Search results for: post-editing machine translation output

Authors: Mohamed Al-Badrashiny, Abdelghani Bellaachia

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This paper presents a classifier ensemble approach for predicting the survivability of the breast cancer patients using the latest database version of the Surveillance, Epidemiology, and End Results (SEER) Program of the National Cancer Institute. The system consists of two main components; features selection and classifier ensemble components. The features selection component divides the features in SEER database into four groups. After that it tries to find the most important features among the four groups that maximizes the weighted average F-score of a certain classification algorithm. The ensemble component uses three different classifiers, each of which models different set of features from SEER through the features selection module. On top of them, another classifier is used to give the final decision based on the output decisions and confidence scores from each of the underlying classifiers. Different classification algorithms have been examined; the best setup found is by using the decision tree, Bayesian network, and Na¨ıve Bayes algorithms for the underlying classifiers and Na¨ıve Bayes for the classifier ensemble step. The system outperforms all published systems to date when evaluated against the exact same data of SEER (period of 1973-2002). It gives 87.39% weighted average F-score compared to 85.82% and 81.34% of the other published systems. By increasing the data size to cover the whole database (period of 1973-2014), the overall weighted average F-score jumps to 92.4% on the held out unseen test set.

Keywords: classifier ensemble, breast cancer survivability, data mining, SEER

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Authors: Fatma Abdedayem

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We propose an extension to the famous method called Bag of words (BOW) which proved a successful role in the field of image categorization. Practically, this method based on representing image with visual words. In this work, firstly, we extract features from images using Spatial Pyramid Representation (SPR) and two dissimilar color descriptors which are opponent-SIFT and transformed-color-SIFT. Secondly, we fuse color local features by joining the two histograms coming from these descriptors. Thirdly, after collecting of all features, we generate multi-dictionaries coming from n random feature subsets that obtained by dividing all features into n random groups. Then, by using these dictionaries separately each image can be represented by n histograms which are lately concatenated horizontally and form the final histogram, that allows to combine Multiple Dictionaries (MDBoW). In the final step, in order to classify image we have applied Support Vector Machine (SVM) on the generated histograms. Experimentally, we have used two dissimilar image datasets in order to test our proposition: Caltech 256 and PASCAL VOC 2007.

Keywords: bag of words (BOW), color descriptors, multi-dictionaries, MDBoW

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Authors: Dalia Salem, Detlef Schulz

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As Germany pursues its ambitious plan towards a power system based on renewable energy sources, the necessity to establish steady, robust microgrids becomes more evident. Inside the microgrid, there is at least one grid-forming inverter responsible for generating the coupling voltage and stabilizing the system frequency within the standardized accepted limits when the microgrid is forced to operate as a stand-alone power system. Grid-forming control for distributed inverters is required to enable steady control of a low-inertia power system. In this paper, a designed droop control technique is tested at the controller of an inverter as a component of a hardware test bed to understand the microgrid behavior in two modes of operation: i) grid-connected and ii) operating in islanding mode. This droop technique includes many current and voltage inner control loops, where the Q-V and P-f droop provide the required terminal output voltage and frequency. The technique is tested first in a simulation model of the inverter in MATLAB/SIMULINK, and the results are compared to the results of the hardware laboratory test. The results of this experiment illuminate the pivotal role of the grid-forming inverter in facilitating microgrid resilience during grid disconnection events and how microgrids could provide the functionality formerly provided by synchronous machinery, such as the black start process.

Keywords: microgrid, grid-forming inverters, droop-control, islanding-operation

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Authors: Babak Forouraghi

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A genetic circuit is a collection of interacting genes and proteins that enable individual cells to implement and perform vital biological functions such as cell division, growth, death, and signaling. In cell engineering, synthetic gene circuits are engineered networks of genes specifically designed to implement functionalities that are not evolved by nature. These engineered networks enable scientists to tackle complex problems such as engineering cells to produce therapeutics within the patient's body, altering T cells to target cancer-related antigens for treatment, improving antibody production using engineered cells, tissue engineering, and production of genetically modified plants and livestock. Construction of computational models to realize genetic circuits is an especially challenging task since it requires the discovery of the flow of genetic information in complex biological systems. Building synthetic biological models is also a time-consuming process with relatively low prediction accuracy for highly complex genetic circuits. The primary goal of this study was to investigate the utility of a pre-trained bidirectional encoder transformer that can accurately predict gene expressions in genetic circuit designs. The main reason behind using transformers is their innate ability (attention mechanism) to take account of the semantic context present in long DNA chains that are heavily dependent on the spatial representation of their constituent genes. Previous approaches to gene circuit design, such as CNN and RNN architectures, are unable to capture semantic dependencies in long contexts, as required in most real-world applications of synthetic biology. For instance, RNN models (LSTM, GRU), although able to learn long-term dependencies, greatly suffer from vanishing gradient and low-efficiency problem when they sequentially process past states and compresses contextual information into a bottleneck with long input sequences. In other words, these architectures are not equipped with the necessary attention mechanisms to follow a long chain of genes with thousands of tokens. To address the above-mentioned limitations, a transformer model was built in this work as a variation to the existing DNA Bidirectional Encoder Representations from Transformers (DNABERT) model. It is shown that the proposed transformer is capable of capturing contextual information from long input sequences with an attention mechanism. In previous works on genetic circuit design, the traditional approaches to classification and regression, such as Random Forrest, Support Vector Machine, and Artificial Neural Networks, were able to achieve reasonably high R2 accuracy levels of 0.95 to 0.97. However, the transformer model utilized in this work, with its attention-based mechanism, was able to achieve a perfect accuracy level of 100%. Further, it is demonstrated that the efficiency of the transformer-based gene expression classifier is not dependent on the presence of large amounts of training examples, which may be difficult to compile in many real-world gene circuit designs.

Keywords: machine learning, classification and regression, gene circuit design, bidirectional transformers

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Authors: Stewart A. Keir, Faik A. Hamad

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This study aims to use both analytical and experimental methods of analysis to examine the accuracy of Computational Fluid Dynamics (CFD) models that can then be used for more complex analyses, accurately representing more elaborate flow phenomena such as internal shockwaves and boundary layers. The geometry used in the analytical study and CFD model is taken from the experimental rig. The analytical study is undertaken using isentropic and adiabatic relationships and the output of the analytical study, the 'shockwave location tool', is created. The results from the analytical study are then used to optimize the redesign an experimental rig for more favorable placement of pressure taps and gain a much better representation of the shockwaves occurring in the divergent section of the nozzle. The CFD model is then optimized through the selection of different parameters, e.g. turbulence models (Spalart-Almaras, Realizable k-epsilon & Standard k-omega) in order to develop an accurate, robust model. The results from the CFD model can then be directly compared to experimental and analytical results in order to gauge the accuracy of each method of analysis. The CFD model will be used to visualize the variation of various parameters such as velocity/Mach number, pressure and turbulence across the shock. The CFD results will be used to investigate the interaction between the shock wave and the boundary layer. The validated model can then be used to modify the nozzle designs which may offer better performance and ease of manufacture and may present feasible improvements to existing high-speed flow applications.

Keywords: CFD, nozzle, fluent, gas dynamics, shock-wave

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Authors: Bin Ji, Kai Xu, Shunyu Yao, Jingjing Liu, Ye Pan

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Human pose estimation is a popular research area in computer vision for its important application in human-machine interface. In recent years, 2D human pose estimation based on convolution neural network has got great progress and development. However, in more and more practical applications, people often need to deal with tasks based on video. It’s not far-fetched for us to consider how to combine the spatial and temporal information together to achieve a balance between computing cost and accuracy. To address this issue, this study proposes a new spatiotemporal model, namely Spatiotemporal Net (STNet) to combine both temporal and spatial information more rationally. As a result, the predicted keypoints heatmap is potentially more accurate and spatially more precise. Under the condition of ensuring the recognition accuracy, the algorithm deal with spatiotemporal series in a decoupled way, which greatly reduces the computation of the model, thus reducing the resource consumption. This study demonstrate the effectiveness of our network over the Penn Action Dataset, and the results indicate superior performance of our network over the existing methods.

Keywords: convolutional long short-term memory, deep learning, human pose estimation, spatiotemporal series

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Authors: Milan Podlena, Stepan Hysek, Jiri Prochazka, Martin Bohm, Jan Bomba

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Corner joints are the weakest part of windows, where the members are connected together. Since the dimensions of the windows started become bigger, the strength requirements for corner joints started to increase as well. Therefore, the aim of this study was to test the samples of corner joints of wooden windows. Moisture content of test specimens was stabilized in the climate chamber. After conditioning, test specimens were loaded in the laboratory conditions onto an universal testing machine and the failure load was measured. Data was recalculated by using goniometric, bending moment and stiffness equation to the stiffness coefficients and the bending moments were investigated. The results showed difference that was observed for the mortise with tenon joint and the dowel joint. This difference was explained by a varied adhesive bond area, which is related to the dimensions of dowels (diameter and length) as well. The bending moments and stiffness ware (except of type of corner joint) also affected by type of used adhesive, type of dowels and wood species.

Keywords: corner joint, wooden window, bending moment, stiffness

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Authors: Safa Boudhaouia, Mohamed Amen Gahbiche, Eliane Giraud, Wacef Ben Salem, Philippe Dal Santo

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Multi-point forming (MPF) and asymmetric incremental forming (ISF) are two flexible processes for sheet metal manufacturing. To take advantages of these two techniques, a hybrid process has been developed: The Multipoint Incremental Forming (MPIF). This process accumulates at once the advantages of each of these last mentioned forming techniques, which makes it a very interesting and particularly an efficient process for single, small, and medium series production. In this paper, an experimental and a numerical investigation of this technique are presented. To highlight the flexibility of this process and its capacity to manufacture standard and complex shapes, several pieces were produced by using MPIF. The forming experiments are performed on a 3-axis CNC machine. Moreover, a numerical model of the MPIF process has been implemented in ABAQUS and the analysis showed a good agreement with experimental results in terms of deformed shape. Furthermore, the use of an elastomeric interpolator allows avoiding classical local defaults like dimples, which are generally caused by the asymmetric contact and also improves the distribution of residual strain. Future works will apply this approach to other alloys used in aeronautic or automotive applications.

Keywords: incremental forming, numerical simulation, MPIF, multipoint forming

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Authors: Dev Ras Pandey, Bharat Mishra, S. K. Tripathi

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Cloud computing is ready to transform the structure of businesses and learning through supplying the real-time applications and provide an immediate help for small to medium sized businesses. The ability to run a hypervisor inside a virtual machine is important feature of virtualization and it is called nested virtualization. In today’s growing field of information technology, many of the virtualization models are available, that provide a convenient approach to implement, but decision for a single model selection is difficult. This paper explains the applications of operating system based virtualization in cloud computing with an appropriate/suitable model with their different specifications and user’s requirements. In the present paper, most popular models are selected, and the selection was based on container and hypervisor based virtualization. Selected models were compared with a wide range of user’s requirements as number of CPUs, memory size, nested virtualization supports, live migration and commercial supports, etc. and we identified a most suitable model of virtualization.

Keywords: virtualization, OS based virtualization, container based virtualization, hypervisor based virtualization

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Authors: M. Benothman, M. Stear, S. Mitchel, O. Abuargob, R. Vijayan, Sateesh Kumar

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Parasitic gastroenteritis caused by gastrointestinal nematodes (GIN) is a serious pathological complication in lambs. The dispersion pattern of GIN influences their transmission dynamics. There is no proper study on this aspect in Scottish Blackface lambs in Scotland. This study undertaken on 758 naturally infected, weaned, straight bred Scottish Blackface lambs in high land pasture in East Scotland extending over three months (August, September and October) in a year, and for three successive years demonstrated that the distribution of faecal egg counts (FEC) followed negative binomial distribution, with the exception of a few samples. The inverse index of dispersion (k) ranged between 0.19 ± 0.51 and 1.09 ± 0.08. Expression of low k values resulting from aggregation in a few individuals, suggested that a small proportion of animals with heavy parasitic influx significantly influenced the level of pasture contamination and parasite transmission. There was no discernible trend in the mean faecal egg count (FEC) and mean herbage larval population (HLP) in different months and in different years. Teladorsagia was the highest pasture contaminant (85.14±14.30 L3/kdh) followed by Nematodirus (53.00±13.96), Ostertagia (28.21±10.18) and Cooperia (11.43±5.55). The results of this study would be useful in instituting gastrointestinal nematode control strategies for sheep in cool temperate agro-ecological zones.

Keywords: blackface lamb, faecal egg count, Gastrointestinal nematodes, herbage larval population, Scotland

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Authors: Esther Kelechi Soyele

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Job satisfaction is the expression of an organisation's fulfillment of work output. In order to achieve effective job satisfaction, the motivation of employees is very essential in stimulating their obligation towards their work. The study examined the motivational strategies, job satisfaction as a correlation of library service delivery in some selected tertiary institutions in southwest Nigeria. The study adopted a descriptive survey research design. A simple random sampling method was employed to select 200 library staff consisting of both library professionals and para-professionals. Two hundred (200) questionnaires were given out, but only one hundred and twenty-nine 129 (96% response rate) were used for the study. Both simple percentage and one and two way ANOVA was used for data analysis. Findings revealed that 60.4% of the respondents were males while 39.6% were female; most of the respondents’ relatively belong to the age group of 31-40 and 41-50, 93.3% were within the age range of 21-50 years, and 43.2 % were M.Sc degree holders. The result revealed a (p < 0.05) significant relationship between work motivational strategies and library service delivery. The results also revealed that motivational development program strategies and job satisfaction have (p < 0.05) a positive significant relationship with library service delivery. It was concluded that work motivation strategies are essential for job satisfaction which is very important in any organization in the attainment of its goals and objectives and helps in maintaining a high standard. The study recommended that more incentive plans that will enhance job satisfaction should be put in place to encourage employees to be more active in carrying out their job effectively.

Keywords: job satisfaction, library, library services, motivational strategies

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Authors: Ava Zarif Sanayei, Mustafa Farjad-Fard, Mohammad-Reza Mohammadian-Behbahani, Leyli Ebrahimi, Sedigheh Sina

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The fabrication and testing of a low-cost air-filled ion chamber for X-ray dosimetry is studied. The chamber is made of a metal cylinder, a central wire, a BC517 Darlington transistor, a 9V DC battery, and a voltmeter in order to have a cost-effective means to measure the dose. The output current of the dosimeter is amplified by the transistor and then fed to the large internal resistance of the voltmeter, producing a readable voltage signal. The dose-response linearity of the ion chamber is evaluated for different exposure scenarios by the X-ray tube. kVp values 70, 90, and 120, and mAs up to 20 are considered. In all experiments, a solid-state dosimeter (Solidose 400, Elimpex Medizintechnik) is used as a reference device for chamber calibration. Each case of exposure is repeated three times, the voltmeter and Solidose readings are recorded, and the mean and standard deviation values are calculated. Then, the calibration curve, derived by plotting voltmeter readings against Solidose readings, provided a linear fit result for all tube kVps of 70, 90, and 120. A 99, 98, and 100% linear relationship, respectively, for kVp values 70, 90, and 120 are demonstrated. The study shows the feasibility of achieving acceptable dose measurements with a simplified setup. Further enhancements to the proposed setup include solutions for limiting the leakage current, optimizing chamber dimensions, utilizing electronic microcontrollers for dedicated data readout, and minimizing the impact of stray electromagnetic fields on the system.

Keywords: dosimetry, ion chamber, radiation detection, X-ray

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Authors: Federica Di Marcantonio, Antoine Leblois, Wolfgang Göbel, Hervè Kerdiles

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Weather-related shocks can threaten the ability of farmers to maintain their agricultural output and food security levels. Informal coping mechanisms (i.e. migration or community risk sharing) have always played a significant role in mitigating the negative effects of weather-related shocks in Ethiopia, but they have been found to be an incomplete strategy, particularly as a response to covariate shocks. Particularly, as an alternative to the traditional risk pooling products, an innovative form of insurance known as Index-based Insurance has received a lot of attention from researchers and international organizations, leading to an increased number of pilot initiatives in many countries. Despite the potential benefit of the product in protecting the livelihoods of farmers and pastoralists against climate shocks, to date there has been an unexpectedly low uptake. Using information from current pilot projects on index-based insurance in Ethiopia, this paper discusses the determinants of uptake that have so far undermined the scaling-up of the products, by focusing in particular on weather data availability, price affordability and willingness to pay. We found that, aside from data constraint issues, high price elasticity and low willingness to pay represent impediments to the development of the market. These results, bring us to rethink the role of index insurance as products for enhancing smallholders’ response to covariate shocks, and particularly for improving their food security.

Keywords: index-based insurance, willingness to pay, satellite information, Ethiopia

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Authors: Muhamad Sahir Bin Ahmad Shatiry, Wan Normeza Wan Zakaria, Mohamad Zaki Hassan

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The pandemic Covid19 has significantly impacted the world; the spreading of the Covid19 virus initially from China has dramatically impacted the world's economy. Therefore, the world reacts with establishing the new way or norm in daily life. The rapid rise of the latest technology has been seen by introducing many technologies to ease human life to have a minor contract between humans and avoid spreading the virus Covid19. Digital twin technologies are one of the technologies created before the pandemic Covid19 but slow adoption in the industry. Throughout the Covid19, most of the companies in the world started to explore to use it. The digital twin technology provides the virtual platform to replicate the existing condition or setup for anything such as office, manufacturing line, factories' machine, building, and many more. This study investigates the effect on the economic perspective after the companies use the Digital Twin technology in the industry. To minimize the contact between humans and to have the ability to operate the system digitally remotely. In this study, the explanation of the digital twin technology impacts the world's microeconomic and macroeconomic.

Keywords: productivity, artificially intelligence, IoT, digital twin

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Authors: Ouahiba Taamallah, Tarek Litim

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The study deals with the burnishing effect of AISI 52100 steel and parameters influence (Py, i and f on surface integrity. The results show that the optimal effects are closely related to the treatment parameters. With a 92% improvement in roughness, SB can be defined as a finishing operation within the machining range. Due to 85% gain in consolidation rate, this treatment constitutes an efficient process for work-hardening of material. In addition, a statistical study based on regression and Taguchi's design has made it possible to develop mathematical models to predict output responses according to the studied burnishing parameters. Response Surface Methodology RSM showed a simultaneous influence of the burnishing parameters and to observe the optimal parameters of the treatment. ANOVA Analysis of results led to validate the prediction model with a determination coefficient R2=94.60% and R2=93.41% for surface roughness and micro-hardness, respectively. Furthermore, a multi-objective optimization allowed to identify a regime characterized by P=20 Kgf, i=5 passes and f=0.08 mm.rev-1, which favors minimum surface roughness and a maximum of micro-hardness. The result was validated by a composite desirability D_i=1 for both surface roughness and microhardness, respectively. Applying optimal parameters, burnishing showed its beneficial effects in fatigue resistance, especially for imposed loading in the low cycle fatigue of the material where the lifespan increased by 90%.

Keywords: AISI 52100 steel, burnishing, Taguchi, fatigue

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Authors: Luyao Zhang, Tianyu Wu, Jiayi Li, Carlos-Gustavo Salas-Flores, Saad Lahrichi

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Recent advances in AI have made algorithmic trading a central role in finance. However, current research and applications are disconnected information islands. We propose a generally applicable pipeline for designing, programming, and evaluating algorithmic trading of stock and crypto tokens. Moreover, we provide comparative case studies for four conventional algorithms, including moving average crossover, volume-weighted average price, sentiment analysis, and statistical arbitrage. Our study offers a systematic way to program and compare different trading strategies. Moreover, we implement our algorithms by object-oriented programming in Python3, which serves as open-source software for future academic research and applications.

Keywords: algorithmic trading, AI for finance, fintech, machine learning, moving average crossover, volume weighted average price, sentiment analysis, statistical arbitrage, pair trading, object-oriented programming, python3

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Authors: Amr Sadek, Abdelrahaman Al-Qahtany, Turkey Salem Al-Qahtany

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In this study, a backpropagation artificial neural network (ANN) model has been used to predict the cooling and heating capacities of air conditioners (AC) under different conditions. Sufficiently large measurement results were obtained from the national energy-efficiency laboratories in Saudi Arabia and were used for the learning process of the ANN model. The parameters affecting the performance of the AC, including temperature, humidity level, specific heat enthalpy indoors and outdoors, and the air volume flow rate of indoor units, have been considered. These parameters were used as inputs for the ANN model, while the cooling and heating capacity values were set as the targets. A backpropagation ANN model with two hidden layers and one output layer could successfully correlate the input parameters with the targets. The characteristics of the ANN model including the input-processing, transfer, neurons-distance, topology, and training functions have been discussed. The performance of the ANN model was monitored over the training epochs and assessed using the mean squared error function. The model was then used to predict the performance of the AC under conditions that were not included in the measurement results. The optimum performance of the AC was also predicted under the different environmental conditions in Saudi Arabia. The uncertainty of the ANN model predictions has been evaluated taking into account the randomness of the data and lack of learning.

Keywords: artificial neural network, uncertainty of model predictions, efficiency of air conditioners, cooling and heating capacities

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Authors: Joseph Chen, N. Hundal

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Conventional machining is a form of subtractive manufacturing, in which a collection of material-working processes utilizing power-driven machine tools are used to remove undesired material to achieve a desired geometry. This paper presents an approach for comparison between turning center and vertical machining center by optimization of cutting parameters at cylindrical workpieces leading to minimum surface roughness by using taguchi methodology. Aluminum alloy was taken to conduct experiments due to its unique high strength-weight ratio that is maintained at elevated temperatures and their exceptional corrosion resistance. During testing, the effects of the cutting parameters on the surface roughness were investigated. Additionally, by using taguchi methodology for each of the cutting parameters (spindle speed, depth of cut, insert diameter, and feed rate) minimum surface roughness for the process of turn-milling was determined according to the cutting parameters. A confirmation experiment demonstrates the effectiveness of taguchi method.

Keywords: surface roughness, Taguchi parameter design, turning center, turn-milling operations, vertical machining center

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Authors: Oladapo Tolulope Ibitoye

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Neural networks have become prominent and widely engaged in algorithmic-based machine learning networks. They are perfect in solving day-to-day issues to a certain extent. Neural networks are computing systems with several interconnected nodes. One of the numerous areas of application of neural networks is object detection. This is a prominent area due to the coronavirus disease pandemic and the post-pandemic phases. Wearing a face mask in public slows the spread of the virus, according to experts’ submission. This calls for the development of a reliable and effective model for detecting face masks on people's faces during compliance checks. The existing neural network models for facemask detection are characterized by their black-box nature and large dataset requirement. The highlighted challenges have compromised the performance of the existing models. The proposed model utilized Faster R-CNN Model on Inception V3 backbone to reduce system complexity and dataset requirement. The model was trained and validated with very few datasets and evaluation results shows an overall accuracy of 96% regardless of skin tone.

Keywords: convolutional neural network, face detection, face mask, masked faces

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Authors: Jean Marriz M. Manzano, Marc D. Rosales, Magdaleno R. Vasquez Jr., Maria Theresa G. De Leon

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This paper discusses the effects of surface roughness on a cantilever beam vibrational energy harvester. A silicon sample was fabricated using MEMS fabrication processes. When etching silicon using deep reactive ion etching (DRIE) at large etch depths, rougher surfaces are observed as a result of increased response in process pressure, amount of coil power and increased helium backside cooling readings. To account for the effects of surface roughness on the characteristics of the cantilever beam, finite element method (FEM) modeling was performed using actual roughness data from fabricated samples. It was found that when etching about 550um of silicon, root mean square roughness parameter, Sq, varies by 1 to 3 um (at 100um thick) across a 6-inch wafer. Given this Sq variation, FEM simulations predict an 8 to148 Hz shift in the resonant frequency while having no significant effect on the output power. The significant shift in the resonant frequency implies that careful consideration of surface roughness from fabrication processes must be done when designing energy harvesters.

Keywords: deep reactive ion etching, finite element method, microelectromechanical systems, multiphysics analysis, surface roughness, vibrational energy harvester

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Authors: M. Michael Gromiha, A. Mary Thangakani, Sandeep Kumar, D. Velmurugan

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The main cause of several neurodegenerative diseases such as Alzhemier, Parkinson, and spongiform encephalopathies is formation of amyloid fibrils and plaques in proteins. We have analyzed different sets of proteins and peptides to understand the influence of sequence-based features on protein aggregation process. The comparison of 373 pairs of homologous mesophilic and thermophilic proteins showed that aggregation-prone regions (APRs) are present in both. But, the thermophilic protein monomers show greater ability to ‘stow away’ the APRs in their hydrophobic cores and protect them from solvent exposure. The comparison of amyloid forming and amorphous b-aggregating hexapeptides suggested distinct preferences for specific residues at the six positions as well as all possible combinations of nine residue pairs. The compositions of residues at different positions and residue pairs have been converted into energy potentials and utilized for distinguishing between amyloid forming and amorphous b-aggregating peptides. Our method could correctly identify the amyloid forming peptides at an accuracy of 95-100% in different datasets of peptides.

Keywords: aggregation, amyloids, thermophilic proteins, amino acid residues, machine learning techniques

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Authors: Sedat Yayla, Mehmet Oruc, Shakhwan Yaseen

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Cavitation phenomena might rigorously impair machine parts such as pumps, propellers and impellers or devices as the pressure in the fluid declines under the liquid's saturation pressure. To evaluate the influence of cavitation, in this research two-dimensional computational fluid dynamics (CFD) venturi models with variety of inlet pressure values, throat lengths and vapor fluid contents were applied. In this research three different vapor contents (0%, 5% 10%), four inlet pressures (2, 4, 6, 8 and 10 atm) and two venturi models were employed at different throat lengths ( 5, 10, 15 and 20 mm) for discovering the impact of each parameter on the cavitation number. It is uncovered that there is a positive correlation between pressure inlet and vapor fluid content and cavitation number. Furthermore, it is unveiled that velocity remains almost constant at the inlet pressures of 6, 8,10atm, nevertheless increasing the length of throat results in the substantial escalation in the velocity of the throat at inlet pressures of 2 and 4 atm. Furthermore, velocity and cavitation number were negatively correlated. The results of the cavitation number varied between 0.092 and 0.495 depending upon the velocity values of the throat.

Keywords: cavitation number, computational fluid dynamics, mixture of fluid, two-phase flow, velocity of throat

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Authors: Mukesh Roy, Basant Singh Sikarwar, Ravi Prakash, Priya Ranjan, Ayush Goyal

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More than 11% of people suffer from weakness in the bone resulting in inability in walking or climbing stairs or from limited upper body and limb immobility. This motivates a fresh bio-mimicking solution to the design of an exo-skeleton to support human movement in the case of partial or total immobility either due to congenital or genetic factors or due to some accident or due to geratological factors. A deeper insight and detailed understanding is required into the workings of the ball and socket joints. Our research is to mimic ball and socket joints to design snugly fitting exoskeletons. Our objective is to design an exoskeleton which is comfortable and the presence of which is not felt if not in use. Towards this goal, a parametric study is conducted to provide detailed design parameters to fabricate an exoskeleton. This work builds up on real data of the design of the exoskeleton, so that the designed exo-skeleton will be able to provide required strength and support to the subject.

Keywords: bio-mimicking, exoskeleton, ball joint, socket joint, artificial limb, patient rehabilitation, joints, human-machine interface, wearable robotics

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Authors: M Ananthakrishnan, Sunil K Patil, Koti Naveen, Inuganti Hemanth Kumar

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State estimation output of EMS forms the base case for all other advanced applications used in real time by a power system operator. Ensuring tuning of state estimator is a repeated process and cannot be left once a good solution is obtained. This paper attempts to demonstrate methods to improve state estimator solution by identifying incorrect modelling and telemetry inputs to the application. In this work, identification of database topology modelling error by plotting static network using node-to-node connection details is demonstrated with examples. Analytical methods to identify wrong transmission parameters, incorrect limits and mistakes in pseudo load and generator modelling are explained with various cases observed. Further, methods used for active and reactive power tuning using bus summation display, reactive power absorption summary, and transformer tap correction are also described. In a large power system, verifying all network static data and modelling parameter on regular basis is difficult .The proposed tuning methods can be easily used by operators to quickly identify errors to obtain the best possible state estimation performance. This, in turn, can lead to improved decision-support capabilities, ultimately enhancing the safety and reliability of the power grid.

Keywords: active power tuning, database modelling, reactive power, state estimator

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Authors: Pengfan Wu, Xiaoan Chen, Ye He, Tianchi Chen

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This paper introduces the design of an unpowered knee exoskeleton to assist human walking by redistributing the moment of the knee joint during stair descent (SD). Considering the knee moment varying with the knee joint angle and the work of the knee joint is all negative, the custom-built spring was used to convert negative work into the potential energy of the spring during flexion, and the obtained energy work as assistance during extension to reduce the consumption of lower limb muscles. The human-machine adaptability problem was left by traditional rigid wearable due to the knee involves sliding and rotating without a fixed-axis rotation, and this paper designed the two-direction grooves to follow the human-knee kinematics, and the wire spring provides a certain resistance to the pin in the groove to prevent extra degrees of freedom. The experiment was performed on a normal stair by healthy young wearing the device on both legs with the surface electromyography recorded. The results show that the quadriceps (knee extensor) were reduced significantly.

Keywords: unpowered exoskeleton, stair descent, knee compliant joint, energy redistribution

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Authors: Pradeep Singh, Shrish Verma

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Software fault prediction models are created by using the source code, processed metrics from the same or previous version of code and related fault data. Some company do not store and keep track of all artifacts which are required for software fault prediction. To construct fault prediction model for such company, the training data from the other projects can be one potential solution. The earlier we predict the fault the less cost it requires to correct. The training data consists of metrics data and related fault data at function/module level. This paper investigates fault predictions at early stage using the cross-project data focusing on the design metrics. In this study, empirical analysis is carried out to validate design metrics for cross project fault prediction. The machine learning techniques used for evaluation is Naïve Bayes. The design phase metrics of other projects can be used as initial guideline for the projects where no previous fault data is available. We analyze seven data sets from NASA Metrics Data Program which offer design as well as code metrics. Overall, the results of cross project is comparable to the within company data learning.

Keywords: software metrics, fault prediction, cross project, within project.

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Authors: Ahmed. A. Hossam Eldin, Karim H. Youssef, Kareem M. AboRas

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Recent current rapid industrial development and energy shortage are essential problems, which face most of the developing countries. Moreover, increased prices of fossil fuel and advanced energy conversion technology lead to the need for renewable energy resources. A study, modelling and simulation of an outdoor micro scale stand alone wind turbine was carried out. For model validation an experimental study was applied. In this research the aim was to clarify effects of real outdoor operating conditions and the instantaneous fluctuations of both wind direction and wind speed on the actual produced power. The results were compared with manufacturer’s data. The experiments were carried out in Borg Al-Arab, Alexandria. This location is on the north Western Coast of Alexandria. The results showed a real max output power for outdoor micro scale wind turbine, which is different from manufacturer’s value. This is due to the fact that the direction of wind speed is not the same as that of the manufacturer’s data. The measured wind speed and direction by the portable metrological weather station anemometer varied with time. The blade tail response could not change the blade direction at the same instant of the wind direction variation. Therefore, designers and users of micro scale wind turbine stand alone system cannot rely on the maker’s name plate data to reach the required power.

Keywords: micro-turbine, wind turbine, inverters, renewable energy, hybrid system

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Authors: Ajai Singh

Abstract:

Simulation of hydrological processes at the watershed outlet through modelling approach is essential for proper planning and implementation of appropriate soil conservation measures in Damodar Barakar catchment, Hazaribagh, India where soil erosion is a dominant problem. This study quantifies the parametric uncertainty involved in simulation of stream flow using Soil and Water Assessment Tool (SWAT), a watershed scale model and Radial Basis Neural Network (RBNN), an artificial neural network model. Both the models were calibrated and validated based on measured stream flow and quantification of the uncertainty in SWAT model output was assessed using ‘‘Sequential Uncertainty Fitting Algorithm’’ (SUFI-2). Though both the model predicted satisfactorily, but RBNN model performed better than SWAT with R2 and NSE values of 0.92 and 0.92 during training, and 0.71 and 0.70 during validation period, respectively. Comparison of the results of the two models also indicates a wider prediction interval for the results of the SWAT model. The values of P-factor related to each model shows that the percentage of observed stream flow values bracketed by the 95PPU in the RBNN model as 91% is higher than the P-factor in SWAT as 87%. In other words the RBNN model estimates the stream flow values more accurately and with less uncertainty. It could be stated that RBNN model based on simple input could be used for estimation of monthly stream flow, missing data, and testing the accuracy and performance of other models.

Keywords: SWAT, RBNN, SUFI 2, bootstrap technique, stream flow, simulation

Procedia PDF Downloads 368

Authors: D. Deepak, N. Yagnesh Sharma

Abstract:

Abrasive Water Jet (AWJ) machining is a relatively new nontraditional machine tool used in machining of fiber reinforced composite. The quality of machined surface depends on jet exit kinetic energy which depends on various operating and material parameters. In the present work the effect abrasive parameters such as its size, concentration and type on jet kinetic energy is investigated using computational fluid dynamics (CFD). In addition, the effect of these parameters on wall shear stress developed inside the nozzle is also investigated. It is found that for the same operating parameters, increase in the abrasive volume fraction (concentration) results in significant decrease in the wall shear stress as well as the jet exit kinetic energy. Increase in the abrasive particle size results in marginal decrease in the jet exit kinetic energy. Numerical simulation also indicates that garnet abrasives produce better jet exit kinetic energy than aluminium oxide and silicon carbide.

Keywords: abrasive water jet machining, jet kinetic energy, operating pressure, wall shear stress, Garnet abrasive

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Authors: Misbah Fatma, Anam Saiyeda

Abstract:

This analysis charts the introduction and expansion of research into the filter bubble phenomena over the last 10 years using a large dataset of academic publications. This bibliometric study demonstrates how interdisciplinary filter bubble research is. The identification of key authors and organizations leading the filter bubble study sheds information on collaborative networks and knowledge transfer. Relevant papers are organized based on themes including algorithmic bias, polarisation, social media, and ethical implications through a systematic examination of the literature. In order to shed light on how these patterns have changed over time, the study plots their historical history. The study also looks at how research is distributed globally, showing geographic patterns and discrepancies in scholarly output. The results of this bibliometric analysis let us fully comprehend the development and reach of filter bubble research. This study offers insights into the ongoing discussion surrounding information personalization and its implications for societal discourse, democratic participation, and the potential risks to an informed citizenry by exposing dominant themes, interdisciplinary collaborations, and geographic patterns. In order to solve the problems caused by filter bubbles and to advance a more diverse and inclusive information environment, this analysis is essential for scholars and researchers.

Keywords: bibliometric analysis, social media, social networking, algorithmic personalization, self-selection, content moderation policies and limited access to information, recommender system and polarization

Procedia PDF Downloads 117