and rake value of 85°. It was found that these events form intense clustering of small to moderate events within a narrow zone between Panjal Thrust and Kishtwar Window. Moment tensor solution of the main events and their aftershocks indicating thrust type of movement is occurring in this region.

Keywords: b-value, moment tensor, seismotectonics, source parameters

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Authors: Bliss Singhal

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Machine learning (ML) is a branch of Artificial Intelligence (AI) where computers analyze data and find patterns in the data. The study focuses on the detection of metastatic cancer using ML. Metastatic cancer is the stage where cancer has spread to other parts of the body and is the cause of approximately 90% of cancer-related deaths. Normally, pathologists spend hours each day to manually classifying whether tumors are benign or malignant. This tedious task contributes to mislabeling metastasis being over 60% of the time and emphasizes the importance of being aware of human error and other inefficiencies. ML is a good candidate to improve the correct identification of metastatic cancer, saving thousands of lives and can also improve the speed and efficiency of the process, thereby taking fewer resources and time. So far, the deep learning methodology of AI has been used in research to detect cancer. This study is a novel approach to determining the potential of using preprocessing algorithms combined with classification algorithms in detecting metastatic cancer. The study used two preprocessing algorithms: principal component analysis (PCA) and the genetic algorithm, to reduce the dimensionality of the dataset and then used three classification algorithms: logistic regression, decision tree classifier, and k-nearest neighbors to detect metastatic cancer in the pathology scans. The highest accuracy of 71.14% was produced by the ML pipeline comprising of PCA, the genetic algorithm, and the k-nearest neighbor algorithm, suggesting that preprocessing and classification algorithms have great potential for detecting metastatic cancer.

Keywords: breast cancer, principal component analysis, genetic algorithm, k-nearest neighbors, decision tree classifier, logistic regression

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Authors: Krupali Mukeshkumar, Jinesh Shah

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Background: Bilateral Sagittal Split Osteotomy (BSSO), used for the correction of mandibular deformities, is a common oral and maxillofacial surgical procedure. Inferior alveolar nerve dysfunction is commonly reported post-operatively by patients as paresthesia or anesthesia. The current literature lacks a consensus on the incidence of inferior alveolar nerve dysfunction as patients are not routinely assessed pre and post-operatively with an objective assessment. The range of incidence varies from 9% to 85% of patients, with some authors arguing that 100% of patients experience nerve dysfunction immediately post-surgery. Systematic reviews have shown a difference between incidence rates at different follow-up periods using objective and subjective methods. Aim: To identify the incidence of inferior alveolar nerve dysfunction following BSSO. Gold standard: Nerve dysfunction incidence rates similar or lower than current literature of 83% day one post-operatively and 18.4% at one year follow up. Setting: A retrospective cross-sectional audit of patients treated between 2017-2019 at the Royal Stoke University Hospital, Maxillofacial and Orthodontic departments. Sample: All patients who underwent a BSSO (with or without le fort one osteotomy) between 2017–2019 were identified from the database. Patients with pre-existing neurosensory disturbance, those who had a genioplasty at the same time and those with no follow-up were excluded. The sample consisted of 121 patients, 37 males and 84 females between the ages of 17-50 years at the time of surgery. Methods: Clinical records of 121 cases were reviewed to assess the age, sex, type of mandibular osteotomy, status of the nerve during the surgical procedure, type of bony split and incidence of nerve dysfunction at follow-up appointments. The surgical procedure was carried out by three Maxillo-facial surgeons and follow-up appointments were carried out in the Orthodontic and Oral and Maxillo-facial departments. Results: 120 patients were treated to correct the mandibular facial deformity and 1 patient was treated for sleep apnoea. Seventeen patients had a mandibular setback and 104 patients had mandibular advancement. 68 patients reported inferior alveolar nerve dysfunction at one week following their surgery. Seventy-six patients had temporary paresthesia present between 2 weeks and 12 months post-surgery. 13 patients had persistent nerve dysfunction at 12 months, of which 1 had a bad bony split during the BSSO. The incidence of nerve dysfunction postoperatively was 6.6% after 1 day, 56.1% at 1 week, 62.8% at 2 weeks, 59.5% between 3-6 weeks, 43.0% between 8-16 weeks and 10.7% at 1 year. Conclusions: The results of this audit show a similar incidence rate to the research gold standard at the one-year follow-up. Future Recommendations: No changes to surgical procedure or technique are indicated, but a need for improved documentation and a standardized approach for assessment of post-operative nerve dysfunction would be beneficial.

Keywords: bilateral sagittal split osteotomy, inferior alveolar nerve, mandible, nerve dysfunction

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Authors: Aid Abdelkrim

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A method of fatigue damage accumulation based upon application of energy parameters of the fatigue process is proposed in the paper. Using this model is simple, it has no parameter to be determined, it requires only the knowledge of the curve W–N (W: strain energy density N: number of cycles at failure) determined from the experimental Wöhler curve. To examine the performance of nonlinear models proposed in the estimation of fatigue damage and fatigue life of components under random loading, a batch of specimens made of 6082 T 6 aluminium alloy has been studied and some of the results are reported in the present paper. The paper describes an algorithm and suggests a fatigue cumulative damage model, especially when random loading is considered. This work contains the results of uni-axial random load fatigue tests with different mean and amplitude values performed on 6082T6 aluminium alloy specimens. The proposed model has been formulated to take into account the damage evolution at different load levels and it allows the effect of the loading sequence to be included by means of a recurrence formula derived for multilevel loading, considering complex load sequences. It is concluded that a ‘damaged stress interaction damage rule’ proposed here allows a better fatigue damage prediction than the widely used Palmgren–Miner rule, and a formula derived in random fatigue could be used to predict the fatigue damage and fatigue lifetime very easily. The results obtained by the model are compared with the experimental results and those calculated by the most fatigue damage model used in fatigue (Miner’s model). The comparison shows that the proposed model, presents a good estimation of the experimental results. Moreover, the error is minimized in comparison to the Miner’s model.

Keywords: damage accumulation, energy model, damage indicator, variable loading, random loading

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Authors: Gabriel Efomeh Omolaiye, Olatunji Seminu, Jimoh Ajadi, Yusuf Ayoola Jimoh

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The Nigerian part of the Chad Basin till date has been one of the few critically studied basins, with few published scholarly works, compared to other basins such as Niger Delta, Dahomey, etc. This work was undertaken by the integration of 3D seismic interpretations and the well data analysis of eight wells fairly distributed in block A, Baga/Lake sub-basin in Borno basin with the aim of determining the thickness of Chad, Kerri-Kerri, Fika, and Gongila Formations in the sub-basin. Da-1 well (type-well) used in this study was subdivided into stratigraphic units based on the regional stratigraphic subdivision of the Chad basin and was later correlated with other wells using similarity of observed log responses. The combined density and sonic logs were used to generate synthetic seismograms for seismic to well ties. Five horizons were mapped, representing the tops of the formations on the 3D seismic data covering the block; average velocity function with maximum error/residual of 0.48% was adopted in the time to depth conversion of all the generated maps. There is a general thickening of sediments from the west to the east, and the estimated thicknesses of the various formations in the Baga/Lake sub-basin are Chad Formation (400-750 m), Kerri-Kerri Formation (300-1200 m), Fika Formation (300-2200 m) and Gongila Formation (100-1300 m). The thickness of the Bima Formation could not be established because the deepest well (Da-1) terminates within the formation. This is a modification to the previous and widely referenced studies of over forty decades that based the estimation of formation thickness within the study area on the observed outcrops at different locations and the use of few well data.

Keywords: Baga/Lake sub-basin, Chad basin, formation thickness, seismic, velocity

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Authors: Lovesh Goyal, Pushkar Dave, Prajyot Jadhav, GonnaYaswanth, Sakshi Giri, Sahil Dharme, Rushika Joshi, Rishabh Verma, Shital Chiddarwar

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With the rising need for human-robot interaction, researchers have proposed and tested multiple models with varying degrees of success. A few of these models performed on aerial platforms are commonly known as Tethered Aerial Systems. These aerial vehicles may be powered continuously by a tether cable, which addresses the predicament of the short battery life of quadcopters. This system finds applications to minimize humanitarian efforts for industrial, medical, agricultural, and service uses. However, a significant challenge in employing such systems is that it necessities attaining smooth and secure robot-human interaction while ensuring that the forces from the tether remain within the standard comfortable range for the humans. To tackle this problem, a hybrid control method that could switch between two control techniques: constant control input and the steady-state solution, is implemented. The constant control approach is implemented when a person is far from the target location, and error is thought to be eventually constant. The controller switches to the steady-state approach when the person reaches within a specific range of the goal position. Both strategies take into account human velocity feedback. This hybrid technique enhances the outcomes by assisting the person to reach the desired location while decreasing the human's unwanted disturbance throughout the process, thereby keeping the interaction between the robot and the subject smooth.

Keywords: unmanned aerial vehicle, tethered system, physical human-robot interaction, hybrid control

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Authors: Gunasekaran Raja, Ramkumar Jayaraman

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In this paper, we consider CCL-N (Cooperative Cross Layer Network) topology based on the cross layer (both centralized and distributed) environment to form network communities. Various performance metrics related to the IEEE 802.16 networks are discussed to design CCL-N Topology. In CCL-N topology, nodes are classified as master nodes (Master Base Station [MBS]) and serving nodes (Relay Station [RS]). Nodes communities are organized based on the networking terminologies. Based on CCL-N Topology, various simulation analyses for both transparent and non-transparent relays are tabulated and throughput efficiency is calculated. Weighted load balancing problem plays a challenging role in IEEE 802.16 network. CoTS (Concurrent Transmission Scheduling) Scheme is formulated in terms of three aspects – transmission mechanism based on identical communities, different communities and identical node communities. CoTS scheme helps in identifying the weighted load balancing problem. Based on the analytical results, modularity value is inversely proportional to that of the error value. The modularity value plays a key role in solving the CoTS problem based on hop count. The transmission mechanism for identical node community has no impact since modularity value is same for all the network groups. In this paper three aspects of communities based on the modularity value which helps in solving the problem of weighted load balancing and CoTS are discussed.

Keywords: cross layer network topology, concurrent scheduling, modularity value, network communities and weighted load balancing

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Authors: Burak Oksuzoglu, Savas Yildirim, Ferhat Kutlu

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Opinion mining (OM) is one of the natural language processing (NLP) problems to determine the polarity of opinions, mostly represented on a positive-neutral-negative axis. The data for OM is usually collected from various social media platforms. In an era where social media has considerable control over companies’ futures, it’s worth understanding social media and taking actions accordingly. OM comes to the fore here as the scale of the discussion about companies increases, and it becomes unfeasible to gauge opinion on individual levels. Thus, the companies opt to automize this process by applying machine learning (ML) approaches to their data. For the last two decades, OM or sentiment analysis (SA) has been mainly performed by applying ML classification algorithms such as support vector machines (SVM) and Naïve Bayes to a bag of n-gram representations of textual data. With the advent of deep learning and its apparent success in NLP, traditional methods have become obsolete. Transfer learning paradigm that has been commonly used in computer vision (CV) problems started to shape NLP approaches and language models (LM) lately. This gave a sudden rise to the usage of the pretrained language model (PTM), which contains language representations that are obtained by training it on the large datasets using self-supervised learning objectives. The PTMs are further fine-tuned by a specialized downstream task dataset to produce efficient models for various NLP tasks such as OM, NER (Named-Entity Recognition), Question Answering (QA), and so forth. In this study, the traditional and modern NLP approaches have been evaluated for OM by using a sizable corpus belonging to a large private company containing about 76,000 comments in Turkish: SVM with a bag of n-grams, and two chosen pre-trained models, multilingual universal sentence encoder (MUSE) and bidirectional encoder representations from transformers (BERT). The MUSE model is a multilingual model that supports 16 languages, including Turkish, and it is based on convolutional neural networks. The BERT is a monolingual model in our case and transformers-based neural networks. It uses a masked language model and next sentence prediction tasks that allow the bidirectional training of the transformers. During the training phase of the architecture, pre-processing operations such as morphological parsing, stemming, and spelling correction was not used since the experiments showed that their contribution to the model performance was found insignificant even though Turkish is a highly agglutinative and inflective language. The results show that usage of deep learning methods with pre-trained models and fine-tuning achieve about 11% improvement over SVM for OM. The BERT model achieved around 94% prediction accuracy while the MUSE model achieved around 88% and SVM did around 83%. The MUSE multilingual model shows better results than SVM, but it still performs worse than the monolingual BERT model.

Keywords: BERT, MUSE, opinion mining, pretrained language model, SVM, Turkish

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Authors: Alexandros Pournaras, Anastasia Papadopoulou, Serafim Kontos, Anastasios Karakostas

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The air quality of modern cities is an important concern, as poor air quality contributes to human health and environmental issues. Reliable air quality forecasting has, thus, gained scientific and governmental attention as an essential tool that enables authorities to take proactive measures for public safety. In this study, the potential of Machine Learning (ML) models to forecast PM2.5 at local scale is investigated in the city of Thessaloniki, the second largest city in Greece, which has been struggling with the persistent issue of air pollution. ML models, with proven ability to address timeseries forecasting, are employed to predict the PM2.5 concentrations and the respective Air Quality Index 5-days ahead by learning from daily historical air quality and meteorological data from 2014 to 2016 and gathered from two stations with different land use characteristics in the urban fabric of Thessaloniki. The performance of the ML models on PM2.5 concentrations is evaluated with common statistical methods, such as R squared (r²) and Root Mean Squared Error (RMSE), utilizing a portion of the stations’ measurements as test set. A multi-categorical evaluation is utilized for the assessment of their performance on respective AQIs. Several conclusions were made from the experiments conducted. Experimenting on MLs’ configuration revealed a moderate effect of various parameters and training schemas on the model’s predictions. Their performance of all these models were found to produce satisfactory results on PM2.5 concentrations. In addition, their application on untrained stations showed that these models can perform well, indicating a generalized behavior. Moreover, their performance on AQI was even better, showing that the MLs can be used as predictors for AQI, which is the direct information provided to the general public.

Keywords: Air Quality, AQ Forecasting, AQI, Machine Learning, PM2.5

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Authors: Muhammad Idnan, Arshad Javid, Muhammad Nadeem

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The present study was conducted from April 2013 to March 2014 to observe the behavioral parameters of Chinkara (Gazella bennettii) under captive conditions by comparing the captive-born and wild-caught animals for conservation strategies. Understanding the behavioral conformations plays a significant role in captive management. Due to human population explosion and mechanized hunting, the captive breeding seems to be the best way for sports hunting, bush meat, for leather industry and horns for traditional medicinal usage. Primarily, captive management has been used on trial and error basis due to deficiency of ethology of this least concerned species. Behavior of [(20 wild-caught (WC) and 10 captive-bred (CB)] adult Chinkara was observed at captive breeding facilities for ungulates at Ravi Campus, University of Veterinary and Animal Sciences at Kasur district which is situated on southeast side of Lahore. The average annual rainfall is about 650 mm, with frequent raining during monsoon. A focal sample was used to observe the various behavioral patterns for CB and WC chinkara. A similarity was observed in behavioral parameters in WC and CB animals, however, when the differences were considered, WC male deer showed a significantly higher degree of agonistic interaction as compared to the CB male chinkara. These findings suggest that there is no immediate impact of captivity on behavior of chinkara nevertheless 10 generations of captivity. It is suggested that the Chinkara is not suitable for domestication and for successful deer farming, a further study is recommended for ethology of chinkara.

Keywords: Chinkara (Gazella bennettii), domestication, deer farming, ex-situ conservation

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Authors: Sudhir Kumar, V. R. Sinha

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Water in oil topical microemulsions of 5-FU were developed and optimized using face centered central composite design. Topical w/o microemulsion of 5-FU were prepared using sorbitan monooleate (Span 80), polysorbate 80 (Tween 80), with different oils such as oleic acid (OA), triacetin (TA), and isopropyl myristate (IPM). The ternary phase diagrams designated the microemulsion region and face centered central composite design helped in determining the effects of selected variables viz. type of oil, smix ratio and water concentration on responses like drug content, globule size and viscosity of microemulsions. The CCD design exhibited that the factors have statistically significant effects (p<0.01) on the selected responses. The actual responses showed excellent agreement with the predicted values as suggested by the CCD with lower residual standard error. Similarly, the optimized values were found within the range as predicted by the model. Furthermore, other characteristics of microemulsions like pH, conductivity were investigated. For the optimized microemulsion batch, ex-vivo skin flux, skin irritation and retention studies were performed and compared with marketed 5-FU formulation. In ex vivo skin permeation studies, higher skin retention of drug and minimal flux was achieved for optimized microemulsion batch then the marketed cream. Results confirmed the actual responses to be in agreement with predicted ones with least residual standard errors. Controlled release of drug was achieved for the optimized batch with higher skin retention of 5-FU, which can further be utilized for the treatment of many dermatological disorders.

Keywords: 5-FU, central composite design, microemulsion, ternanry phase diagram

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Authors: M. Karimpour, L. Hitihamillage, N. Elkhoury, S. Moridpour, R. Hesami

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Rail transport authorities around the world have been facing a significant challenge when predicting rail infrastructure maintenance work for a long period of time. Generally, maintenance monitoring and prediction is conducted manually. With the restrictions in economy, the rail transport authorities are in pursuit of improved modern methods, which can provide precise prediction of rail maintenance time and location. The expectation from such a method is to develop models to minimize the human error that is strongly related to manual prediction. Such models will help them in understanding how the track degradation occurs overtime under the change in different conditions (e.g. rail load, rail type, rail profile). They need a well-structured technique to identify the precise time that rail tracks fail in order to minimize the maintenance cost/time and secure the vehicles. The rail track characteristics that have been collected over the years will be used in developing rail track degradation prediction models. Since these data have been collected in large volumes and the data collection is done both electronically and manually, it is possible to have some errors. Sometimes these errors make it impossible to use them in prediction model development. This is one of the major drawbacks in rail track degradation prediction. An accurate model can play a key role in the estimation of the long-term behavior of rail tracks. Accurate models increase the track safety and decrease the cost of maintenance in long term. In this research, a short review of rail track degradation prediction models has been discussed before estimating rail track degradation for the curve sections of Melbourne tram track system using Adaptive Network-based Fuzzy Inference System (ANFIS) model.

Keywords: ANFIS, MGT, prediction modeling, rail track degradation

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Authors: Baha-Aldeen S. Algmati, Ahmed R. Ballil

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Multiphase flows occur widely in many engineering and industrial processes as well as in the environment we live in. In particular, bubbly flows are considered to be crucial phenomena in fluid flow applications and can be studied and analyzed experimentally, analytically, and computationally. In the present paper, the dynamic motion of an air bubble rising within a column of liquid is numerically simulated using an open-source CFD modeling tool 'OpenFOAM'. An interface tracking numerical algorithm called MULES algorithm, which is built-in OpenFOAM, is chosen to solve an appropriate mathematical model based on the volume of fluid (VOF) numerical method. The bubbles initially have a spherical shape and starting from rest in the stagnant column of liquid. The algorithm is initially verified against numerical results and is also validated against available experimental data. The comparison revealed that this algorithm provides results that are in a very good agreement with the 2D numerical data of other CFD codes. Also, the results of the bubble shape and terminal velocity obtained from the 3D numerical simulation showed a very good qualitative and quantitative agreement with the experimental data. The simulated rising bubbles yield a very small percentage of error in the bubble terminal velocity compared with the experimental data. The obtained results prove the capability of OpenFOAM as a powerful tool to predict the behavior of rising characteristics of the spherical bubbles in the stagnant column of liquid. This will pave the way for a deeper understanding of the phenomenon of the rise of bubbles in liquids.

Keywords: CFD simulations, multiphase flows, OpenFOAM, rise of bubble, volume of fluid method, VOF

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Authors: Yong Eun Yoon, Eric N. Johnson, Liling Ren

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Multirotor is one of the most popular types of small unmanned aircraft systems and has already been used in many areas including transport, military, surveillance, and leisure. Together with its popularity, the needs for proper flight control is growing because in most applications it is required to conduct its missions autonomously, which is in many aspects based on autonomous flight control. There have been many studies about the flight control for multirotor, but there is still room for enhancements in terms of performance and efficiency. This paper presents an autonomous flight control method for multirotor based on alternative input output linearization coupled with nested saturations. With alternative choice of the output of the multirotor flight control system, we can reduce computational cost regarding Lie algebra, and the linearized system can be stabilized with the introduction of nested saturations with real poles of our own design. Stabilization of internal dynamics is also based on the nested saturations and accompanies the determination of part of desired states. In particular, outer control loops involving state variables which originally are not included in the output of the flight control system is naturally rendered through this internal dynamics stabilization. We can also observe that desired tilting angles are determined by error dynamics from outer loops. Simulation results show that in any tracking situations multirotor stabilizes itself with small time constants, preceded by tuning process for control parameters with relatively low degree of complexity. Future study includes control of piecewise linear behavior of multirotor with actuator saturations, and the optimal determination of desired states while tracking multiple waypoints.

Keywords: automatic flight control, input output linearization, multirotor, nested saturations

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Authors: Naghmeh Zamani, Ashkan Pourkand, David Grow

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This paper presents the design and mechanical model of a hybrid impedance/admittance haptic device optimized for applications, like bone drilling, spinal awl probe use, and other surgical techniques were high force is required in the tool-axial direction, and low impedance is needed in all other directions. The performance levels required cannot be satisfied by existing, off-the-shelf haptic devices. This design may allow critical improvements in simulator fidelity for surgery training. The device consists primarily of two low-mass (carbon fiber) plates with a rod passing through them. Collectively, the device provides 6 DOF. The rod slides through a bushing in the top plate and it is connected to the bottom plate with a universal joint, constrained to move in only 2 DOF, allowing axial torque display the user’s hand. The two parallel plates are actuated and located by means of four cables pulled by motors. The forward kinematic equations are derived to ensure that the plates orientation remains constant. The corresponding equations are solved using the Newton-Raphson method. The static force/torque equations are also presented. Finally, we present the predicted distribution of location error, cables velocity, cable tension, force and torque for the device. These results and preliminary hardware fabrication indicate that this design may provide a revolutionary approach for haptic display of many surgical procedures by means of an architecture that allows arbitrary workspace scaling. Scaling of the height and width can be scaled arbitrarily.

Keywords: cable direct driven robot, haptics, parallel plates, bone drilling

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Authors: Larifah Mohd Sidik, Anuar Kasa

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In recent years, the demands for raw water are increasing along with the growth of the economy and population. Hence, the need for the construction and operation of dams is one of the solutions for the management of water resources problems. The stability of the embankment should be taken into consideration to evaluate the safety of retaining water. The safety of the dam is mostly based on numerous measurable components, for instance, seepage flowrate, pore water pressure and deformation of the embankment. Seepage and slope stability is the primary and most important reason to ascertain the overall safety behavior of the dams. This research study was conducted to evaluate static condition seepage and slope stability performances of Batu dam which is located in Kuala Lumpur capital city. The numerical solution Geostudio-2012 software was employed to analyse the seepage using finite element method, SEEP/W and slope stability using limit equilibrium method, SLOPE/W for three different cases of reservoir level operations; normal and flooded condition. Results of seepage analysis using SEEP/W were utilized as parental input for the analysis of SLOPE/W. Sensitivity analysis on hydraulic conductivity of material was done and calibrated to minimize the relative error of simulation SEEP/W, where the comparison observed field data and predicted value were also carried out. In seepage analysis, such as leakage flow rate, pore water distribution and location of a phreatic line are determined using the SEEP/W. The result of seepage analysis shows the clay core effectively lowered the phreatic surface and no piping failure is shown in the result. Hence, the total seepage flux was acceptable and within the permissible limit.

Keywords: earth dam, dam safety, seepage, slope stability, pore water pressure

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Authors: D. Pogliana, A. Maso, N. Milani, D. Panzin, S. Rivaroli, J. Konin

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This case aims to identify the role of movement quality during the final stage of return to sport (RTS) in a male amateur football player 13 years old after passing the acute phase of the bilateral Osgood-Schlatter disease (OSD). The patient, after a year from passing the acute phase of OSD with the abstention of physical activity, reports bilateral anterior knee pain at the beginning of the football sport activity. Interventions: After the orthopedist check, who recommended physiotherapy sessions for the correction of motor patterns and the isometric reinforcement of the muscles of the quadriceps, the rehabilitation intervention was developed in 7 weeks through 14 sessions of neuro-motor training (NMT) with a frequency of two weekly sessions and six sessions of muscle-strengthening with a frequency of one weekly session. The sessions of NMT were carried out through free body exercises (or with overloads) with visual bio-feedback with the help of two cameras (one with anterior vision and one with lateral vision of the subject) and a big touch screen. The aim of these sessions of NMT was to modify the dysfunctional motor patterns evaluated by the 2D motion analysis test. The test was carried out at the beginning and at the end of the rehabilitation course and included five movements: single-leg squat (SLS), drop jump (DJ), single-leg hop (SLH), lateral shuffle (LS), and change of direction (COD). Each of these movements was evaluated through the video analysis of dynamic valgus knee, pelvic tilt, trunk control, shock absorption, and motor strategy. A free image analysis software (Kinovea) was then used to calculate scores. Results: Baseline assessment of the subject showed a total score of 59% on the right limb and 64% on the left limb (considering an optimal score above 85%) with large deficits in shock absorption capabilities, the presence of dynamic valgus knee, and dysfunctional motor strategies defined “quadriceps dominant.” After six weeks of training, the subject achieved a total score of 80% on the right limb and 86% on the left limb, with significant improvements in shock absorption capabilities, the presence of dynamic knee valgus, and the employment of more hip-oriented motor strategies on both lower limbs. The improvements shown in dynamic knee valgus, greater hip-oriented motor strategies, and improved shock absorption identified through six weeks of the NMT program can help a teenager amateur football player to manage the anterior knee pain during sports activity. In conclusion, NMT was a good choice to help a 13 years old male amateur football player to return to performance without pain after OSD and can also be used with all this type of athletes of the other teams' sports.

Keywords: movement analysis, neuro-motor training, knee pain, movement strategies

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Authors: Karla D. Vazquez-Valdez, Bradley P. Wynne

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In the last decades manufacturing needs have been changing, leading to the study of manufacturing methods that were underdeveloped, such as incremental forming processes like shear forming. These processes use rotating tools in constant local contact with the workpiece, which is often also rotating, to generate shape. This means much lower loads to forge large parts and no need for expensive special tooling. Potential has already been established by demonstrating manufacture of high-value products, e.g., turbine and satellite parts, with high dimensional accuracy from difficult to manufacture materials. Thus, huge opportunities exist for these processes to replace the current method of manufacture for a range of high value components, e.g., eliminating lengthy machining, reducing material waste and process times; or the manufacture of a complicated shape without the development of expensive tooling. However, little is known about the exact deformation conditions during processing and why certain materials are better than others for shear forming, leading to a lot of trial and error before production. Three alloys were used for this study: Ti-54M, Jethete M154, and IN718. General Microscopy and Electron Backscatter Diffraction (EBSD) were used to measure strains and orientation maps during shear forming. A Design of Experiments (DOE) analysis was also made in order to understand the impact of process parameters in the properties of the final workpieces. Such information was the key to develop a reliable Finite Element Method (FEM) model that closely resembles the deformation paths of this process. Finally, the potential of these three materials to be shear spun was studied using the FEM model and their Forming Limit Diagram (FLD) which led to the development of a rough methodology for testing the shear spinnability of various metals.

Keywords: shear forming, damage, principal strains, forming limit diagram

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Authors: Qian Chen, Lin Xu, Ping Ju, Zhuoran Li, Yiping Yu, Yuqing Jin

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The operation of power grid is becoming more and more complex and difficult due to its rapid development towards high voltage, long distance, and large capacity. For instance, many large-scale wind farms have connected to power grid, where their fluctuation and randomness is very likely to affect the stability and safety of the grid. Fortunately, many new-type equipments based on power electronics have been applied to power grid, such as UPFC (Unified Power Flow Controller), TCSC (Thyristor Controlled Series Compensation), STATCOM (Static Synchronous Compensator) and so on, which can help to deal with the problem above. Compared with traditional equipment such as generator, new-type controllable devices, represented by the FACTS (Flexible AC Transmission System), have more accurate control ability and respond faster. But they are too expensive to use widely. Therefore, on the basis of the comparison and analysis of the controlling characteristics between traditional control equipment and new-type controllable equipment in both time and space scale, a coordinated optimizing control method within mutil-time-space frame is proposed in this paper to bring both kinds of advantages into play, which can better both control ability and economical efficiency. Firstly, the coordination of different space sizes of grid is studied focused on the fluctuation caused by large-scale wind farms connected to power grid. With generator, FSC (Fixed Series Compensation) and TCSC, the coordination method on two-layer regional power grid vs. its sub grid is studied in detail. The coordination control model is built, the corresponding scheme is promoted, and the conclusion is verified by simulation. By analysis, interface power flow can be controlled by generator and the specific line power flow between two-layer regions can be adjusted by FSC and TCSC. The smaller the interface power flow adjusted by generator, the bigger the control margin of TCSC, instead, the total consumption of generator is much higher. Secondly, the coordination of different time sizes is studied to further the amount of the total consumption of generator and the control margin of TCSC, where the minimum control cost can be acquired. The coordination method on two-layer ultra short-term correction vs. AGC (Automatic Generation Control) is studied with generator, FSC and TCSC. The optimal control model is founded, genetic algorithm is selected to solve the problem, and the conclusion is verified by simulation. Finally, the aforementioned method within multi-time-space scale is analyzed with practical cases, and simulated on PSASP (Power System Analysis Software Package) platform. The correctness and effectiveness are verified by the simulation result. Moreover, this coordinated optimizing control method can contribute to the decrease of control cost and will provide reference to the following studies in this field.

Keywords: FACTS, multi-space-time frame, optimal control, TCSC

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Authors: Soukaina Ounss, Hamid Mounir, Abdellatif El Marjani

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Sandwich materials with composite reinforced skins are mostly required in advanced construction applications with a view to ensure resistant structures. Their lightweight, their high flexural stiffness and their optimal thermal insulation make them a suitable solution to obtain efficient structures with performing rigidity and optimal energy safety. In this paper, the mechanical behavior of a sandwich beam with composite skins reinforced by unidirectional carbon fibers is investigated numerically through analyzing the impact of reinforcements specifications on the longitudinal elastic modulus in order to select the adequate sandwich configuration that has an interesting rigidity and an accurate convergence to the analytical approach which is proposed to verify performed numerical simulations. Therefore, concerned study starts by testing flexion performances of skins with various fibers orientations and volume fractions to determine those to use in sandwich beam. For that, the combination of a reinforcement inclination of 30° and a volume ratio of 60% is selected with the one with 60° of fibers orientation and 40% of volume fraction, this last guarantees to chosen skins an important rigidity with an optimal fibers concentration and a great enhance in convergence to analytical results in the sandwich model for the reason of the crucial core role as transverse shear absorber. Thus, a resistant sandwich beam is elaborated from a face-sheet constituted from two layers of previous skins with fibers oriented in 60° and an epoxy core; concerned beam has a longitudinal elastic modulus of 54 Gpa (gigapascal) that equals to the analytical value by a negligible error of 2%.

Keywords: fibers orientation, fibers volume ratio, longitudinal elastic modulus, sandwich beam

Procedia PDF Downloads 139

Authors: T. Nakpun, T. Kanjanarach, T. Kittisopee

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Turnover of community pharmacist can affect continuity of patient care and most importantly the quality of care and also the costs of a pharmacy. It was hypothesized that organizational resources, job characteristics, and social supports had direct effect on pharmacist turnover intention, and indirect effect on pharmacist turnover intention via pharmacist engagement. This research aimed to study influencing factors on pharmacist engagement and pharmacist turnover intention by testing the proposed structural hypothesized model to explain the relationship among organizational resources, job characteristics, and social supports that effect on pharmacist turnover intention and pharmacist engagement in Thai community pharmacists. A cross sectional study design with self-administered questionnaire was conducted in 209 Thai community pharmacists. Data were analyzed using Structural Equation Modeling technique with analysis of a moment structures AMOS program. The final model showed that only organizational resources had significant negative direct effect on pharmacist turnover intention (β =-0.45). Job characteristics and social supports had significant positive relationship with pharmacist engagement (β = 0.44, and 0.55 respectively). Pharmacist engagement had significant negative relationship with pharmacist turnover intention (β = - 0.24). Thus, job characteristics and social supports had significant negative indirect effect on turnover intention via pharmacist engagement (β =-0.11 and -0.13, respectively). The model fit the data well (χ2/ degree of freedom (DF) = 2.12, the goodness of fit index (GFI)=0.89, comparative fit index (CFI) = 0.94 and root mean square error of approximation (RMSEA) = 0.07). This study can be concluded that organizational resources were the most important factor because it had direct effect on pharmacist turnover intention. Job characteristics and social supports were also help decrease pharmacist turnover intention via pharmacist engagement.

Keywords: community pharmacist, influencing factor, turnover intention, work engagement

Procedia PDF Downloads 177

Authors: Marc Solé, Francesc Giné, Magda Valls, Nina Bijedic

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What people say on social media has turned into a rich source of information to understand social behavior. Specifically, the growing use of Twitter social media for political communication has arisen high opportunities to know the opinion of large numbers of politically active individuals in real time and predict the global political tendencies of a specific country. It has led to an increasing body of research on this topic. The majority of these studies have been focused on polarized political contexts characterized by only two alternatives. Unlike them, this paper tackles the challenge of forecasting Spanish political trends, characterized by multiple political parties, by means of analyzing the Twitters Users political tendency. According to this, a new strategy, named Tweets Analysis Strategy (TAS), is proposed. This is based on analyzing the users tweets by means of discovering its sentiment (positive, negative or neutral) and classifying them according to the political party they support. From this individual political tendency, the global political prediction for each political party is calculated. In order to do this, two different strategies for analyzing the sentiment analysis are proposed: one is based on Positive and Negative words Matching (PNM) and the second one is based on a Neural Networks Strategy (NNS). The complete TAS strategy has been performed in a Big-Data environment. The experimental results presented in this paper reveal that NNS strategy performs much better than PNM strategy to analyze the tweet sentiment. In addition, this research analyzes the viability of the TAS strategy to obtain the global trend in a political context make up by multiple parties with an error lower than 23%.

Keywords: political tendency, prediction, sentiment analysis, Twitter

Procedia PDF Downloads 215

Authors: Xiaohua Zhu, Lingling Ma, Yongguang Zhao

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Leaf Area Index (LAI) is a key structural characteristic of crops and plays a significant role in precision agricultural management and farmland ecosystem modeling. However, LAI retrieved from different resolution data contain a scaling bias due to the spatial heterogeneity and model non-linearity, that is, there is scale effect during multi-scale LAI estimate. In this article, a typical farmland in semi-arid regions of Chinese Inner Mongolia is taken as the study area, based on the combination of PROSPECT model and SAIL model, a multiple dimensional Look-Up-Table (LUT) is generated for multiple crops LAI estimation from unmanned aerial vehicle (UAV) hyperspectral data. Based on Taylor expansion method and computational geometry model, a scale transfer model considering both difference between inter- and intra-class is constructed for scale effect analysis of LAI inversion over inhomogeneous surface. The results indicate that, (1) the LUT method based on classification and parameter sensitive analysis is useful for LAI retrieval of corn, potato, sunflower and melon on the typical farmland, with correlation coefficient R2 of 0.82 and root mean square error RMSE of 0.43m2/m-2. (2) The scale effect of LAI is becoming obvious with the decrease of image resolution, and maximum scale bias is more than 45%. (3) The scale effect of inter-classes is higher than that of intra-class, which can be corrected efficiently by the scale transfer model established based Taylor expansion and Computational geometry. After corrected, the maximum scale bias can be reduced to 1.2%.

Keywords: leaf area index (LAI), scale effect, UAV-based hyperspectral data, look-up-table (LUT), remote sensing

Procedia PDF Downloads 423

Authors: Nidhal Azawi

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Colonoscopy procedure is widely used in the world to detect an abnormality. Early diagnosis can help to heal many patients. Because of the unavoidable artifacts that exist in colon images, doctors cannot detect a colon surface precisely. The purpose of this work is to improve the visual quality of colonoscopy videos to provide better information for physicians by removing some artifacts. This work complements a series of work consisting of three previously published papers. In this paper, Optic flow is used for motion compensation, and then consecutive images are aligned/registered to integrate some information to create a new image that has or reveals more information than the original one. Colon images have been classified into informative and noninformative images by using a deep neural network. Then, two different strategies were used to treat informative and noninformative images. Informative images were treated by using Lucas Kanade (LK) with an adaptive temporal mean/median filter, whereas noninformative images are treated by using Lucas Kanade with a derivative of Gaussian (LKDOG) with adaptive temporal median images. A comparison result showed that this work achieved better results than that results in the state- of- the- art strategies for the same degraded colon images data set, which consists of 1000 images. The new proposed algorithm reduced the error alignment by about a factor of 0.3 with a 100% successfully image alignment ratio. In conclusion, this algorithm achieved better results than the state-of-the-art approaches in case of enhancing the informative images as shown in the results section; also, it succeeded to convert the non-informative images that have very few details/no details because of the blurriness/out of focus or because of the specular highlight dominate significant amount of an image to informative images.

Keywords: optic flow, colonoscopy, artifacts, spatial temporal filter

Procedia PDF Downloads 95

Authors: S. I. Usenko, V. N. Golubeva, I. A. Konopkina, I. V. Astakhova, O. V. Vakhnina, A. A. Korableva, A. A. Kalinina, K. B. Zhogova

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Gold can be determined in natural objects and manufactured articles of different origin. The up-to-date status of research and problems of high gold level determination in alloys and manufactured articles are described in detail in the literature. No less important is the task of this metal determination in minerals, process products and waste pieces. The latters, as objects of gold content chemical analysis, are most hard-to-study for two reasons: Because of high requirements to accuracy of analysis results and because of difference in chemical and phase composition. As a rule, such objects are characterized by compound, variable and very often unknown matrix composition that leads to unpredictable and uncontrolled effect on accuracy and other analytical characteristics of analysis technique. In this paper, the methods for the determination of gold are described, using flame atomic-absorption spectrophotometry and gravimetric analysis technique. The techniques are aimed at gold determination in a solution for gold etching (KJ+J2), in the technological mixture formed after cleaning stainless steel members of vacuum-deposit installation with concentrated nitric and hydrochloric acids as well as in gold-containing powder resulted from liquid wastes reprocessing. Optimal conditions for sample preparation and analysis of liquid and solid waste specimens of compound and variable matrix composition were chosen. The boundaries of relative resultant error were determined for the methods within the range of gold mass concentration from 0.1 to 30g/dm3 in the specimens of liquid wastes and mass fractions from 3 to 80% in the specimens of solid wastes.

Keywords: microelectronics waste pieces, gold, sample preparation, atomic-absorption spectrophotometry, gravimetric analysis technique

Procedia PDF Downloads 178

Authors: Jingling Li, Yi Zhang, Wei Liang, Tao Cui, Jun Li

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Compared with terrestrial network, the traffic of spatial information network has both self-similarity and short correlation characteristics. By studying its traffic prediction method, the resource utilization of spatial information network can be improved, and the method can provide an important basis for traffic planning of a spatial information network. In this paper, considering the accuracy and complexity of the algorithm, the spatial information network traffic is decomposed into approximate component with long correlation and detail component with short correlation, and a time series hybrid prediction model based on wavelet decomposition is proposed to predict the spatial network traffic. Firstly, the original traffic data are decomposed to approximate components and detail components by using wavelet decomposition algorithm. According to the autocorrelation and partial correlation smearing and truncation characteristics of each component, the corresponding model (AR/MA/ARMA) of each detail component can be directly established, while the type of approximate component modeling can be established by ARIMA model after smoothing. Finally, the prediction results of the multiple models are fitted to obtain the prediction results of the original data. The method not only considers the self-similarity of a spatial information network, but also takes into account the short correlation caused by network burst information, which is verified by using the measured data of a certain back bone network released by the MAWI working group in 2018. Compared with the typical time series model, the predicted data of hybrid model is closer to the real traffic data and has a smaller relative root means square error, which is more suitable for a spatial information network.

Keywords: spatial information network, traffic prediction, wavelet decomposition, time series model

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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

Procedia PDF Downloads 87

Authors: Neeraj Vij, Amber Brennan, Jenni Winters, Hadi Salehi, Hamy Temkit, Emily Andrisevic, Mohan V. Belthur

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Idiopathic clubfoot is a common lower extremity deformity with an incidence of 1:500. The Ponseti Method is well known as the gold standard of treatment. However, there is limited functional data demonstrating correction of the clubfoot after treatment with the Ponseti method. The purpose of this study was to study the clinical and functional outcomes after the Ponseti method with the Clubfoot Disease-Specific Instrument (CDS) and pedobarography. This IRB-approved prospective study included patients aged 3-18 who were treated for idiopathic clubfoot with the Ponseti method between January 2008 and December 2018. Age-matched controls were identified through siblings of clubfoot patients and other community members. Treatment details were collected through a chart review of the included patients. Laboratory assessment included a physical exam, gait analysis, and pedobarography. The Pediatric Outcomes Data Collection Instrument and the Clubfoot Disease-Specific Instrument were also obtained on clubfoot patients (CF). The Wilcoxson rank-sum test was used to study differences between the CF patients and the typically developing (TD) patients. Statistical significance was set at p < 0.05. There were a total of 37 enrolled patients in our study. 21 were priorly treated for CF and 16 were TD. 94% of the CF patients had bilateral involvement. The age at the start of treatment was 29 days, the average total number of casts was seven to eight, and the average total number of casts after Achilles tenotomy was one. The reoccurrence rate was 25%, tenotomy was required in 94% of patients, and ≥1 tenotomy was required in 25% of patients. There were no significant differences between step length, step width, stride length, force-time integral, maximum peak pressure, foot progression angles, stance phase time, single-limb support time, double limb support time, and gait cycle time between children treated with the Ponseti method and typically developing children. The average post-treatment Pirani and Dimeglio scores were 5.50±0.58 and 15.29±1.58, respectively. The average post-treatment PODCI subscores were: Upper Extremity: 90.28, Transfers: 94.6, Sports: 86.81, Pain: 86.20, Happiness: 89.52, Global: 88.6. The average post-treatment Clubfoot Disease-Specific Instrument scores subscores were: Satisfaction: 73.93, Function: 80.32, Overall: 78.41. The Ponseti Method has a very high success rate and remains to be the gold standard in the treatment of idiopathic clubfoot. Timely management leads to good outcomes and a low need for repeated Achilles tenotomy. Children treated with the Ponseti method demonstrate good functional outcomes as measured through pedobarography. Pedobarography may have clinical utility in studying congenital foot deformities. Objective measures for hours of brace wear could represent an improvement in clubfoot care.

Keywords: functional outcomes, pediatric deformity, patient-reported outcomes, talipes equinovarus

Procedia PDF Downloads 59