Search results for: robotic experiments

Authors: Gebreegziabher Hailu

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This paper presents the development of residual power series methods aimed at efficiently solving stiff differential equations, which pose significant challenges in numerical analysis due to their rapid changes in solution behavior. The RPSM is a numerical approach that generates polynomial-based approximate solutions without the need for linearization, discretization, or perturbation techniques, making it straightforward to implement and less prone to computational errors. We introduce an approach that utilizes power series expansions combined with residual minimization techniques to enhance convergence and stability. By analyzing the theoretical foundations of stiffness, we delve into the formulation of the residual power series method, detailing how it effectively captures the dynamics of stiff systems while maintaining computational efficiency. Numerical experiments demonstrate the method's superiority in terms of accuracy and computational cost when compared to traditional methods like implicit Runge-Kutta or multistep techniques. We also explore adaptive strategies within our framework to automatically adjust parameters based on the stiffness characteristics of the problem at hand. Ultimately, our findings contribute to the broader toolkit for tackling stiff differential equations, offering a robust alternative that promises to streamline computational workflows in various applied mathematics and engineering contexts.

Keywords: residual power series methods, stiff differential equoations, numerical approach, Runge Kutta methods

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Authors: André C. Silva, Débora N. Sousa, Elenice M. S. Silva, Thales P. Fontes, Raphael S. Tomaz

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Starches are widely used as depressant in froth flotation operations in Brazil due to their efficiency, increasing the selectivity in the inverse flotation of quartz depressing iron ore. Starches market have been growing and improving in recent years, leading to better products attending the requirements of the mineral industry. The major source of starch used for iron ore is corn starch, which needs to be gelatinized with sodium hydroxide (NaOH) prior to use. This stage has a direct impact on industrials costs, once the lowest consumption of NaOH in gelatinization provides better control of the pH in the froth flotation and reduces the amount of electrolytes present in the pulp. In order to evaluate the gelatinization degree of different starches and flour were subjected to the addiction of NaOH and temperature variation experiments. Samples of starch (corn, cassava, HIPIX 100, HIPIX 101 and HIPIX 102 commercialized by Ingredion) and flour (cassava and potato) were tested. The starch samples were characterized through Scanning Electronic Microscopy and the amylose content were determined through spectrometry, swelling and solubility tests. The gelatinization was carried out through titration with NaOH, keeping the solution temperature constant at 40 ^oC. At the end of the tests, the optimal amount of NaOH consumed to gelatinize the starch or flour from different botanical sources was established and a correlation between the content of amylopectin in the starch and the starch/NaOH ratio needed for its gelatinization.

Keywords: froth flotation, gelatinization, sodium hydroxide, starches and flours

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Authors: Zekang Lan, Yan Xu, Yingkun Huang, Dian Huang, Shengzhong Feng

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Jobs on high-performance computing (HPC) clusters can suffer significant performance degradation due to inter-job network interference. Topology-aware job allocation problem (TJAP) is such a problem that decides how to dedicate nodes to specific applications to mitigate inter-job network interference. In this paper, we study the window-based TJAP on a fat-tree network aiming at minimizing the cost of communication hop, a defined inter-job interference metric. The window-based approach for scheduling repeats periodically, taking the jobs in the queue and solving an assignment problem that maps jobs to the available nodes. Two special allocation strategies are considered, i.e., static continuity assignment strategy (SCAS) and dynamic continuity assignment strategy (DCAS). For the SCAS, a 0-1 integer programming is developed. For the DCAS, an approach called neural simulated algorithm (NSA), which is an extension to simulated algorithm (SA) that learns a repair operator and employs them in a guided heuristic search, is proposed. The efficacy of NSA is demonstrated with a computational study against SA and SCIP. The results of numerical experiments indicate that both the model and algorithm proposed in this paper are effective.

Keywords: high-performance computing, job allocation, neural simulated annealing, topology-aware

Procedia PDF Downloads 118

Authors: Pooja Rani, G. S. Mahapatra, S. K. Pandey

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In this paper, we propose a novel approach of Neural Network and Particle Swarm Optimization methods for software reliability prediction. We first explain how to apply compound function in neural network so that we can derive a Flexible Logistic (S-shaped) Growth Curve (FLGC) model. This model mathematically represents software failure as a random process and can be used to evaluate software development status during testing. To avoid trapping in local minima, we have applied Particle Swarm Optimization method to train proposed model using failure test data sets. We drive our proposed model using computational based intelligence modeling. Thus, proposed model becomes Neuro-Particle Swarm Optimization (NPSO) model. We do test result with different inertia weight to update particle and update velocity. We obtain result based on best inertia weight compare along with Personal based oriented PSO (pPSO) help to choose local best in network neighborhood. The applicability of proposed model is demonstrated through real time test data failure set. The results obtained from experiments show that the proposed model has a fairly accurate prediction capability in software reliability.

Keywords: software reliability, flexible logistic growth curve model, software cumulative failure prediction, neural network, particle swarm optimization

Procedia PDF Downloads 344

Authors: Jin Ho Kim, Yujeong Shin, Seong-Jin Cho, Dong-Jin Kim, U-Syn Ha

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Due to the development of the information industry and technologies, cellular phones have must not only function to communicate, but also have functions such as the Internet, e-banking, entertainment, etc. These phones are called smartphones. The performance of smartphones has improved, because of the various functions of smartphones, and the capacity of the battery has been increased gradually. Recently, linear generators have been embedded in smartphones in order to recharge the smartphone's battery. In this study, optimization is performed and an array change of permanent magnets is examined in order to increase efficiency. We propose an optimal design using design of experiments (DOE) to maximize the generated induced voltage. The thickness of the poleshoe and permanent magnet (PM), the height of the poleshoe and PM, and the thickness of the coil are determined to be design variables. We made 25 sampling points using an orthogonal array according to four design variables. We performed electromagnetic finite element analysis to predict the generated induced voltage using the commercial electromagnetic analysis software ANSYS Maxwell. Then, we made an approximate model using the Kriging algorithm, and derived optimal values of the design variables using an evolutionary algorithm. The commercial optimization software PIAnO (Process Integration, Automation, and Optimization) was used with these algorithms. The result of the optimization shows that the generated induced voltage is improved.

Keywords: smartphone, linear generator, design of experiment, approximate model, optimal design

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Authors: Feng Yang

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Machine learning (ML), especially deep learning (DL), has been extensively applied to many applications in recently years and gained great success in solving different problems, including scientific problems. However, conventional ML/DL methodologies are purely data-driven which have the limitations, such as need of ample amount of labelled training data, lack of consistency to physical principles, and lack of generalizability to new problems/domains. Recently, there is a growing consensus that ML models need to further take advantage of prior knowledge to deal with these limitations. Physics-informed machine learning, aiming at integration of physics/domain knowledge into ML, has been recognized as an emerging area of research, especially in the recent 2 to 3 years. In this work, physics-informed ML, specifically physics-informed neural network (NN), is employed and implemented to estimate the displacements at x, y, z directions in a solid mechanics problem that is controlled by equilibrium equations with boundary conditions. By incorporating the physics (i.e. the equilibrium equations) into the learning process of NN, it is showed that the NN can be trained very efficiently with a small set of labelled training data. Experiments with different settings of the NN model and the amount of labelled training data were conducted, and the results show that very high accuracy can be achieved in fulfilling the equilibrium equations as well as in predicting the displacements, e.g. in setting the overall displacement of 0.1, a root mean square error (RMSE) of 2.09 × 10−4 was achieved.

Keywords: deep learning, neural network, physics-informed machine learning, solid mechanics

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Authors: Aida Kalantari, Boyang Ji, Tao Chen, Ivan Mijakovic

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3-hydroxypropanoic acid (3-HP) is one of the most important biomass-derivable platform chemicals that can be converted into a number of industrially important compounds. There have been several attempts at production of 3-HP from renewable sources in cell factories, focusing mainly on Escherichia coli, Klebsiella pneumoniae, and Saccharomyces cerevisiae. Despite the significant progress made in this field, commercially exploitable large-scale production of 3-HP in microbial strains has still not been achieved. In this study, we investigated the potential of Bacillus subtilis to be used as a microbial platform for bioconversion of glycerol into 3-HP. Our recombinant B. subtilis strains overexpress the two-step heterologous pathway containing glycerol dehydratase and aldehyde dehydrogenase from various backgrounds. The recombinant strains harboring the codon-optimized synthetic pathway from K. pneumoniae produced low levels of 3-HP. Since the enzymes in the heterologous pathway are sensitive to oxygen, we had to perform our experiments in micro-aerobic conditions. Under these conditions, the cell produces lactate in order to regenerate NAD+, and we found the lactate production to be in competition with the production of 3-HP. Therefore, based on the in silico predictions, we knocked out the glycerol kinase (glpk), which in combination with growth on glucose, resulted in improving the 3-HP titer to 1 g/L and the removal of lactate. Cultivation of the same strain in an enriched medium improved the 3-HP titer up to 7.6 g/L. Our findings provide the first report of successful introduction of the biosynthetic pathway for conversion of glycerol into 3-HP in B. subtilis.

Keywords: bacillus subtilis, glycerol, 3-hydroxypropanoic acid, metabolic engineering

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Authors: Deng Zengming, Wang Mingjiang

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As 3D video is explored as a hot research topic in the last few decades, free-viewpoint TV (FTV) is no doubt a promising field for its better visual experience and incomparable interactivity. View synthesis is obviously a crucial technology for FTV; it enables to render images in unlimited numbers of virtual viewpoints with the information from limited numbers of reference view. In this paper, a novel hybrid synthesis framework is proposed and blending priority is explored. In contrast to the commonly used View Synthesis Reference Software (VSRS), the presented synthesis process is driven in consideration of the temporal correlation of image sequences. The temporal correlations will be exploited to produce fine synthesis results even near the foreground boundaries. As for the blending priority, this scheme proposed that one of the two reference views is selected to be the main reference view based on the distance between the reference views and virtual view, another view is chosen as the auxiliary viewpoint, just assist to fill the hole pixel with the help of background information. Significant improvement of the proposed approach over the state-of –the-art pixel-based virtual view synthesis method is presented, the results of the experiments show that subjective gains can be observed, and objective PSNR average gains range from 0.5 to 1.3 dB, while SSIM average gains range from 0.01 to 0.05.

Keywords: fusion method, Gaussian mixture model, hybrid framework, view synthesis

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Authors: Yilin Liao, Hewen Li, Paula McConvey

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Speed skaters often face a risk of concussion when they fall on the ice floor and impact crash mats during practices and competitive races. Several variables, including those related to the skater, the crash mat, and the impact position (body side/head/feet impact), are believed to influence the severity of the skater's concussion. While computer simulation modeling can be employed to analyze these accidents, the simulation process is time-consuming and does not provide rapid information for coaches and teams to assess the skater's injury risk in competitive events. This research paper promotes the exploration of the feasibility of using AI techniques for evaluating skater’s potential concussion severity, and to develop a fast concussion prediction tool using artificial neural networks to reduce the risk of treatment delays for injured skaters. The primary data is collected through virtual tests and physical experiments designed to simulate skater-mat impact. It is then analyzed to identify patterns and correlations; finally, it is used to train and fine-tune the artificial neural networks for accurate prediction. The development of the prediction tool by employing machine learning strategies contributes to the application of AI methods in sports science and has theoretical involvements for using AI techniques in predicting and preventing sports-related injuries.

Keywords: artificial neural networks, concussion, machine learning, impact, speed skater

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Authors: Shaira L. Kee, Michael Aaron G. Sy, Myles Joshua T. Tan, Hezerul Abdul Karim, Nouar AlDahoul

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Skin diseases are considered the fourth most common disease, with melanoma and non-melanoma skin cancer as the most common type of cancer in Caucasians. The alarming increase in Skin Cancer cases shows an urgent need for further research to improve diagnostic methods, as early diagnosis can significantly improve the 5-year survival rate. Machine Learning algorithms for image pattern analysis in diagnosing skin lesions can dramatically increase the accuracy rate of detection and decrease possible human errors. Several studies have shown the diagnostic performance of computer algorithms outperformed dermatologists. However, existing methods still need improvements to reduce diagnostic errors and generate efficient and accurate results. Our paper proposes an ensemble method to classify dermoscopic images into benign and malignant skin lesions. The experiments were conducted using the International Skin Imaging Collaboration (ISIC) image samples. The dataset contains 3,297 dermoscopic images with benign and malignant categories. The results show improvement in performance with an accuracy of 88% and an F1 score of 87%, outperforming other existing models such as support vector machine (SVM), Residual network (ResNet50), EfficientNetB0, EfficientNetB4, and VGG16.

Keywords: deep learning - VGG16 - efficientNet - CNN – ensemble – dermoscopic images - melanoma

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Authors: ZhangRun, Yi-Fang Chiang, Li-Shia Huang

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The purpose of this study was to explore the impact of emotional story marketing on the fundraising effectiveness of non-profit organizations and to further clarify the communication effectiveness of emotional story types by using "social distance" which reflects individual differences, as an intervening variable in two experiments. The quasi-experimental design of the development experiment (positive warmth of the story v.s. negative sadness of the story) × social distance (near v.s. far) to clarify the effects of social distance. In this study, we designed the experimental advertising situation ourselves, and data were collected through a questionnaire survey. A total of 391 questionnaires were distributed, and data analysis and hypothesis verification were conducted through variance analysis. According to the analysis results of this study, the use of positive emotional appeals in the design of non-profit organization advertisements on issues related to the loss of children will increase the willingness of listeners to donate. For those with close social distance, there is no significant difference between the positive and "warm" emotional story ads and the negative and "sad" emotional story ads. For those with far social distance, there is a significant difference between the positive and "warm" emotional story ads and the negative and "sad" emotional story ads, with the positive and "warm" emotional appeals improving their willingness to donate. Therefore, this study suggests that NPOs should use more positive and warm emotional stories in their advertising design to enhance the fundraising effectiveness of NPO story marketing.

Keywords: story marketing, emotional appeal, social distance, willingness to donate

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Authors: Purnendu Bose, Jyoti Kainthola

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Due to recent advances in illumination technology, artificially illuminated algal-bacterial photo bioreactors are now a potentially feasible option for simultaneous and comprehensive organic carbon and nutrients removal from secondary treated domestic sewage. The experiments described herein were designed to determine the extent of nutrient uptake in photo bioreactors through algal assimilation. Accordingly, quasi steady state data on algal photo bioreactor performance was obtained under 20 different conditions. Results indicated that irrespective of influent N and P levels, algal biomass recycling resulted in superior performance of algal photo bioreactors in terms of both N and P removals. Further, both N and P removals were positively related to the growth of algal biomass in the reactor. Conditions in the reactor favouring greater algal growth also resulted in greater N and P removals. N and P removals were adversely impacted in reactors with low algal concentrations due to the inability of the algae to grow fast enough under the conditions provided. Increasing algal concentrations in reactors over a certain threshold value through higher algal biomass recycling was also not fruitful, since algal growth slowed under such conditions due to reduced light availability due to algal ‘self-shading’. It was concluded that N removals greater than 80% at high influent N concentrations is not possible with the present reactor configuration. Greater than 80% N removals may however be possible in similar reactors if higher light intensity is provided. High P removal is possible only if the influent N: P ratio in the reactor is aligned closely with the algal stoichiometric requirements for P.

Keywords: nutrients, algae, photo, bioreactor

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Authors: Hankyu Lee, Seoung Bum Kim

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The issue with imbalance and overlapping in the class distribution becomes important in various applications of data mining. The imbalanced dataset is a special case in classification problems in which the number of observations of one class (i.e., major class) heavily exceeds the number of observations of the other class (i.e., minor class). Overlapped dataset is the case where many observations are shared together between the two classes. Imbalanced and overlapped data can be frequently found in many real examples including fraud and abuse patients in healthcare, quality prediction in manufacturing, text classification, oil spill detection, remote sensing, and so on. The class imbalance and overlap problem is the challenging issue because this situation degrades the performance of most of the standard classification algorithms. In this study, we propose a classification procedure that can effectively handle imbalanced and overlapped datasets by splitting data space into three parts: nonoverlapping, light overlapping, and severe overlapping and applying the classification algorithm in each part. These three parts were determined based on the Hausdorff distance and the margin of the modified support vector machine. An experiments study was conducted to examine the properties of the proposed method and compared it with other classification algorithms. The results showed that the proposed method outperformed the competitors under various imbalanced and overlapped situations. Moreover, the applicability of the proposed method was demonstrated through the experiment with real data.

Keywords: classification, imbalanced data with class overlap, split data space, support vector machine

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Authors: Zainab Fadhil Al Toki, Nader Ghareeb

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This study investigates how lightweight a fire resistance door will perform with under types of insulation materials. Data is initially collected from various websites, scientific books and research papers. Results show that different layers of insulation in a single door can perform better than one insulator. Furthermore, insulation materials that are lightweight, high strength and low thermal conductivity are the most preferred for fire-rated doors. Whereas heavy weight, low strength, and high thermal conductivity are least preferred for fire resistance doors. Fire-rated door specifications, theoretical test methodology, structural analysis, and comparison between five different models with diverse layers insulations are presented. Five different door models are being investigated with different insulation materials and arrangements. Model 1 contains an air gap between door layers. Model 2 includes phenolic foam, mild steel and polyurethane. Model 3 includes phenolic foam and glass wool. Model 4 includes polyurethane and glass wool. Model 5 includes only rock wool between the door layers. It is noticed that model 5 is the most efficient model, and its design is simple compared to other models. For this model, numerical calculations are performed to check its efficiency and the results are compared to data from experiments for validation. Good agreement was noticed.

Keywords: fire resistance, insulation, strength, thermal conductivity, lightweight, layers

Procedia PDF Downloads 51

Authors: Chien-Kuo Chang, You-Syuan Wu, Min-Chiu Wu, Bharath-Kumar Boyanapalli

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The electrical treeing mechanism in epoxy resin insulation is a critical area of study concerning the degradation of high-voltage electrical equipment. In this study, we conducted pressure-induced degradation experiments on epoxy resin specimens using a needle-plane electrode structure to simulate electrical treeing. The specimens featured two different defect spacings, allowing for detailed observation facilitated by time-lapse photography. Our investigation revealed four distinct stages of insulation degradation: initial dark tree growth, filamentary tree growth, reverse tree growth, and eventual insulation breakdown. The initial dark treeing stage, though shortest in duration, exhibited a thicker main branch and shorter branching, ceasing upon the appearance of filamentary treeing. Filamentary treeing manifested in two forms: dark filamentary treeing during the resin's glassy state, characterized by branching structures, and fuzzy filamentary treeing during the rubbery state, resembling white feathers. The channels formed by filamentary treeing were observed to be as narrow as a few micrometers and continued to grow until the end of the experiment. Additionally, the transition to reverse treeing occurred when filamentary treeing reached the ground electrode, with the earliest manifestation being growth from the ground electrode towards the high-voltage end.

Keywords: epoxy resin insulation, high-voltage equipment, electrical treeing mechanism

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Authors: Ali Bolat, Ali Bayat, Mustafa Gullu

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Maize can be cultivated both under main and second crop conditions in Turkey. Main pests of maize under second crop conditions are Sesamia nonagrioides Lefebvre (Lepidoptera: Noctuidae) and Ostrinia nubilalis Hübner (Lepidoptera: Crambidae). Aerial spraying applications to control these two main maize pests can be carried out until 2006 in Turkey before it was banned due to environmental concerns like drifting of sprayed pestisides and low biological efficiency. In this context, pulverizers which can spray tall maize plants ( > 175 cm) from the ground have begun to be used. However, the biological efficiency of these sprayers is unknown. Some methods have been tested to increase the success of ground spraying in field experiments conducted in second crop maize in 2008 and 2009. For this aim, 6 spraying methods (air assisted spraying with TX cone jet, domestic cone nozzles, twinjet nozzles, air induction nozzles, standard domestic cone nozzles and tail booms) were used at two application rates (150 and 300 l.ha-1) by a sprayer. In the study, biological efficacy evaluations of each methods were measured in each parcel. Biological efficacy evaluations included counts of number of insect damaged plants, number of holes in stems and live larvae and pupa in stems of selected plants. As a result, the highest biological efficacy value (close to 70%) was obtained from Air Assisted Spraying method at 300 l / ha application volume.

Keywords: air assisted sprayer, drift nozzles, biological efficiency, maize plant

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Authors: Bhavita S. Dave, Chandresh H. Solanki, Atul K. Desai

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Soils of Greater Himalayas mostly pertain to Leh & Ladakh, Lahaul & Sppiti, & high reaches to Uttarakhand. The moisture regime is aridic. The arid zone starts from Baralacha pass in Lahaul and covers the entire Spiti valley in the district of Lahaul & Spiti, Himachal Pradesh of India. Here, the present study is an attempt to determine the suction value of soil collected from the arid zone of Spiti valley for different freezing-thawing cycles considering the climate ranges of Spiti valley. Suction is the basic and most important parameter which influences the behavior of unsaturated soil. It is essential to determine the suction value of unsaturated soil before other tests like shear test, and permeability. Basically, it is the negative pore water pressure in partially saturated soil measured in terms of the height of the water column. The filter paper method has been used for the study as an economical approach to evaluate suction. It is the only method from which both contact and non-contact suction can be deduced. In this study, soil specimens were subjected to 0, 1, 3, & 5 freezing-thawing (F-T) cycles for different degrees of saturation to have a wide range of suction, and soil freezing characteristic curves (SFCC) were formulated for all F-T cycles. The result data collected from the experiments have shown best-fitted values using Fredlund & Xing model for each SFCC.

Keywords: suction, arid region soil, soil freezing characteristic curve, freezing-thawing cycle

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Authors: Eu Tteum Ha, Seyoung Kim, Jeongmin Kim, Kwang Ryel Ryu

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The smart home environment backed up by IoT (internet of things) technologies enables intelligent services based on the awareness of the situation a user is currently in. One of the convenient sensors for recognizing the situations within a home is the smart meter that can monitor the status of each electrical appliance in real time. This paper aims at learning a Bayesian network that models the causal relationship between the user situations and the status of the electrical appliances. Using such a network, we can infer the current situation based on the observed status of the appliances. However, learning the conditional probability tables (CPTs) of the network requires many training examples that cannot be obtained unless the user situations are closely monitored by any means. This paper proposes a method for learning the CPT entries of the network relying only on the user feedbacks generated occasionally. In our case study with a robot vacuum cleaner, the feedback comes in whenever the user gives an order to the robot adversely from its preprogrammed setting. Given a network with randomly initialized CPT entries, our proposed method uses this feedback information to adjust relevant CPT entries in the direction of increasing the probability of recognizing the desired situations. Simulation experiments show that our method can rapidly improve the recognition performance of the Bayesian network using a relatively small number of feedbacks.

Keywords: Bayesian network, IoT, learning, situation -awareness, smart home

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Authors: Mohamed Mahmoud Hasan, Hoda M. O. Mokhtar

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With the gigantic increment in portable applications use and the spread of positioning and location-aware technologies that we are seeing today, new procedures and methodologies for location-based strategies are required. Location recommendation is one of the highly demanded location-aware applications uniquely with the wide accessibility of social network applications that are location-aware including Facebook check-ins, Foursquare, and others. In this paper, we aim to present a new methodology for location recommendation. The proposed approach coordinates customary spatial traits alongside other essential components including shortest distance, and user interests. We also present another idea namely, "activity trajectory" that represents trajectory that fulfills the set of activities that the user is intrigued to do. The approach dispatched acquaints the related distance value to select trajectory(ies) with minimum cost value (distance) and spatial-area to prune unneeded directions. The proposed calculation utilizes the idea of movement direction to prescribe most comparable N-trajectory(ies) that matches the client's required action design with least voyaging separation. To upgrade the execution of the proposed approach, parallel handling is applied through the employment of a MapReduce based approach. Experiments taking into account genuine information sets were built up and tested for assessing the proposed approach. The exhibited tests indicate how the proposed approach beets different strategies giving better precision and run time.

Keywords: location based recommendation, map-reduce, recommendation system, trajectory search

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Authors: Giselle Maggie-Fer Castañeda Lozano

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The main objective of this research was to explore the role of neural network architectures in simulating behavioral phenomena as a potential explanation for selective associations, specifically related to biological constraints on learning. Biological constraints on learning refer to the limitations observed in conditioning procedures, where learning is expected to occur. The study involved simulations of five different experiments exploring various phenomena and sources of biological constraints in learning. These simulations included the interaction between response and reinforcer, stimulus and reinforcer, specificity of stimulus-reinforcer associations, species differences, neuroanatomical constraints, and learning in uncontrolled conditions. The overall results demonstrated that by manipulating neural network architectures, conditions can be created to model and explain diverse biological constraints frequently reported in comparative psychology literature as learning typicities. Additionally, the simulations offer predictive content worthy of experimental testing in the pursuit of new discoveries regarding the specificity of learning. The implications and limitations of these findings are discussed. Finally, it is suggested that this research could inaugurate a line of inquiry involving the use of neural networks to study biological factors in behavior, fostering the development of more ethical and precise research practices.

Keywords: comparative psychology, connectionism, conditioning, experimental analysis of behavior, neural networks

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Authors: Demisash, Abate Getnet, Gudisa, Ababo Geleta, Daba, Berhane Olani

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As consumption trends of fish are rising in Ethiopia, assessment of the environmental performance of Fisheries becomes vital. Hence, Cleaner Production Assessment was conducted on Lake Tana No.1 Fish Supply Association. This paper focuses on determining the characteristics, quantity, and setting up cleaner production options for the site with the experimental investigation. The survey analysis showed that illegal waste dumping in Lake Tana is common practice in the area, and some of the main reasons raised were they have no option than doing this for dis-charging fish wastes. Quantifying a fish waste by examination of records at the point of generation resulted in a generation rate of 72,822.61 kg per year, which is a significant amount of waste and needs management system. The result of the proximate analysis showed high free fat content of about 12.33%, and this was a good candidate for the production of biodiesel that has been set as an option for fish waste utilization. Among the different waste management options, waste reduction by product optimization, which involves biodiesel production, was chosen as a potential method. Laboratory scale experiments were performed to produce a renewable energy source from the wastes. The resulting biodiesel was characterized and found to have a density of 0.756kg/L, viscosity 0.24p, and 153°C flashpoints, which shows the product has values in compliance with the American Society for Testing and Materials (ASTM) standards.

Keywords: biodiesel, cleaner production, renewable energy, waste management

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Authors: Summer Hassan, David Crossman

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Myocardial fibrosis (MF) has been loosely defined as the process occurring in the pathological remodeling of the myocardium due to excessive production and deposition of extracellular matrix (ECM) proteins, including collagen. This reduces tissue compliance and accelerates progression to heart failure, as well as affecting the electrical properties of the myocytes resulting in arrhythmias. Microscopic interrogation of MF is key to understanding the molecular orchestrators of disease. It is well-established that recruitment and stimulation of myofibroblasts result in Collagen deposition and the resulting expansion in the ECM. Many types of Collagens have been identified and implicated in scarring of tissue. In a series of experiments conducted at our lab, we aim to elucidate the role collagen VI plays in the development of myocardial fibrosis and its direct impact on myocardial function. This was investigated through an animal experiment in Rats with Collagen VI knockout diseased and healthy animals as well as Collagen VI wild diseased and healthy rats. Echocardiogram assessments of these rats ensued at four-time points, followed by microscopic interrogation of the myocardium aiming to correlate the role collagen VI plays in myocardial function. Our results demonstrate a deterioration in cardiac function as represented by the ejection fraction in the knockout healthy and diseased rats. This elucidates a potential protective role that collagen-VI plays following a myocardial insult. Current work is dedicated to the microscopic characterisation of the fibrotic process in all rat groups, with the results to follow.

Keywords: heart failure, myocardial fibrosis, collagen, echocardiogram, confocal microscopy

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Authors: Amauri Josafat Gomez Aguilar, Jean Pierre Kenné

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This research presents the development of a self-sustainable manufacturing system from a circular economy perspective, structured by a multi-stage serial production system consisting of a series of machines under deterioration in charge of producing a single product and a reverse remanufacturing system constituted by the same productive systems of the first scheme and different tooling, fed by-products collected at the end of their life cycle, and non-conforming elements of the first productive scheme. Since the advanced production manufacturing system is unable to satisfy the customer's quality expectations completely, we propose the development of a mixed integer linear mathematical model focused on the optimal search and assignment of quality stations and preventive maintenance operation to the machines over a time horizon, intending to segregate the correct number of non-conforming parts for reuse in the remanufacturing system and thereby minimizing production, quality, maintenance, and customer non-conformance penalties. Numerical experiments are performed to analyze the solutions found by the model under different scenarios. The results showed that the correct implementation of a closed manufacturing system and allocation of quality inspection and preventive maintenance operations generate better levels of customer satisfaction and an efficient manufacturing system.

Keywords: closed loop, mixed integer linear programming, preventive maintenance, quality inspection

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Authors: Isheanesu Hungwe, Munyaradzi Shumba, Tichaona Nharingo

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High levels of nitrates in drinking water present a potential risk to human health for it is responsible for methemoglobinemia in infants. It also gives rise to eutrophication of dams and rivers. It is, therefore, important to find ways of compating the increasing amount of nitrates in the environment. This study explored the bioremediation of nitrates from aqueous solution using Brachystegia spiciformis leaf powder (BSLP). The acid treated leaf powder was characterized using FTIR and SEM before and after nitrate biosorption and desorption experiments. Critical biosorption factors, pH, contact time and biomass dosage were optimized as 4, 30 minutes and 10 g/L respectively. The equilibrium data generated from the investigation of the effect of initial nitrate ion concentration fitted the isotherm models in the order Dudinin-Radushkevich < Halsey=Freundlich < Langmuir < Temkin model based on the correlation of determination (R2). The Freundlich’s adsorption intensity and Langmuir’s separation factors revealed the favorability of nitrate ion sorption onto BSLP biomass with maximum sorption capacity of 87.297 mg/g. About 95% of the adsorbed nitrate was removed from the biomass under alkaline conditions (pH 11) proving that the regeration of the biomass, critical in sorption-desorption cycles, was possible. It was concluded that the BSLP was a multifunctional group material characterised by both micropores and macropores that could be effectively utilised in nitrate ion removal from aqueous solutions.

Keywords: adsorption, brachystegia spiciformis, methemoglobinemia, nitrates

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Authors: Chen Bingru

Abstract:

This study is intended to describe and analyze the effects of polysyllabic shortening and word or phrase boundary on the duration patterns of spoken utterances by Mandarin learners of English in comparison with native speakers of English. To investigate the relative contribution of these effects, two production experiments were conducted. The study included 11 native British English speakers and 20 Mandarin learners of English who were asked to produce four sets of tokens consisting of a mono-syllabic base form, disyllabic, and trisyllabic words derived from the base by the addition of suffixes, and a set of short sentences with a particular combination of phrase size, stress pattern, and boundary location. The duration of words and segments was measured, and results from the data analysis suggest that the amount of polysyllabic shortening and the effect of word or phrase position are likely to affect a Chinese accent for Mandarin ESL speakers. This study sheds light on research on the duration patterns of language by demonstrating the effect of duration-related factors on the foreign accent of Mandarin ESL speakers. It can also benefit both L2 learners and language teachers by increasing their sensitivity to the duration differences and difficulties experienced by L2 learners of English. An understanding of the amount of polysyllabic shortening and the effect of position in words and phrase on syllable duration can also facilitate L2 teachers to establish priorities for teaching pronunciation to ESL learners.

Keywords: duration patterns, Chinese accent, Mandarin ESL speakers, polysyllabic shortening

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Authors: Suhas Pednekar, Prashant Chavan, Ramesh Chaughule, Deepak Patkar

Abstract:

Iron oxide (Fe3O4) magnetic nanoparticles (MNPs) are one of the most attractive nanomaterials for various biomedical applications. An important potential medical application of polymer-coated iron oxide nanoparticles (NPs) is as imaging agents. Composition, size, morphology and surface chemistry of these nanoparticles can now be tailored by various processes to not only improve magnetic properties but also affect the behavior of nanoparticles in vivo. MNPs are being actively investigated as the next generation of magnetic resonance imaging (MRI) contrast agents. Also, there is considerable interest in developing magnetic nanoparticles and their surface modifications with therapeutic agents. Our study involves the synthesis of biocompatible cancer drug coated with iron oxide nanoparticles and to evaluate their efficacy as MRI contrast agents. A simple and rapid microwave method to prepare Fe3O4 nanoparticles has been developed. The drug was successfully conjugated to the Fe3O4 nanoparticles which can be used for various applications. The relaxivity R2 (reciprocal of the spin-spin relaxation time T2) is an important factor to determine the efficacy of Fe nanoparticles as contrast agents for MRI experiments. R2 values of the coated magnetic nanoparticles were also measured using MRI technique and the results showed that R2 of the Fe complex consisting of Fe3O4, polymer and drug was higher than that of bare Fe nanoparticles and polymer coated nanoparticles. This is due to the increase in hydrodynamic sizes of Fe NPs. The results with various amounts of iron molar concentrations are also discussed. Using MRI, it is seen that the R2 relaxivity increases linearly with increase in concentration of Fe NPs in water.

Keywords: cancer drug, hydrodynamic size, magnetic nanoparticles, MRI

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Authors: Alok S Chauhan, Sridhar S., Pradyumna R.

Abstract:

In the process of precision investment casting of turbine hollow blade/vane components, a part of the dimensional deviations observed in the castings can be attributed to the wax pattern. In the process of injection moulding of wax to produce patterns, heated wax shrinks in size during cooling in the die, leading to a reduction in the dimensions of the pattern. Also, flow and thermal induced residual stresses result in shrinkage & warpage of the component after removal from the die, further adding to the deviations. Injection moulding parameters such as wax temperature, flow rate, packing pressure, etc. affect the flow and thermal behavior of the component and hence are directly responsible for the dimensional deviations. There is a need to precisely determine and control these deviations in order to achieve stringent dimensional accuracies imposed on these castings by aerospace standards. Simulation based approaches provide a platform to predict these dimensional deviations without resorting to elaborate experimentation. In the present paper, Moldex3D simulation package has been utilized to analyze the effect of variations in injection temperature, packing pressure and cooling time on the shrinkage behavior of a stepped pattern. Two types of waxes with different rheological properties have been included in the study to gauge the effect of change in wax on the dimensional deviations. A full factorial design of experiments has been configured with these parameters and results of analysis of variance have been presented.

Keywords: wax patterns, investment casting, pattern die/mould, wax injection, Moldex3D simulation

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Authors: Yin Hua Zhang

Abstract:

Cardiac metabolism is essential in myocardial contraction. In addition to glucose, fatty acids (FA) are essential in producing energy in the myocardium since FA-dependent beta-oxidation accounts for > 70-90% of cellular ATP under resting conditions. However, metabolism shifts from FAs to glucose utilization during disease progression (e.g. hypertrophy and ischemic myocardium), where glucose oxidation and glycolysis become the predominant sources of cellular ATP. At advanced failing stage, both glycolysis and beta-oxidation are dysregulated, result in insufficient supply of intracellular ATP and weakened myocardial contractility. Undeniably, our understandings of myocyte function in healthy and diseased hearts are based on glucose (10 mM)-dependent metabolism because glucose is the “sole” metabolic substrate in most of the physiological experiments. In view of the importance of FAs in cardiovascular health and diseases, we aimed to elucidate the impacts of FA supplementation on myocyte contractility and evaluate cellular mechanisms those mediate the functions in normal heart and with pathological stress. In particular, we have investigated cardiac excitation-contraction (E-C) coupling in the presence and absence of FAs in normal and hypertensive rat left ventricular (LV) myocytes. Our results reveal that FAs increase mitochondrial activity, intracellular [Ca²+]i, and LV myocyte contraction in healthy LV myocytes, whereas FA-dependent cardiac inotropyis attenuated in hypertension. FA-dependent myofilament Ca²+ desensitization could be fundamental in regulating [Ca²+]i. Collectively, FAs supplementation resets cardiac E-C coupling scheme in healthy and diseased hearts.

Keywords: hypertension, fatty acid, heart, calcium

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Authors: A. Razavykia, A. Esmaeilzadeh, S. Iranmanesh

Abstract:

Many orthopedic implants like proximal humerus cases require lower surface roughness and almost immediate/short lead time surgery. Thus, rapid response from the manufacturer is very crucial. Tool path strategy of milling process has a direct influence on the surface roughness and lead time of medical implant. High-speed milling as promised process would improve the machined surface quality, but conventional or super-abrasive grinding still required which imposes some drawbacks such as additional costs and time. Currently, many CAD/CAM software offers some different tool path strategies to milling free form surfaces. Nevertheless, the users must identify how to choose the strategies according to cutting tool geometry, geometry complexity, and their effects on the machined surface. This study investigates the effect of different tool path strategies for milling a proximal humerus head during finishing operation on stainless steel 316L. Experiments have been performed using MAHO MH700 S vertical milling machine and four machining strategies, namely, spiral outward, spiral inward, and radial as well as zig-zag. In all cases, the obtained surfaces were analyzed in terms of roughness and dimension accuracy compared with those obtained by simulation. The findings provide evidence that surface roughness, dimensional accuracy, and machining time have been affected by the considered tool path strategy.

Keywords: CAD/CAM software, milling, orthopedic implants, tool path strategy

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Authors: Iman Adnan Annon, Kadhim F. Alsultan

Abstract:

This study investigate the internal corrosion of low carbon steel pipelines in the crude oil, as well as prepare and use natural and locally available plant as a natural corrosion inhibiter, the nature extraction achieved by two types of solvents in order to show the solvent effect on inhibition process, the first being distilled water and the second is diethyl ether. FT-IR spectra and using a chemical reagents achieved to detection the presence of many active groups and the presence of tannins, phenols, and alkaloids in the natural extraction. Some experiments were achieved to estimate the performance of a new inhibitor, one of these tests include corrosion measurement by simple immersion in crude oil within and without inhibitors which added in different amounts 30,40,50and 60 ppm at tow temperature 300 and 323k, where the best inhibition efficiencies which get when added the inhibitors in a critical amounts or closest to it, since for the aqueous extract (EB-A) the inhibition efficiency reached (94.4) and (86.71)% at 300 and 323k respectively, and for diethyl ether extract (EB-D) reached (82.87) and (84.6)% at 300 and 323k respectively. Optical microscopy examination have been conducted to evaluate the corrosion nature where it show a clear difference in the topography of the immersed samples surface after add the inhibitors at two temperatures. The results show that the new corrosion inhibitor is not only equivalent to a chemical inhibitor but has greatly improvement properties such as: high efficiency, low cost, non-toxic, easily to produce, and nonpolluting as compared with chemical inhibitor.

Keywords: corrosion in pipeline, inhibitors, crude oil, carbon steel, types of solvent

Procedia PDF Downloads 140