Search results for: patch-based similarity metric

Authors: Chenxi Zhang, Weizhong Qian, Fei Wei

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Gas solids multiphase flow is common in many engineering and environmental applications. Turbulence and multiphase flows are two of the most challenging topics in fluid mechanics, and when combined they pose a formidable challenge, even in the dilute dispersed regime. Dimensionless numbers are important in mechanics because their constancy can imply dynamic similarity between systems, despite possible differences in medium or scale. In the fluid mechanics literature, the Strouhal number is usually associated with the dimensionless shedding frequency of a von Karman wake; here we introduce this dimensionless number to investigate bubbling in gas solids fluidization. St=fA/U, which divides stroke frequency (f) and amplitude (A) by forward speed (U). The bubble behavior in a large two-dimensional bubbling fluidized bed (500mm×30mm×6000mm) is investigated. Our result indicates that propulsive efficiency is high and energy dissipation is low over a narrow range of St and usually within the interval 0.2Keywords: bubbles, Strouhal number, two-phase flow, energy dissipation

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Authors: Guoliang Lu, Changhou Lu, Xueyong Li

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In action recognition, previous studies have demonstrated the effectiveness of using multiple features to improve the recognition performance. We focus on two practical issues: i) most studies use a direct way of concatenating/accumulating multi features to evaluate the similarity between two actions. This way could be too strong since each kind of feature can include different dimensions, quantities, etc; ii) in many studies, the employed classification methods lack of a flexible and effective mechanism to add new feature(s) into classification. In this paper, we explore an unified scheme based on recently-proposed multi-dimensional dynamic time warping (MD-DTW). Experiments demonstrated the scheme's effectiveness of combining multi-feature and the flexibility of adding new feature(s) to increase the recognition performance. In addition, the explored scheme also provides us an open architecture for using new advanced classification methods in the future to enhance action recognition.

Keywords: action recognition, multi features, dynamic time warping, feature combination

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Authors: Kisik Song, Sungjoo Lee

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Recent years have seen an increasing number of patent disputes due to excessive competition in the global market and a reduced technology life-cycle; this has increased the risk of investment in technology development. While many global companies have started developing a methodology to identify promising technologies and assess for decisions, the existing methodology still has some limitations. Post hoc assessments of the new technology are not being performed, especially to determine whether the suggested technologies turned out to be promising. For example, in existing quantitative patent analysis, a patent’s citation information has served as an important metric for quality assessment, but this analysis cannot be applied to recently registered patents because such information accumulates over time. Therefore, we propose a new technology assessment model that can replace citation information and positively affect technological development based on post hoc analysis of the patents for promising technologies. Additionally, we collect customer reviews on a target technology to extract keywords that show the customers’ needs, and we determine how many keywords are covered in the new technology. Finally, we construct a portfolio (based on a technology assessment from patent information) and a customer-based marketability assessment (based on review data), and we use them to visualize the characteristics of the new technologies.

Keywords: technology assessment, patents, citation information, opinion mining

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Authors: Zabeehullah, Fahim Arif, Yawar Abbas

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Software Defined Network (SDN) is a next genera-tion networking model which simplifies the traditional network complexities and improve the utilization of constrained resources. Currently, most of the SDN based Internet of Things(IoT) environments use traditional network routing strategies which work on the basis of max or min metric value. However, IoT network heterogeneity, dynamic traffic flow and complexity demands intelligent and self-adaptive routing algorithms because traditional routing algorithms lack the self-adaptions, intelligence and efficient utilization of resources. To some extent, SDN, due its flexibility, and centralized control has managed the IoT complexity and heterogeneity but still Software Defined IoT (SDIoT) lacks intelligence. To address this challenge, we proposed a model called Deep Routing Strategy (DRS) which uses Deep Learning algorithm to perform routing in SDIoT intelligently and efficiently. Our model uses real-time traffic for training and learning. Results demonstrate that proposed model has achieved high accuracy and low packet loss rate during path selection. Proposed model has also outperformed benchmark routing algorithm (OSPF). Moreover, proposed model provided encouraging results during high dynamic traffic flow.

Keywords: SDN, IoT, DL, ML, DRS

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Authors: Maiss Razem

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The construction sector is considered a major consumer of materials that undergoes processes of extraction, processing, transportation, and maintaining when used in buildings. Several metrics have been devised to capture the environmental impact of the materials consumed during construction using lifecycle thinking. Rarely has the materiality of this sector been explored qualitatively and systemically. This paper aims to explore socio-cultural forces that drive the use of certain materials in the Jordanian construction industry, using practice theory as a heuristic method of analysis, more specifically Shove et al. three-element model. By conducting semi-structured interviews with architects, the results unravel contextually embedded routines when determining qualities of three materialities highlighted herein; stone, glass and spatial openness. The study highlights the inadequacy of only using efficiency as a quantitative metric of sustainable materials and argues for the need to link material consumption with socio-economic, cultural, and aesthetic driving forces. The operationalization of practice theory by tracing materials’ lifetimes as they integrate with competencies and meanings captures dynamic engagements through the analyzed routines of actors in the construction practice. This study can offer policymakers better-nuanced representation to green this sector beyond efficiency rhetoric and quantitative metrics.

Keywords: architects' practices, construction materials, Jordan, practice theory

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Authors: B. G. Patel, A. K. Desai, S. G. Shah

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The main of objective of this paper is to develop new measurement technique without touching the object. DIC is advance measurement technique use to measure displacement of particle with very high accuracy. This powerful innovative technique which is used to correlate two image segments to determine the similarity between them. For this study, nine geometrically similar beam specimens of different sizes with (steel fibers and glass fibers) and without fibers were tested under three-point bending in a closed loop servo-controlled machine with crack mouth opening displacement control with a rate of opening of 0.0005 mm/sec. Digital images were captured before loading (unreformed state) and at different instances of loading and were analyzed using correlation techniques to compute the surface displacements, crack opening and sliding displacements, load-point displacement, crack length and crack tip location. It was seen that the CMOD and vertical load-point displacement computed using DIC analysis matches well with those measured experimentally.

Keywords: Digital Image Correlation, fibres, self compacting concrete, size effect

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Authors: Ali Akbar Jabbari

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This study investigates the role of the prior acquired languages, Persian and English, concerning the acquisition of the third language (L3) French and German at the initial stages. The data were collected from two groups of L3 learners: 28 learners of L3 French and 21 learners of L3 German, in order to test the placement of the attributive adjectives and the noun adjuncts through a grammaticality judgment task and an element rearrangement task. The aim of the study was to investigate whether any of the models proposed in the L3 acquisition could account for the case of the present study. The results of the analysis revealed that the learners of L3 German and French were both affected by the typological similarity of the previous languages. The outperformance of the German learners is an indication of the facilitative effect of L2 English (which is typologically more similar to the German than that of French). English had also a non-facilitative role in the acquisition of French and this is proved in the lower performance of the French learners. This study provided evidence for the TPM as the most accepted model of L3 acquisition.

Keywords: cross-linguistic influence, multilingualism, third language acquisition, transfer

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Authors: Monika Soni, Shovonlal Bhowmick, Chandra Bhattacharya, Jitendra Sharma, Prafulla Dutta, Jagadish Mahanta

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Aedes aegypti and Aedes albopictus are considered as two major vectors to transmit dengue virus. In North-east India, two states viz. Assam and Arunachal Pradesh are known to be high endemic zone for dengue and Chikungunya viral infection. The taxonomical classification of medically important vectors are important for mapping of actual evolutionary trends and epidemiological studies. However, misidentification of mosquito species in field-collected mosquito specimens could have a negative impact which may affect vector-borne disease control policy. DNA barcoding is a prominent method to record available species, differentiate from new addition and change of population structure. In this study, a combined approach of a morphological and molecular technique of DNA barcoding was adopted to explore sequence variation in mitochondrial cytochrome c oxidase subunit I (COI) gene within dengue vectors. The study has revealed the map distribution of the dengue vector from two states i.e. Assam and Arunachal Pradesh, India. Approximate five hundred mosquito specimens were collected from different parts of two states, and their morphological features were compared with the taxonomic keys. The analysis of detailed taxonomic study revealed identification of two species Aedes aegypti and Aedes albopictus. The species aegypti comprised of 66.6% of the specimen and represented as dominant dengue vector species. The sequences obtained through standard DNA barcoding protocol were compared with public databases, viz. GenBank and BOLD. The sequences of all Aedes albopictus have shown 100% similarity whereas sequence of Aedes aegypti has shown 99.77 - 100% similarity of COI gene with that of different geographically located same species based on BOLD database search. From dengue prevalent different geographical regions fifty-nine sequences were retrieved from NCBI and BOLD databases of the same and related taxa to determine the evolutionary distance model based on the phylogenetic analysis. Neighbor-Joining (NJ) and Maximum Likelihood (ML) phylogenetic tree was constructed in MEGA6.06 software with 1000 bootstrap replicates using Kimura-2-Parameter model. Data were analyzed for sequence divergence and found that intraspecific divergence ranged from 0.0 to 2.0% and interspecific divergence ranged from 11.0 to 12.0%. The transitional and transversional substitutions were tested individually. The sequences were deposited in NCBI: GenBank database. This observation claimed the first DNA barcoding analysis of Aedes mosquitoes from North-eastern states in India and also confirmed the range expansion of two important mosquito species. Overall, this study insight into the molecular ecology of the dengue vectors from North-eastern India which will enhance the understanding to improve the existing entomological surveillance and vector incrimination program.

Keywords: COI, dengue vectors, DNA barcoding, molecular identification, North-east India, phylogenetics

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Authors: Mikyung Bak

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The dokkaebi, a beloved icon of Korean culture, is represented as an elf, goblin, monster, dwarf, or any similar creature in different media, such as animated shows, comics, soap operas, and movies. It is often described as a mythical creature with a horn or horns and long teeth, wearing tiger-skin pants or a grass skirt, and carrying a magic stick. Many Korean researchers agree on the similarity of the image of the Korean dokkaebi with that of the Japanese oni, a view that is regard as negative from an anti-colonial or nationalistic standpoint. They cite such similarity between the two mythical creatures as evidence that Japanese colonialism persists in Korea. The debate on the originality of dokkaebi’s visual representation is an issue that must be addressed urgently. This research demonstrates through a diagram the plurality of interpretations of dokkaebi’s visual representations in what are considered ‘authentic’ images of dokkaebi in Korean art and culture. This diagram presents the opinions of four major groups in the debate, namely, the scholars of Korean literature and folklore, art historians, authors, and artists. It also shows the creation of new dokkaebi visual representations in popular media, including those influenced by the debate. The diagram further proves that dokkaebi’s representations varied, which include the typical persons or invisible characters found in Korean literature, original Korean folk characters in traditional art, and even universal spirit characters. They are also visually represented by completely new creatures as well as oni-based mythical beings and the actual oni itself. The earlier dokkaebi representations were driven by the creation of a national ideology or national cultural paradigm and, thus, were more uniform and protected. In contrast, the more recent representations are influenced by the Korean industrial strategy of ‘cultural economics,’ which is concerned with the international rather than the domestic market. This recent Korean cultural strategy emphasizes diversity and commonality with the global culture rather than originality and locality. It employs traditional cultural resources to construct a global image. Consequently, dokkaebi’s recent representations have become more common and diverse, thereby incorporating even oni’s characteristics. This argument has rendered the grounds of the debate irrelevant. The dokkaebi has been used recently for tourist marketing purposes, particularly in revitalizing interest in regions considered the cradle of various traditional dokkaebi tales. These campaign strategies include the Jeju-do Dokkaebi Park, Koksung Dokkaebi Land, as well as the Taebaek and Sokri-san Dokkaebi Festivals. Almost dokkaebi characters are identical to the Japanese oni in tourist marketing. However, the pursuit for dokkaebi’s authentic visual representation is less interesting and fruitful than the appreciation of the entire spectrum of dokkaebi images that have been created. Thus, scholars and stakeholders must not exclude the possibilities for a variety of potentials within the visual culture. The same sentiment applies to traditional art and craft. This study aims to contribute to a new visualization of the dokkaebi that embraces the possibilities of both folk craft and art, which continue to be uncovered by diverse and careful researchers in a still-developing field.

Keywords: Dokkaebi, post-colonial period, representation, tourist marketing

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Authors: Jarbas Lopes Cardoso Jr., Frederic Andres, Alexandre Guitton, Asanee Kawtrakul, Silvio E. Barbin

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The important objective of the CyberBrain Mass Agriculture Alarm Acquisition and Analysis (CBMa4) project is to minimize the impacts of diseases and disasters on rice cultivation. For example, early detection of insects will reduce the volume of insecticides that is applied to the rice fields through the use of CBMa4 platform. In order to reach this goal, two major factors need to be considered: (1) the social network of smart farmers; and (2) the warning data alarm acquisition and analysis component. This paper outlines the process for collecting the warning and improving the decision-making result to the warning. It involves two sub-processes: the warning collection and the understanding enrichment. Human sensors combine basic suitable data processing techniques in order to extract warning related semantic according to collective intelligence. We identify each warning by a semantic content called 'warncons' with multimedia metaphors and metadata related to these metaphors. It is important to describe the metric to measuring the relation among warncons. With this knowledge, a collective intelligence-based decision-making approach determines the action(s) to be launched regarding one or a set of warncons.

Keywords: agricultural engineering, warning systems, social network services, context awareness

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Authors: Monika Chuchro

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This paper presents a preliminary attempt to apply classification of time series using meta-clusters in order to improve the quality of regression models. In this case, clustering was performed as a method to obtain a subgroups of time series data with normal distribution from inflow into waste water treatment plant data which Composed of several groups differing by mean value. Two simple algorithms: K-mean and EM were chosen as a clustering method. The rand index was used to measure the similarity. After simple meta-clustering, regression model was performed for each subgroups. The final model was a sum of subgroups models. The quality of obtained model was compared with the regression model made using the same explanatory variables but with no clustering of data. Results were compared by determination coefficient (R2), measure of prediction accuracy mean absolute percentage error (MAPE) and comparison on linear chart. Preliminary results allows to foresee the potential of the presented technique.

Keywords: clustering, data analysis, data mining, predictive models

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Authors: Sumit Rahangdale

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This research paper is a comparative study of two BRTS cities of Madhya Pradesh (INDIA), Bhopal and Indore. Indore is the largest and most populous city of Madhya Pradesh, with heavy traffic, while Bhopal though being the capital of Madhya Pradesh is comparatively less developed and shows less traffic The cities show similarity in case of peri-urban nature, but variation is observed in transportation fare, where Indore has been able to reduce it but Bhopal couldn’t, one of the reason for it is the para-transit services. Indore can be considered as a successful model due to the low fares and can be implemented in other parts of the city. The research paper tries to identify relation of para-transit services with the peri-urban connectivity and provide a solution for the Bhopal case study.

Keywords: demand-supply-fare relationship, mobility and accessibility, paratransit, peri-urban connectivity

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Authors: Shirin Ghasemi

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The structure of Asadi’s poem in Garshasb-Nameh coordinates with the structure of human psyche based on Jung’s perspective. The poetic stories of Asadi in Garshasb-Nameh is contrasted to human psyche according to Jung’s view in psychology which indicated the similarity of poetic structure of stories of Garshasb-Nameh to analytical psychology of Jung. In fact, by studying the stories of this collection the reader travels with him and finds it consistent with the human psyche. To demonstrate this, the story of Jamshid marriage with Kuhrang’s daughter and the story of Garshasb marriage with King’s daughter are selected. These two stories illustrate the poetic structure and the human psyche based on Jung’s analytical psychology perspective.

Keywords: Asadi Tusi, Garshasb-Nameh, Jung, analytical psychology

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Authors: N. Samarinas, C. Evangelides, C. Vrekos

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The aim of this paper is the comparison of three different methods, in order to produce fuzzy tolerance relations for rainfall data classification. More specifically, the three methods are correlation coefficient, cosine amplitude and max-min method. The data were obtained from seven rainfall stations in the region of central Greece and refers to 20-year time series of monthly rainfall height average. Three methods were used to express these data as a fuzzy relation. This specific fuzzy tolerance relation is reformed into an equivalence relation with max-min composition for all three methods. From the equivalence relation, the rainfall stations were categorized and classified according to the degree of confidence. The classification shows the similarities among the rainfall stations. Stations with high similarity can be utilized in water resource management scenarios interchangeably or to augment data from one to another. Due to the complexity of calculations, it is important to find out which of the methods is computationally simpler and needs fewer compositions in order to give reliable results.

Keywords: classification, fuzzy logic, tolerance relations, rainfall data

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Authors: Masood Beheshtirad

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Identification of regions having potential for landslide occurrence is one of the basic measures in natural resources management. Different landslide hazard mapping models are proposed based on the environmental condition and goals. In this research landslide hazard map using multiple regression model were provided and applicability of this model is investigated in Baghdasht watershed. Dependent variable is landslide inventory map and independent variables consist of information layers as Geology, slope, aspect, distance from river, distance from road, fault and land use. For doing this, existing landslides have been identified and an inventory map made. The landslide hazard map is based on the multiple regression provided. The level of similarity potential hazard classes and figures of this model were compared with the landslide inventory map in the SPSS environments. Results of research showed that there is a significant correlation between the potential hazard classes and figures with area of the landslides. The multiple regression model is suitable for application in the Baghdasht Watershed.

Keywords: landslide, mapping, multiple model, regression

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Authors: Eran Viezel

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Critical biblical scholarship emerged in the early modern period, yet scholars frequently search for precursors to it among medieval commentators who adopted critical positions—and many mention Abraham Ibn Ezra (Spain–England, 1089–1164/7) in this context. Indeed, in several places, Ibn Ezra claims that there are verses in the Torah that were added to it after the time of Moses; and some major thinkers and scholars in the early modern period (for example, Baruch Spinoza) were aware of these remarks and influenced by them. However, Ibn Ezra’s belief that the Torah includes verses added at a later time is not based on the considerations that led the founders of critical biblical scholarship to their conclusion that Moses did not write the Torah. Ibn Ezra’s positions on the question of the Torah’s authorship are an example of the fact that similarity in conclusions and even in interpretive methodology should not obscure the different interpretive and attitudinal points of departure that distinguish traditional biblical interpretation from a critical biblical scholarship. Ultimately, a chasm exists between the views of Ibn Ezra and those of critical thinkers such as Spinoza.

Keywords: hebrew bible, Abraham Ibn Ezra, exegesis, biblical scholarship

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Authors: Fátima Gonçalves, Joaquim Brigas, Jorge Gonçalves

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As a way of managing the increasing volume and complexity of information that circulates in the present time, graphical representations are increasingly used, which add meaning to the information presented in communication media, through an efficient communication design. The visual culture itself, driven by technological evolution, has been redefining the forms of communication, so that contemporary visual communication represents a major impact on society. This article presents the results and respective comparative analysis of four publications in the Iberian press, focusing on the formal aspects of newspapers and the space they dedicate to the various communication elements. Two Portuguese newspapers and two Spanish newspapers were selected for this purpose. The findings indicated that the newspapers show a similarity in the use of graphic solutions, which corroborate a visual trend in communication design. The results also reveal that Spanish newspapers are more meticulous with graphic consistency. This study intended to contribute to improving knowledge of the Iberian generalist press.

Keywords: communication design, graphic resources, Iberian press, visual journalism

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Authors: Yohannes Yirga, Daniel Tesfay

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The convective heat and mass transfer in nanofluid flow through a porous media due to a permeable stretching sheet with magnetic field, viscous dissipation, and chemical reaction and Soret effects are numerically investigated. Two types of nanofluids, namely Cu-water and Ag-water were studied. The governing boundary layer equations are formulated and reduced to a set of ordinary differential equations using similarity transformations and then solved numerically using the Keller box method. Numerical results are obtained for the skin friction coefficient, Nusselt number and Sherwood number as well as for the velocity, temperature and concentration profiles for selected values of the governing parameters. Excellent validation of the present numerical results has been achieved with the earlier linearly stretching sheet problems in the literature.

Keywords: heat and mass transfer, magnetohydrodynamics, nanofluid, fluid dynamics

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Authors: Rasaq A. Kareem, Sulyman O. Salawu

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The study of Soret and Dufour effect on variable viscosity and thermal conductivity of an inclined magnetic field with dissipation in non-Darcy porous medium over a continuously stretching sheet for power-law variation in the sheet temperature and concentration are investigated. The viscosity of the fluid flow and thermal conductivity are considered to vary as a function of temperature. The local similarity solutions for different values of the physical parameters are presented for velocity, temperature and concentration. The result shows that variational increase in the values of Soret and Dufour parameters increase the temperature and concentration distribution. Finally, the effects of skin friction, Nusselt and Sherwood numbers which are of physical and engineering interest are considered and discussed.

Keywords: Dufour, non-Darcy Flow, Soret, thermal conductivity, variable viscosity

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Authors: Baljeet Singh Saharan

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Bioremediation is the demand of the day. Tannery and textile effluents/waste waters have lots of pollution due to presence of hexavalent Chromium. Methodologies used in the present investigations include isolation, cultivation and purification of bacterial strain. Further characterization techniques and 16S rRNA sequencing were performed. Efficient bacterial strain capable of reducing hexavalent chromium was obtained. The strain can be used for bioremediation of industrial effluents containing hexavalent Cr. A gram negative, rod shaped and yellowish pigment producing bacterial strain from tannery effluent was isolated using nutrient agar. The 16S rRNA gene sequence similarity indicated that isolate SA13A is associated with genus Luteimonas (99%). This isolate has been found to reduce 100% of hexavalent chromium Cr (VI) (100 mg L-1) 100% in 16 h. Growth conditions were optimized for Cr (VI) reduction. Maximum reduction was observed at a temperature of 37 °C and pH 8.0. Additionally, Luteimonas aestuarii SA13A showed resistance against various heavy metals like Cr+6, Cr+3, Cu+2, Zn+2, Co+2, Ni+2 and Cd+2 . Hence, Luteimonas aestuarii SA13A could be used as potent Cr (VI) reducing strain as well as significant bioremediator in heavy metal contaminated sites.

Keywords: bioremediation, chromium, eco-friendly, heavy metals

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Authors: Natasha Petrovska, Mirjana Pavlovic, Maria M. Larrondo-Petrie

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Neural stem cells (NSCs) are multi-potent, self-renewing cells that generate new neurons. Three subtypes of NSCs can be separated regarding the stages of NSC lineage: quiescent neural stem cells (qNSCs), activated neural stem cells (aNSCs) and neural progenitor cells (NPCs), but their gene expression signatures are not utterly understood yet. Single-cell examinations have started to elucidate the complex structure of NSC populations. Nevertheless, there is a lack of thorough molecular interpretation of the NSC lineage heterogeneity and an increasing need for tools to analyze and improve the efficiency and correctness of single-cell sequencing data. Feature selection and ordering can identify and classify the gene expression signatures of these subtypes and can discover novel subpopulations during the NSCs activation and differentiation processes. The aim here is to review the implementation of the feature selection technique on NSC subtypes and the classification techniques that have been used for the identification of gene expression signatures.

Keywords: feature selection, feature similarity, neural stem cells, genes, feature selection methods

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Authors: Pedro Paulo Galindo Francisco, Eli Dhadad Junior

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This study addresses the Meese-Rogoff Puzzle by introducing the latest machine learning techniques as alternatives for predicting the exchange rates. Using RMSE as a comparison metric, Meese and Rogoff discovered that economic models are unable to outperform the random walk model as short-term exchange rate predictors. Decades after this study, no statistical prediction technique has proven effective in overcoming this obstacle; although there were positive results, they did not apply to all currencies and defined periods. Recent advancements in artificial intelligence technologies have paved the way for a new approach to exchange rate prediction. Leveraging this technology, we applied five machine learning techniques to attempt to overcome the Meese-Rogoff puzzle. We considered daily data for the real, yen, British pound, euro, and Chinese yuan against the US dollar over a time horizon from 2010 to 2023. Our results showed that none of the presented techniques were able to produce an RMSE lower than the Random Walk model. However, the performance of some models, particularly LSTM and N-BEATS were able to outperform the ARIMA model. The results also suggest that machine learning models have untapped potential and could represent an effective long-term possibility for overcoming the Meese-Rogoff puzzle.

Keywords: exchage rate, prediction, machine learning, deep learning

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Authors: Phakamas Rachamontree, Theerawut Phusantisampan, Natthakorn Woravutthikul, Peerapong Pornwongthong, Malinee Sriariyanun

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A total of 115 yeast strains isolated from local cassava processing wastes were measured for crude protein content. Among these strains, the strain MSY-2 possessed the highest protein concentration (>3.5 mg protein/mL). By using molecular identification tools, it was identified to be a strain of Pichia kudriavzevii based on similarity of D1/D2 domain of 26S rDNA region. In this study, to optimize the protein production by MSY-2 strain, Response Surface Methodology (RSM) was applied. The tested parameters were the carbon content, nitrogen content, and incubation time. Here, the value of regression coefficient (R2) = 0.7194 could be explained by the model, which is high to support the significance of the model. Under the optimal condition, the protein content was produced up to 3.77 g per L of the culture and MSY-2 strain contain 66.8 g protein per 100 g of cell dry weight. These results revealed the plausibility of applying the novel strain of yeast in single-cell protein production.

Keywords: single cell protein, response surface methodology, yeast, cassava processing waste

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Authors: Alamgir Naushad, Ghulam Abbas, Shehzad Ali Shah, Ziaul Haq Abbas

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Unlike conventional networks, it is particularly challenging to manage resources efficiently in Wireless Sensor Networks (WSNs) due to their inherent characteristics, such as dynamic network topology and limited bandwidth and battery power. To ensure energy efficiency, this paper presents a routing protocol for WSNs, namely, Enhanced Hybrid Multipath Routing (EHMR), which employs hierarchical clustering and proposes a next hop selection mechanism between nodes according to a maximum residual energy metric together with a minimum hop count. Load-balancing of data traffic over multiple paths is achieved for a better packet delivery ratio and low latency rate. Reliability is ensured in terms of higher data rate and lower end-to-end delay. EHMR also enhances the fast-failure recovery mechanism to recover a failed path. Simulation results demonstrate that EHMR achieves a higher packet delivery ratio, reduced energy consumption per-packet delivery, lower end-to-end latency, and reduced effect of data rate on packet delivery ratio when compared with eminent WSN routing protocols.

Keywords: energy efficiency, load-balancing, hierarchical clustering, multipath routing, wireless sensor networks

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Authors: Oumaima Lahmar

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This paper aims to define a structured topography for finance researchers seeking to navigate the body of knowledge in their extrapolation of finance phenomena. To make sense of the body of knowledge in finance, a probabilistic topic modeling approach is applied on 6000 abstracts of academic articles published in three top journals in finance between 1976 and 2020. This approach combines both machine learning techniques and natural language processing to statistically identify the conjunctions between research articles and their shared topics described each by relevant keywords. The topic modeling analysis reveals 35 coherent topics that can well depict finance literature and provide a comprehensive structure for the ongoing research themes. Comparing the extracted topics to the Journal of Economic Literature (JEL) classification system, a significant similarity was highlighted between the characterizing keywords. On the other hand, we identify other topics that do not match the JEL classification despite being relevant in the finance literature.

Keywords: finance literature, textual analysis, topic modeling, perplexity

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Authors: Liana Lortkipanidze, Nino Amirezashvili, Nino Javashvili

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The purpose of the grammatical dictionary is to provide information on the morphological and syntactic characteristics of the basic word in the dictionary entry. The electronic grammatical dictionaries are used as a tool of automated morphological analysis for texts processing. The Georgian Grammatical Dictionary should contain grammatical information for each word: part of speech, type of declension/conjugation, grammatical forms of the word (paradigm), alternative variants of basic word/lemma. In this paper, we present the system for compiling the Georgian Grammatical Dictionary automatically. We propose dictionary-based methods for extending grammatical lexicons. The input lexicon contains only a few number of words with identical grammatical features. The extension is based on similarity measures between features of words; more precisely, we add words to the extended lexicons, which are similar to those, which are already in the grammatical dictionary. Our dictionaries are corpora-based, and for the compiling, we introduce the method for lemmatization of unknown words, i.e., words of which neither full form nor lemma is in the grammatical dictionary.

Keywords: acquisition of lexicon, Georgian grammatical dictionary, lemmatization rules, morphological processor

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Authors: Abeer M. Algeblawi

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Fifteen isolates of Agrobacterium tumefaciens were obtained from crown gall samples collected from six locations (Tripoli, Alzahra, Ain-Zara, Alzawia, Alazezia in Libya) from Grape (Vitis vinifera L.), Pear (Pyrus communis L.), Peach (Prunus persica L.) and Alexandria in Egypt from Guava (Psidium guajava L.) trees, Artichoke (Cynara cardunculus L.) and Sugar beet (Beta vulgaris L.). Total DNA was extracted from the eight isolates as well as the identification of six isolates used into Polymerase Chain Reaction (PCR) analysis and Random Amplified Polymorphic DNA (RAPD) technique were used. High similarity (55.5%) was observed among the eight A. tumefaciens isolates (Agro1, Agro2, Agro3, Agro4, Agro5, Agro6, Agro7, and Agro8). The PCR amplification products were resulting from the use of two specific primers (virD2A-virD2C). Analysis induction six isolates of A. tumefaciens obtained from different hosts. A visible band was specific to A. tumefaciens of (220 bp, 224 bp) and 338 bp produced with total DNA extracted from bacterial cells.

Keywords: Agrobacterium tumefaciens, crown gall, identification, molecular characterization, PCR, RAPD

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Authors: Tanish Mittal, Preyansh Agrawal, Esha Pahwa, Aarya Makwana

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Multi-Scale and U-shaped Networks are widely used in various image restoration problems, including deblurring. Keeping in mind the wide range of applications, we present a comparison of these architectures and their effects on image deblurring. We also introduce a new block called as NFResblock. It consists of a Fast Fourier Transformation layer and a series of modified Non-Linear Activation Free Blocks. Based on these architectures and additions, we introduce NFResnet and NFResnet+, which are modified multi-scale and U-Net architectures, respectively. We also use three differ-ent loss functions to train these architectures: Charbonnier Loss, Edge Loss, and Frequency Reconstruction Loss. Extensive experiments on the Deep Video Deblurring dataset, along with ablation studies for each component, have been presented in this paper. The proposed architectures achieve a considerable increase in Peak Signal to Noise (PSNR) ratio and Structural Similarity Index (SSIM) value.

Keywords: multi-scale, Unet, deblurring, FFT, resblock, NAF-block, nfresnet, charbonnier, edge, frequency reconstruction

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Authors: A. Senthil Kumar, V. Murali Bhaskaran

Abstract:

In the information technology ground, people are using various tools and software for their official use and personal reasons. Nowadays, people are worrying to choose data accessing and extraction tools at the time of buying and selling their products. In addition, worry about various quality factors such as price, durability, color, size, and availability of the product. The main purpose of the research study is to find solutions to these unsolved existing problems. The proposed algorithm is a Multidirectional Rank Prediction (MDRP) decision making algorithm in order to take an effective strategic decision at all the levels of data extraction, uses a real time textile dataset and analyzes the results. Finally, the results are obtained and compared with the existing measurement methods such as PCC, SLCF, and VSS. The result accuracy is higher than the existing rank prediction methods.

Keywords: Knowledge Discovery in Database (KDD), Multidirectional Rank Prediction (MDRP), Pearson’s Correlation Coefficient (PCC), VSS (Vector Space Similarity)

Procedia PDF Downloads 288

Authors: Aadhya Kaul, Gautam Manocha, Preeti Nagrath

Abstract:

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

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

Procedia PDF Downloads 164