Search results for: biology experiments

Authors: Azam Fazelpour, Saeed R. Dehghani, Vlastimil Masek, Yuri S. Muzychka

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This study employs a method based on image analyses and structure information to detect accumulated ice on known structures. The icing of marine vessels and offshore structures causes significant reductions in their efficiency and creates unsafe working conditions. Image processing methods are used to measure ice loads automatically. Most image processing methods are developed based on captured image analyses. In this method, ice loads on structures are calculated by defining structure coordinates and processing captured images. A pyramidal structure is designed with nine cylindrical bars as the known structure of experimental setup. Unsymmetrical ice accumulated on the structure in a cold room represents the actual case of experiments. Camera intrinsic and extrinsic parameters are used to define structure coordinates in the image coordinate system according to the camera location and angle. The thresholding method is applied to capture images and detect iced structures in a binary image. The ice thickness of each element is calculated by combining the information from the binary image and the structure coordinate. Averaging ice diameters from different camera views obtains ice thicknesses of structure elements. Comparison between ice load measurements using this method and the actual ice loads shows positive correlations with an acceptable range of error. The method can be applied to complex structures defining structure and camera coordinates.

Keywords: camera calibration, ice detection, ice load measurements, image processing

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Authors: Mohd Adil, Rosina Khan, Danishuddin, Asad U. Khan

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The aim of this study was to evaluate the influence of the crude and active solvent fraction of Trachyspermum ammi on S. mutans cariogenicity, effect on expression of genes involved in biofilm formation and caries development in rats. GC–MS was carried out to identify the major components present in the crude and the active fraction of T. ammi. The crude extract and the solvent fraction exhibiting least MIC were selected for further experiments. Scanning electron microscopy was carried out to observe the effect of the extracts on S. mutans biofilm. Comparative gene expression analysis was carried out for nine selected genes. 2-Isopropyl-5-methyl-phenol was found as major compound in crude and the active fraction. Binding site of this compound within the proteins involved in biofilm formation was mapped with the help of docking studies. Real-time RT-PCR analyses revealed significant suppression of the genes involved in biofilm formation. All the test groups showed reduction in caries (smooth surface as well as sulcal surface caries) in rats. Moreover, it also provides new insight to understand the mechanism influencing biofilm formation in S. mutans. Furthermore, the data suggest the putative cariostatic properties of T. Ammi and hence can be used as an alternative medicine to prevent caries infection.

Keywords: bio-film, Streptococcus mutans, dental caries, bio-informatic

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Authors: Alae Lamtai, Said Elkoun, Hniya Kharmoudi, Mathieu Robert, Carl Diez

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This study applies Taguchi's design of experiment methodology and grey relational analysis (GRA) for multi objective optimization of an industrial recycling line. This last is composed mainly of a mono and twin-screw extruder and a filtration system. Experiments were performed according to L₁₆ standard orthogonal array based on five process parameters, namely: mono screw design, screw speed of the mono and twin-screw extruder, melt pump pressure, and filter mesh size. The objective of this optimization is to improve the durability of the Polyethylene (PE) blend by decreasing the loss of Stress Crack resistance (SCR) using Notched Crack Ligament Stress (NCLS) test and Unnotched Crack Ligament Stress (UCLS) in parallel with increasing the gain of Izod impact strength of the Polyethylene (PE) blend before and after recycling. Based on Grey Relational Analysis (GRA), the optimal setting of process parameters was identified, and the results indicated that the mono-screw design and screw speed of both mono and twin-screw extruder impact significantly the mechanical properties of recycled Polyethylene (PE) blend.

Keywords: Taguchi, recycling line, polyethylene, stress crack resistance, Izod impact strength, grey relational analysis

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Authors: Pujan Sarkar

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Frictional behavior of glass epoxy and Al particulate glass-epoxy composites sliding against mild steel are investigated experimentally at normal atmospheric condition. Glass epoxy (0 wt% Al) and 5, 10 and 15 wt% Al particulate filled glass-epoxy composites are fabricated in conventional hand lay-up technique followed by light compression moulding process. A pin on disc type friction apparatus is used under dry sliding conditions. Experiments are carried out at a normal load of 5-50 N, and sliding speeds of 0.5-5.0 m/s for a fixed duration. Variations of friction coefficient with sliding time at different loads and speeds for all the samples are considered. Results show that the friction coefficient is influenced by sliding time, normal loads, sliding speeds, and wt% of Al content. In general, with respect to time, friction coefficient increases initially with a lot of fluctuations for a certain duration. After that, it becomes stable for the rest of the experimental time. With the increase of normal load, friction coefficient decreases at all speed levels and for all the samples whereas, friction coefficient increases with the increase of sliding speed at all normal loads for glass epoxy and 5 wt% Al content glass-epoxy composites. But for 10 and 15 wt%, Al content composites at all loads, reverse trend of friction coefficient has been recorded. Under different tribological conditions, the suitability of composites in respect of wt% of Al content is noted, and 5 wt% Al content glass-epoxy composite reports as the lowest frictional material at all loads compared to other samples.

Keywords: Al powder, composite, epoxy, friction, glass fiber

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Authors: Gehan El-Sayed Sharaf El-Deen

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In this study, super paramagnetic iron-oxide nano- materials (SPMIN) were investigated for removal of toxic heavy metals from aqueous solution. The magnetic nanoparticles of 12 nm were synthesized using a co-precipitation method and characterized by transmission electron microscopy (TEM), transform infrared spectroscopy (FTIR), x-ray diffraction (XRD) and vibrating sample magnetometer (VSM). Batch experiments carried out to investigate the influence of different parameters such as contact time, initial concentration of metal ions, the dosage of SPMIN, desorption,pH value of solutions. The adsorption process was found to be highly pH dependent, which made the nanoparticles selectively adsorb these three metals from wastewater. Maximum sorption for all the studies cations obtained at the first half hour and reached equilibrium at one hour. The adsorption data of heavy metals studied were well fitted with the Langmuir isotherm and the equilibrium data show the percent removal of Ni2+, Zn2+ and Cd2+ were 96.5%, 80% and 75%, respectively. Desorption studies in acidic medium indicate that Zn2+, Ni2+ and Cd2+ were removed by 89%, 2% and 18% from the first cycle. Regeneration studies indicated that SPMIN nanoparticles undergoing successive adsorption–desorption processes for Zn2+ ions retained original metal removal capacity. The results revealed that the most prominent advantage of the prepared SPMIN adsorbent consisted in their separation convenience compared to the other adsorbents and SPMIN has high efficiency for removal the investigated metals from aqueous solution.

Keywords: heavy metals, magnetic nanoparticles, removal efficiency, Batch technique

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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: 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

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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

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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: Aleya Ferdausi, Meriel Jones, Anthony Halls

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The Amaryllidaceae genus Narcissus contains secondary metabolites, which are important sources of bioactive compounds such as pharmaceuticals indicating that their biological activity extends from the native plant to humans. Transcriptome analysis (RNA-seq) is an effective platform for the identification and functional characterization of candidate genes as well as to identify genes encoding uncharacterized enzymes. The biotechnological production of secondary metabolites in plant cell or organ cultures has become a tempting alternative to the extraction of whole plant material. The biochemical pathways for the production of secondary metabolites require primary metabolites to undergo a series of modifications catalyzed by enzymes such as cytochrome P450s, methyltransferases, glycosyltransferases, and acyltransferases. Differential gene expression analysis of Narcissus was obtained from two conditions, i.e. field and in vitro callus. Callus was obtained from modified MS (Murashige and Skoog) media supplemented with growth regulators and twin-scale explants from Narcissus cv. Carlton bulb. A total of 2153 differentially expressed transcripts were detected in Narcissus bulb and in vitro callus, and 78.95% of those were annotated. It showed the expression of genes involved in the biosynthesis of alkaloids were present in both conditions i.e. cytochrome P450s, O-methyltransferase (OMTs), NADP/NADPH dehydrogenases or reductases, SAM-synthetases or decarboxylases, 3-ketoacyl-CoA, acyl-CoA, cinnamoyl-CoA, cinnamate 4-hydroxylase, alcohol dehydrogenase, caffeic acid, N-methyltransferase, and NADPH-cytochrome P450s. However, cytochrome P450s and OMTs involved in the later stage of Amaryllidaceae alkaloids biosynthesis were mainly up-regulated in field samples. Whereas, the enzymes involved in initial biosynthetic pathways i.e. fructose biphosphate adolase, aminotransferases, dehydrogenases, hydroxyl methyl glutarate and glutamate synthase leading to the biosynthesis of precursors; tyrosine, phenylalanine and tryptophan for secondary metabolites were up-regulated in callus. The knowledge of probable genes involved in secondary metabolism and their regulation in different tissues will provide insight into the Narcissus plant biology related to alkaloid production.

Keywords: narcissus, callus, transcriptomics, secondary metabolites

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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: Joanna Estkowska

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The Sensory Integration Method is aimed primarily at children with learning disabilities. It can also be used as a complementary method in treatment of children with cerebral palsy, autistic, mentally handicapped, blind and deaf. Autism is holistic development disorder that manifests itself in the specific functioning of a child. The most characteristic are: disorders in communication, difficulties in social relations, rigid patterns of behavior and impairment in sensory processing. In addition to these disorders may occur abnormal intellectual development, attention deficit disorders, perceptual disorders and others. This study was focused on the application sensory integration techniques in science education of autistic students. The lack of proper sensory integration causes problems with complicated processes such as motor coordination, movement planning, visual or auditory perception, speech, writing, reading or counting. Good functioning and cooperation of proprioceptive, tactile and vestibular sense affect the child’s mastery of skills that require coordination of both sides of the body and synchronization of the cerebral hemispheres. These include, for example, all sports activities, precise manual skills such writing, as well as, reading and counting skills. All this takes place in stages. Achieving skills from the first stage determines the development of fitness from the next level. Any deficit in the scope of the first three stages can affect the development of new skills. This ultimately reflects on the achievements at school and in further professional and personal life. After careful analysis symptoms from the emotional and social spheres appear to be secondary to deficits of sensory integration. During our research, the students gained knowledge and skills in the classroom of experience by learning biology, chemistry and physics with application sensory integration techniques. Sensory integration therapy aims to teach the child an adequate response to stimuli coming to him from both the outside world and the body. Thanks to properly selected exercises, a child can improve perception and interpretation skills, motor skills, coordination of movements, attention and concentration or self-awareness, as well as social and emotional functioning.

Keywords: autism spectrum disorder, science education, sensory integration, special educational needs

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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

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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

Abstract:

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

Abstract:

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

Abstract:

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

Abstract:

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

Abstract:

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

Abstract:

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

Procedia PDF Downloads 153

Authors: Summer Hassan, David Crossman

Abstract:

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

Procedia PDF Downloads 84