Search results for: grey clustering

Authors: Krystian Chachula, Robert Nowak

Abstract:

Manufacturing home-made explosives and synthetic drugs is an increasing problem in Europe. To combat that, a data fusion system is proposed for the detection and localization of production sites in urban environments. The data consists of measurements of properties of wastewater performed by various sensors installed in a sewage network. A four-stage fusion strategy allows detecting sources of waste products from known chemical reactions. First, suspicious measurements are used to compute the amount and position of discharged compounds. Then, this information is propagated through the sewage network to account for missing sensors. The next step is clustering and the formation of tracks. Eventually, tracks are used to reconstruct discharge events. Sensor measurements are simulated by a subsystem based on real-world data. In this paper, different discharge scenarios are considered to show how the parameters of used algorithms affect the effectiveness of the proposed system. This research is a part of the SYSTEM project (SYnergy of integrated Sensors and Technologies for urban sEcured environMent).

Keywords: continuous monitoring, information fusion and sensors, internet of things, multisensor fusion

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Authors: Camilo Cerda Sarabia, Fernanda Bravo Cornejo, Diego Santibanez Oyarce, Hugo Osses Prado, Esteban Gómez Terán, Belén Diaz Diaz, Raúl Caulier-Cisterna, Jorge Vergara-Quezada, Ana Moya-Beltrán

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Antimicrobial resistance (AMR) represents a significant and rapidly escalating global health threat. Projections estimate that by 2050, AMR infections could claim up to 10 million lives annually. Respiratory infections, in particular, pose a severe risk not only to individual patients but also to the broader public health system. Despite the alarming rise in resistant respiratory infections, AMR within the lung microbiome (microbial community) remains underexplored and poorly characterized. The lungs, as a complex and dynamic microbial environment, host diverse communities of microorganisms whose interactions and resistance mechanisms are not fully understood. Unlike studies that focus on individual genomes, analyzing the entire microbiome provides a comprehensive perspective on microbial interactions, resistance gene transfer, and community dynamics, which are crucial for understanding AMR. However, this holistic approach introduces significant computational challenges and exposes the limitations of traditional analytical methods such as the difficulty of identifying the AMR. Machine learning has emerged as a powerful tool to overcome these challenges, offering the ability to analyze complex genomic data and uncover novel insights into AMR that might be overlooked by conventional approaches. This study investigates microbial resistance within the lung microbiome using unsupervised machine learning approaches to uncover resistance patterns and potential clinical associations. it downloaded and selected lung microbiome data from HumanMetagenomeDB based on metadata characteristics such as relevant clinical information, patient demographics, environmental factors, and sample collection methods. The metadata was further complemented by details on antibiotic usage, disease status, and other relevant descriptions. The sequencing data underwent stringent quality control, followed by a functional profiling focus on identifying resistance genes through specialized databases like Antibiotic Resistance Database (CARD) which contains sequences of AMR gene sequence and resistance profiles. Subsequent analyses employed unsupervised machine learning techniques to unravel the structure and diversity of resistomes in the microbial community. Some of the methods employed were clustering methods such as K-Means and Hierarchical Clustering enabled the identification of sample groups based on their resistance gene profiles. The work was implemented in python, leveraging a range of libraries such as biopython for biological sequence manipulation, NumPy for numerical operations, Scikit-learn for machine learning, Matplotlib for data visualization and Pandas for data manipulation. The findings from this study provide insights into the distribution and dynamics of antimicrobial resistance within the lung microbiome. By leveraging unsupervised machine learning, we identified novel resistance patterns and potential drivers within the microbial community.

Keywords: antibiotic resistance, microbial community, unsupervised machine learning., sequences of AMR gene

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Authors: Annalakshmi G., Sakthivel Murugan S.

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This paper presents a local directional encoded derivative binary pattern (LDEDBP) feature extraction method that can be applied for the classification of submarine coral reef images. The classification of coral reef images using texture features is difficult due to the dissimilarities in class samples. In coral reef image classification, texture features are extracted using the proposed method called local directional encoded derivative binary pattern (LDEDBP). The proposed approach extracts the complete structural arrangement of the local region using local binary batten (LBP) and also extracts the edge information using local directional pattern (LDP) from the edge response available in a particular region, thereby achieving extra discriminative feature value. Typically the LDP extracts the edge details in all eight directions. The process of integrating edge responses along with the local binary pattern achieves a more robust texture descriptor than the other descriptors used in texture feature extraction methods. Finally, the proposed technique is applied to an extreme learning machine (ELM) method with a meta-heuristic algorithm known as weighted distance grey wolf optimizer (GWO) to optimize the input weight and biases of single-hidden-layer feed-forward neural networks (SLFN). In the empirical results, ELM-WDGWO demonstrated their better performance in terms of accuracy on all coral datasets, namely RSMAS, EILAT, EILAT2, and MLC, compared with other state-of-the-art algorithms. The proposed method achieves the highest overall classification accuracy of 94% compared to the other state of art methods.

Keywords: feature extraction, local directional pattern, ELM classifier, GWO optimization

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Authors: Haifa Ben Saber, Mourad Elloumi

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In a number of domains, like in DNA microarray data analysis, we need to cluster simultaneously rows (genes) and columns (conditions) of a data matrix to identify groups of constant rows with a group of columns. This kind of clustering is called biclustering. Biclustering algorithms are extensively used in DNA microarray data analysis. More effective biclustering algorithms are highly desirable and needed. We introduce a new algorithm called, Enumerative tree (EnumTree) for biclustering of binary microarray data. is an algorithm adopting the approach of enumerating biclusters. This algorithm extracts all biclusters consistent good quality. The main idea of ​​EnumLat is the construction of a new tree structure to represent adequately different biclusters discovered during the process of enumeration. This algorithm adopts the strategy of all biclusters at a time. The performance of the proposed algorithm is assessed using both synthetic and real DNA micryarray data, our algorithm outperforms other biclustering algorithms for binary microarray data. Biclusters with different numbers of rows. Moreover, we test the biological significance using a gene annotation web tool to show that our proposed method is able to produce biologically relevent biclusters.

Keywords: DNA microarray, biclustering, gene expression data, tree, datamining.

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Authors: Sunday Oladele, Joseph Oluwagbeja Simeon

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Subsurface exploration, integrating methods of geotechnics and geophysics, of a planned construction site in the coastal city of Lagos, Nigeria has been carried out with the aim of characterizing the soil properties and their implication for the proposed infrastructural development. Six Standard Penetration Tests (SPT), fourteen Dutch Cone Penetrometer Tests (DCPT) and 2D Electrical Resistivity Imaging employing Dipole-dipole and Pole-dipole arrays were implemented on the site. The topsoil (0 - 4m) consists of highly compacted sandy lateritic clay(10 to 5595Ωm) to 1.25m in some parts and dense sand in other parts to 5.50m depth. This topsoil was characterized as a material of very high shear strength (≤ 150kg/m2) and allowable bearing pressure value of 54kN/m2 to 85kN/m2 and a safety factor of 2.5. Soft amorphous peat/peaty clay (0.1 to 11.4Ωm), 3-6m thick, underlays the lateritic clay to about 18m depth. Grey, medium dense to very dense sand (0.37 to 2387Ωm) with occasional gravels underlies the peaty clay down to 30m depth. Within this layer, the freshwater bearing zones are characterized by high resistivity response (83 to 2387Ωm), while the clayey sand/saline water intruded sand produced subdued resistivity output (0.37 to 40Ωm). The overall ground-bearing pressure for the proposed structure would be 225kN/m2. Bored/cast-in-place pile at 18.00m depth with any of these diameters and respective safe working loads 600mm/1,140KN, 800mm/2,010KN and 1000mm/3,150KN is recommended for the proposed multi-story structure.

Keywords: subsurface exploration, Geotechnical properties, resistivity imaging, pile

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Authors: William Huang

Abstract:

Retinal prostheses are currently limited to low resolution grayscale images that lack color and spatial information. This study develops a novel real-time image optimization framework and tools to encode maximum information to the prostheses which are constrained by the number of electrodes. One key idea is to localize main objects in images while reducing unnecessary background noise through region-contrast saliency maps. A novel color depth mapping technique was developed through MiniBatchKmeans clustering and color space selection. The resulting image was downsampled using bicubic interpolation to reduce image size while preserving color quality. In comparison to current schemes, the proposed framework demonstrated better visual quality in tested images. The use of the region-contrast saliency map showed improvements in efficacy up to 30%. Finally, the computational speed of this algorithm is less than 380 ms on tested cases, making real-time retinal prostheses feasible.

Keywords: retinal implants, virtual processing unit, computer vision, saliency maps, color quantization

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Authors: Ali D. El-mehdawi, Ibrahim E. Elkanouni

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Thick pre-Upper Cretaceous sandstones, sandstones intercalated with red/black shale or quarzitic sandstones, traditionally known to range in age from Cambrian to Early Cretaceous, mostly overlie the subsurface basement rocks of the Sirt Basin of Libya. These sediments known as Nubian, Sarir, Amal or Cambro-Ordovician sandstones. They are usually barren of any age datable palynomorphs and microfossils and represent the main hydrocarbon reservoirs in the basin. As a part of an ongoing regional project concerned with revision and updating of the stratigraphic nomenclature of the Sirt Basin and adjacent areas, sixteen core and ditch cutting samples from four wells penetrating the known Cambro-Ordovician sediments in the central and eastern parts of the basin were examined palynologicaly to investigate its age and the depositional paleoenvironment. The samples proved to be barren or yielded rare palynomorph assemblage, which dominated by dark grey to black small and large-sized sphaeromorph acritarchs assemblage of leiosphaerid types. The dominated species are Kildinosphaera chagrinata, K. cf. chagrinata, Kildinella ripheica, Kilinella timanica, Leiosphaeridia asperata and Leiosphaeridia spp. These leiosphaerides assemblage are comparable to those have been reported from the Late Precambrian, late Riphean age in Cyrenaica Platform, NE Libya, and would indicated shallow marine depositional environment. The age assignment suggests that this interval most probably equates to Mourizide, Bir Bayai and Wadi alHayt formations known in the Murzuq, Kufrah and Cyrenaica areas, respectively. This study proves the presence of Precambrian sediments in Jaghbub high and Amal Platform in the eastern part of Sirt Basin and probably in Maradah Trough and Aj Jahamah/Zoltun Platform northwestern part of the Sirt Basin.

Keywords: palynology, leiosphaerides, precambrian, sirt basin, libya

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Authors: Abder-Rahman Ali, Antoine Vacavant, Manuel Grand-Brochier, Adélaïde Albouy-Kissi, Jean-Yves Boire

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In this paper, we present a new segmentation approach for liver lesions in regions of interest within MRI (Magnetic Resonance Imaging). This approach, based on a two-cluster Fuzzy C-Means methodology, considers the parameter variable compactness to handle uncertainty. Fine boundaries are detected by a local recursive merging of ambiguous pixels with a sequential forward floating selection with Zernike moments. The method has been tested on both synthetic and real images. When applied on synthetic images, the proposed approach provides good performance, segmentations obtained are accurate, their shape is consistent with the ground truth, and the extracted information is reliable. The results obtained on MR images confirm such observations. Our approach allows, even for difficult cases of MR images, to extract a segmentation with good performance in terms of accuracy and shape, which implies that the geometry of the tumor is preserved for further clinical activities (such as automatic extraction of pharmaco-kinetics properties, lesion characterization, etc).

Keywords: defuzzification, floating search, fuzzy clustering, Zernike moments

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Authors: Zahid Ahmad, Nosheen Imran, Nauman Ali, Farah Amir

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The objective of the study is to forecast the US Dollar and Pak Rupee exchange rate by using time series models. For this purpose, daily exchange rates of US and Pakistan for the period of January 01, 2007 - June 2, 2017, are employed. The data set is divided into in sample and out of sample data set where in-sample data are used to estimate as well as forecast the models, whereas out-of-sample data set is exercised to forecast the exchange rate. The ADF test and PP test are used to make the time series stationary. To forecast the exchange rate ARIMA model and GARCH model are applied. Among the different Autoregressive Integrated Moving Average (ARIMA) models best model is selected on the basis of selection criteria. Due to the volatility clustering and ARCH effect the GARCH (1, 1) is also applied. Results of analysis showed that ARIMA (0, 1, 1 ) and GARCH (1, 1) are the most suitable models to forecast the future exchange rate. Further the GARCH (1,1) model provided the volatility with non-constant conditional variance in the exchange rate with good forecasting performance. This study is very useful for researchers, policymakers, and businesses for making decisions through accurate and timely forecasting of the exchange rate and helps them in devising their policies.

Keywords: exchange rate, ARIMA, GARCH, PAK/USD

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Authors: Sireesha Tamada, Debraj Bhattacharjee, Pranab K. Dan, Prabha Bhola

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The most important component in an automotive transmission system is the gearbox which controls the speed of the vehicle. In an automatic transmission, the right positioning of actuators ensures efficient transmission mechanism embodiment, wherein the challenge lies in formulating the number of actuators associated with modelling a gearbox. Data with respect to actuation and gear shifting sequence has been retrieved from the available literature, including patent documents, and has been used in this proposed heuristics based methodology for modelling actuation sequence in a gear box. This paper presents a methodological approach in designing a gearbox for the purpose of obtaining an optimal shifting sequence. The computational model considers factors namely, the number of stages and gear teeth as input parameters since these two are the determinants of the gear ratios in an epicyclic gear train. The proposed transmission schematic or stick diagram aids in developing the gearbox layout design. The number of iterations and development time required to design a gearbox layout is reduced by using this approach.

Keywords: automatic transmission, gear-shifting, multi-stage planetary gearbox, rank ordered clustering

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Authors: Vahid Bayrami Rad

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In recent years, the use of artificial intelligence techniques, tools, and methods in processing medical images and health-related applications has been highlighted and a lot of research has been done in this regard. For example, colonoscopy and diagnosis of colon lesions are some cases in which the process of diagnosis of lesions can be improved by using image processing and artificial intelligence algorithms, which help doctors a lot. Due to the lack of accurate measurements and the variety of injuries in colonoscopy images, the process of diagnosing the type of lesions is a little difficult even for expert doctors. Therefore, by using different software and image processing, doctors can be helped to increase the accuracy of their observations and ultimately improve their diagnosis. Also, by using automatic methods, the process of diagnosing the type of disease can be improved. Therefore, in this paper, a deep learning framework called KCBA is proposed to classify colonoscopy lesions which are composed of several methods such as K-means clustering, a bag of features and deep auto-encoder. Finally, according to the experimental results, the proposed method's performance in classifying colonoscopy images is depicted considering the accuracy criterion.

Keywords: colorectal cancer, colonoscopy, region of interest, narrow band imaging, texture analysis, bag of feature

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Authors: Tianyang Xu

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Small underwater targets generally have a non-centrosymmetric geometry, and the acoustic scattering field of the target has spatial inhomogeneity under active sonar detection conditions. In view of the above problems, this paper takes the hemispherical cylindrical shell as the research object, and considers the angle continuity implied in the echo characteristics, and proposes a cluster-driven research method for the non-uniform characteristics of target echo angle. First, the target echo features are extracted, and feature vectors are constructed. Secondly, the t-SNE algorithm is used to improve the internal connection of the feature vector in the low-dimensional feature space and to construct the visual feature space. Finally, the implicit angular relationship between echo features is extracted under unsupervised condition by cluster analysis. The reconstruction results of the local geometric structure of the target corresponding to different categories show that the method can effectively divide the angle interval of the local structure of the target according to the natural acoustic scattering characteristics of the target.

Keywords: underwater target;, non-uniform characteristics;, cluster-driven method;, acoustic scattering characteristics

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Authors: Li Qingjian, Li Ke, He Chun, Huang Yong

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In this paper, the method of combining the Pohl Seidman's deep belief network with the self-organizing neural network is proposed to classify the target. This method is mainly aimed at the high nonlinearity of the hyperspectral image, the high sample dimension and the difficulty in designing the classifier. The main feature of original data is extracted by deep belief network. In the process of extracting features, adding known labels samples to fine tune the network, enriching the main characteristics. Then, the extracted feature vectors are classified into the self-organizing neural network. This method can effectively reduce the dimensions of data in the spectrum dimension in the preservation of large amounts of raw data information, to solve the traditional clustering and the long training time when labeled samples less deep learning algorithm for training problems, improve the classification accuracy and robustness. Through the data simulation, the results show that the proposed network structure can get a higher classification precision in the case of a small number of known label samples.

Keywords: DBN, SOM, pattern classification, hyperspectral, data compression

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Authors: Yixiu Liu, Yunzhou Zhang, Jianning Chi, Hao Chu, Rui Zheng, Libo Sun, Guanghao Chen, Fangtong Zhou

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In the last few years, large-scale person re-identification has attracted a lot of attention from video surveillance since it has a potential application prospect in public safety management. However, it is still a challenging job considering the variation in human pose, the changing illumination conditions and the lack of paired samples. Although the accuracy has been significantly improved, the data dependence of the sample training is serious. To tackle this problem, a new strategy is proposed based on bag of visual words (BoVW) model of designing the feature representation which has been widely used in the field of image retrieval. The local features are extracted, and more discriminative feature representation is obtained by cross-view dictionary learning (CDL), then the assignment map is obtained through k-means clustering. Finally, the BoVW histograms are formed which encodes the images with the statistics of the feature classes in the assignment map. Experiments conducted on the CUHK03, Market1501 and MARS datasets show that the proposed method performs favorably against existing approaches.

Keywords: bag of visual words, cross-view dictionary learning, person re-identification, reranking

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Authors: Iniye Irene Wodi, Kennedy B. Gibson

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Students’ poor performance in mathematics in both internal and external examinations has been a source of concern to researchers in Nigeria. The cause of this has been attributed to both teachers and students. To this end, this study sought to find out students’ perceptions of teachers’ attributes as a cause of mathematics phobia among secondary school students in Bayelsa State Nigeria. The population of the study comprised of all students of senior secondary schools in Yenagoa metropolis. A sample of 120 students was drawn from this population using clustering and simple random sampling techniques. The instrument for data collection was a researcher constructed questionnaire titled Mathematics Phobia Questionnaire (MPQ). Data were analysed, and the results revealed that students perceived teachers’ attributes such as methods and styles of teaching, difficulty in communication, etc. as causes of mathematics phobia among students in senior secondary schools in Bayelsa State. Based on the result, it was therefore recommended that mathematics teachers should be retrained periodically in order to learn new and innovative ways of teaching mathematics to prevent its phobia among students.

Keywords: mathematics phobia, teacher attributes, teaching method, teaching style

Procedia PDF Downloads 112

Authors: Bruce Wrightsman

Abstract:

Traditionally, in architectural education, we develop studio projects with learning agendas that try to minimize conflict and reveal clear design objectives. Knowledge is gleaned only tacitly through confronting the reciprocity of site and form, space and light, structure and envelope. This institutional reality can limit student learning to the latent learning opportunities they will have to confront later in practice. One intent of academic design-build projects is to address the learning opportunities which one can discover in the messy grey areas of design. In this immersive experience, students confront the limitations of classroom learning and are exposed to challenges that demand collaborative practice. As a result, design-build has been widely adopted in an attempt to address perceived deficiencies in design education vis a vis the integration of building technology and construction. Hands-on learning is not a new topic, as espoused by John Dewey, who posits a debate between static and active learning in his book Democracy and Education. Dewey espouses the concept that individuals should become participants and not mere observers of what happens around them. Advocates of academic design-build programs suggest a direct link between Dewey’s speculation. These experiences provide irreplaceable life lessons: that real-world decisions have real-life consequences. The goal of the paper is not to confirm or refute the legitimacy and efficacy of online virtual learning. Rather, the paper aims to foster a deeper, honest discourse on the meaning of ‘making’ in architectural education and present projects that confronted the burdens of a global pandemic and developed unique teaching strategies that challenged design thinking as an observational and constructive effort to expand design student’s making skills and foster student agency.

Keywords: design/build, making, remote teaching, architectural curriculum

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Authors: Zhou Mo, Dennis Chow

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In the dynamic positioning system (DPS) for vessels, the reliable information transmission between each note basically relies on the wireless protocols. From the perspective of cluster-based routing protocols for wireless sensor networks, the data fusion technology based on the sleep scheduling mechanism and remaining energy in network layer is proposed, which applies the sleep scheduling mechanism to the routing protocols, considering the remaining energy of node and location information when selecting cluster-head. The problem of uneven distribution of nodes in each cluster is solved by the Equilibrium. At the same time, Classified Forwarding Mechanism as well as Redelivery Policy strategy is adopted to avoid congestion in the transmission of huge amount of data, reduce the delay in data delivery and enhance the real-time response. In this paper, a simulation test is conducted to improve the routing protocols, which turn out to reduce the energy consumption of nodes and increase the efficiency of data delivery.

Keywords: DPS for vessel, wireless sensor network, data fusion, routing protocols

Procedia PDF Downloads 524

Authors: Yibin Qiu, Yubo Ouyang, Shihan Li, Guorui Zhang, Qi Li, Weirong Chen

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This paper aims at exploring the impacts of high dimensional dependencies of wind speed among wind farms on probabilistic optimal power flow. To obtain the reactive power optimization faster and more accurately, a mixture vine Copula structure model combining the K-means clustering, C vine copula and D vine copula is proposed in this paper, through which a more accurate correlation model can be obtained. Moreover, a Modified Backtracking Search Algorithm (MBSA), the three-point estimate method is applied to probabilistic optimal power flow. The validity of the mixture vine copula structure model and the MBSA are respectively tested in IEEE30 node system with measured data of 3 adjacent wind farms in a certain area, and the results indicate effectiveness of these methods.

Keywords: mixture vine copula structure model, three-point estimate method, the probability integral transform, modified backtracking search algorithm, reactive power optimization

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Authors: Rute Faria, Digo Moraes, Amanda Santos, Dione Morais, Maria Sartori

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Seed vigor is a fundamental component for the good performance of the entire soybean production process. Seeds with mechanical damage at harvest time will be more susceptible to fungal and insect attack during storage, which will invariably reduce their vigor to the field, compromising uniformity and final stand performance. Harvesters, even the most modern ones, when not properly regulated or operated, can cause irreversible damages to the seeds, compromising even their commercialization. Therefore, the control of an efficient harvest is necessary in order to guarantee a good quality final product. In this work, the damage caused by two different harvesters (one rented, and another one) was evaluated, traveling in two speeds (4 and 8 km / h). The design was completely randomized in 2 x 2 factorial, with four replications. To evaluate the physiological quality seed germination and vigor tests were carried out over a period of six months. A multivariate analysis of Principal Components (PCA) and clustering allowed us to verify that the leased machine had better performance in the incidence of immediate damages in the seeds, but after a storage period of 6 months the vigor of these seeds reduced more than own machine evidencing that such a machine would bring more damages to the seeds.

Keywords: Glycine max (L.), cluster analysis, PCA, vigor

Procedia PDF Downloads 257

Authors: Endrick Barnacin, Jean-Luc Henry, Jack Molinié, Jimmy Nagau, Hélène Delatte, Gérard Lebreton

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Palynology is a field of interest for many disciplines. It has multiple applications such as chronological dating, climatology, allergy treatment, and even honey characterization. Unfortunately, the analysis of a pollen slide is a complicated and time-consuming task that requires the intervention of experts in the field, which is becoming increasingly rare due to economic and social conditions. So, the automation of this task is a necessity. Pollen slides analysis is mainly a visual process as it is carried out with the naked eye. That is the reason why a primary method to automate palynology is the use of digital image processing. This method presents the lowest cost and has relatively good accuracy in pollen retrieval. In this work, we propose a system combining recognition and grouping of pollen. It consists of using a Logistic Model Tree to classify pollen already known by the proposed system while detecting any unknown species. Then, the unknown pollen species are divided using a cluster-based approach. Success rates for the recognition of known species have been achieved, and automated clustering seems to be a promising approach.

Keywords: pollen recognition, logistic model tree, expectation-maximization, local binary pattern

Procedia PDF Downloads 182

Authors: Zhou Mo, Dennis Chow

Abstract:

In the dynamic positioning system (DPS) for vessels, the reliable information transmission between each note basically relies on the wireless protocols. From the perspective of cluster-based routing pro-tocols for wireless sensor networks, the data fusion technology based on the sleep scheduling mechanism and remaining energy in network layer is proposed, which applies the sleep scheduling mechanism to the routing protocols, considering the remaining energy of node and location information when selecting cluster-head. The problem of uneven distribution of nodes in each cluster is solved by the Equilibrium. At the same time, Classified Forwarding Mechanism as well as Redelivery Policy strategy is adopted to avoid congestion in the transmission of huge amount of data, reduce the delay in data delivery and enhance the real-time response. In this paper, a simulation test is conducted to improve the routing protocols, which turns out to reduce the energy consumption of nodes and increase the efficiency of data delivery.

Keywords: DPS for vessel, wireless sensor network, data fusion, routing protocols

Procedia PDF Downloads 467

Authors: Shadreck Muchaku, Grey Magaiza, Jerit Dube

Abstract:

The high insolvency rate of female-led emerging enterprises in the Southern African mountain region reflects the various vulnerabilities that exist. Although this is the case, there is a limited understanding of how these vulnerabilities influence entrepreneurship failure. This paper focuses on female entrepreneurs because of their role in economic development. Emerging female entrepreneurs in this region often operate in uncertain environments, which makes it difficult for them to thrive. The form and nature of entrepreneurial opportunities rural women of the Afro Montane region engage in are largely unsustainable as a lot of women struggle with confidence, and they need help with understanding their skills. However, there is still a gap in the existing literature on women entrepreneurship resilience and vulnerability reduction in the Afromontane. Furthermore, a major problem is the lack of empirical studies on this matter and limited studies indicating a general profile of emerging female entrepreneurs in this region. This systematic literature review attempts to fill in the gap of knowledge on entrepreneurship resilience and vulnerability reduction of emerging female entrepreneurs in the Afromontane regions and other similar precarious environments. In this review, we focus much on highlighting the nexus between entrepreneurship resilience and vulnerability reduction of emerging female entrepreneurs in academic literature through a chronological dispersal of publications in developing countries. This review adopts an ATLAS ti.22 software-based thematic analysis to analyze results obtained from reviewed academic journal articles. As research on entrepreneurship resilience and vulnerability reduction is still developing in the Sothern African mountain region, the results of this review will contribute to the body of literature and provide recommendations and a foundation for future research. This systematic review paper provides valuable insights and methodological approaches to scholarship in a nascent area of emerging female entrepreneurs in the Afromontane.

Keywords: entrepreneurship resiliency, vulnerability reduction, female entrepreneurs, mountain regions

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Authors: Daniela V. Bavaresco, Tamy Colonetti, Antonio Jose Grande, Francesc Colom, Joao Quevedo, Samira S. Valvassori, Maria Ines da Rosa

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Objective: Performed a systematic review and meta-analysis to evaluated the potential effect of the cyclo-oxygenases (Cox)-2 inhibitor Celecoxib adjunct treatment in Bipolar Disorder (BD), through of randomized controlled trials. Method: A search of the electronic databases was proceeded, on MEDLINE, EMBASE, Scopus, Cochrane Central Register of Controlled Trials (CENTRAL), Biomed Central, Web of Science, IBECS, LILACS, PsycINFO (American Psychological Association), Congress Abstracts, and Grey literature (Google Scholar and the British Library) for studies published from January 1990 to February 2018. A search strategy was developed using the terms: 'Bipolar disorder' or 'Bipolar mania' or 'Bipolar depression' or 'Bipolar mixed' or 'Bipolar euthymic' and 'Celecoxib' or 'Cyclooxygenase-2 inhibitors' or 'Cox-2 inhibitors' as text words and Medical Subject Headings (i.e., MeSH and EMTREE) and searched. The therapeutic effects of adjunctive treatment with Celecoxib were analyzed, it was possible to carry out a meta-analysis of three studies included in the systematic review. The meta-analysis was performed including the final results of the Young Mania Rating Scale (YMRS) at the end of randomized controlled trials (RCT). Results: Three primary studies were included in the systematic review, with a total of 121 patients. The meta-analysis had significant effect in the YMRS scores from patients with BD who used Celecoxib adjuvant treatment in comparison to placebo. The weighted mean difference was 5.54 (95%CI=3.26-7.82); p < 0.001; I2 =0%). Conclusion: The systematic review suggests that adjuvant treatment with Celecoxib improves the response of major treatments in patients with BD when compared with adjuvant placebo treatment.

Keywords: bipolar disorder, Cox-2 inhibitors, Celecoxib, systematic review, meta-analysis

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Authors: Skiker Kaoutar, Mounir Maouene

Abstract:

Neuroimaging studies have shown that animal and tool concepts rely on distinct networks of brain areas. Animal concepts depend predominantly on temporal areas while tool concepts rely on fronto-temporo-parietal areas. However, the origin of this neurofunctional distinction for processing animal and tool concepts remains still unclear. Here, we address this question from a network perspective suggesting that the neural distinction between animals and tools might reflect the differences in their structural semantic networks. We build semantic networks for animal and tool concepts derived from McRae and colleagues’s behavioral study conducted on a large number of participants. These two networks are thus analyzed through a large number of graph theoretical measures for small-worldness: centrality, clustering coefficient, average shortest path length, as well as resistance to random and targeted attacks. The results indicate that both animal and tool networks have small-world properties. More importantly, the animal network is more vulnerable to targeted attacks compared to the tool network a result that correlates with brain lesions studies.

Keywords: animals, tools, network, semantics, small-worls, resilience to damage

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Authors: S. Nandagopalan, N. Pradeep

Abstract:

The aim of this paper is to propose a general framework for storing, analyzing, and extracting knowledge from two-dimensional echocardiographic images, color Doppler images, non-medical images, and general data sets. A number of high performance data mining algorithms have been used to carry out this task. Our framework encompasses four layers namely physical storage, object identification, knowledge discovery, user level. Techniques such as active contour model to identify the cardiac chambers, pixel classification to segment the color Doppler echo image, universal model for image retrieval, Bayesian method for classification, parallel algorithms for image segmentation, etc., were employed. Using the feature vector database that have been efficiently constructed, one can perform various data mining tasks like clustering, classification, etc. with efficient algorithms along with image mining given a query image. All these facilities are included in the framework that is supported by state-of-the-art user interface (UI). The algorithms were tested with actual patient data and Coral image database and the results show that their performance is better than the results reported already.

Keywords: active contour, Bayesian, echocardiographic image, feature vector

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Authors: Hamideh Nouri, Sattar Chavoshi Borujeni

Abstract:

Grey infrastructure is an unavoidable part of urbanisation that is threatening the local microclimates. Sustainable urbanisation requires more green infrastructure in cities such as green roofs to minimise urbanisation impacts. The environmental, social and economic benefits of green roofs are widely deliberated. However, there is still a lack of assessment of the water management for green roofs. This paper aimed to assess the irrigation management of green roofs in a semi-arid region where blue water scarcity is one of the primary challenges in urban water management. To determine the appropriate water source and growing media for green roofs, an experiment was established at the University of South Australia, Australia. This study compared the performance of two growing media and three water sources on the drainage quality, medium weight and survival rate of potted Tussock grass (Poa labillardieral), an endemic plant to Australia and recommended for green roofs. Three irrigation sources were tap water, mixed of wastewater-stormwater, and rainwater. The growing media were natural sandy loam soil and Scoria - one of the most used commercial growing media for green roofs. The drainage quality of these media was tested by analysing leachate samples. Medium weight was measured before and after watering, and all pots were monitored for their survival rates. Results showed that although plant growing development was significantly higher in Scoria, the survival rate was lower. For all three water sources, EC and pH of the leachate were significantly lower from Scoria than the sandy loam soil. However, the mixed of wastewater-stormwater had the highest EC, and rainwater had the lowest EC. Results did not present a significant difference between pH of different water resources in the same media. Our experimental results found the scoria and rainwater as the best sources of medium and water for green roofs.

Keywords: green smart cities, urban water, green roofs, green walls, wastewater, stormwater

Procedia PDF Downloads 159

Authors: Kaining Zhang, Lang Chen, Danyang Liu, Jianying Lu, Kun Yang, Junying Wu

Abstract:

In order to solve the problem of a large amount of simulation and limited simulation scale in the first-principle molecular dynamics simulation of energetic material detonation reaction, we established an artificial intelligence model for analyzing and predicting the detonation reaction mechanism of CL-20 based on the first-principle molecular dynamics simulation of the multiscale shock technique (MSST). We employed principal component analysis to identify the dominant charge features governing molecular reactions. We adopted the K-means clustering algorithm to cluster the reaction paths and screen out the key reactions. We introduced the neural network algorithm to construct the mapping relationship between the charge characteristics of the molecular structure and the key reaction characteristics so as to establish a calculation method for predicting detonation reactions based on the charge characteristics of CL-20 and realize the rapid analysis of the reaction mechanism of energetic materials.

Keywords: energetic material detonation reaction, first-principle molecular dynamics simulation of multiscale shock technique, neural network, CL-20

Procedia PDF Downloads 113

Authors: Anupama Sekhar, Preeti Saryan, Vinita Gowda

Abstract:

Plants, due to their sedentary lifestyle, have evolved mechanisms to synthesize a huge diversity of complex, specialized chemical metabolites, a majority of them being volatile organic compounds (VOCs). These VOCs are heavily involved in their biotic and abiotic interactions. Since chemical composition could be under the same selection processes as other morphological characters, we test if VOCs can be used to taxonomically distinguish species in the well-studied, fragrant ginger genus -Hedychium (Zingiberaceae). We propose that variations in the volatile profiles are suggestive of adaptation to divergent environments, and their presence could be explained by either phylogenetic conservatism or ecological factors. In this study, we investigate the volatile chemistry within Hedychium, which is endemic to Asian palaeotropics. We used an unsupervised clustering approach which clearly distinguished most taxa, and we used ancestral state reconstruction to estimate phylogenetic signals and chemical trait evolution in the genus. We propose that taxonomically, the chemical composition could aid in species identification, especially in species complexes where taxa are not morphologically distinguishable, and extensive, targeted chemical libraries will help in this effort.

Keywords: chemotaxonomy, dynamic headspace sampling, floral fragrance, floral volatile evolution, gingers, Hedychium

Procedia PDF Downloads 95

Authors: Marc S. Paolella, Pawel Polak

Abstract:

The paper proposes a framework for large-scale portfolio optimization which accounts for all the major stylized facts of multivariate financial returns, including volatility clustering, dynamics in the dependency structure, asymmetry, heavy tails, and non-ellipticity. It introduces a so-called risk fear portfolio strategy which combines portfolio optimization with active risk monitoring. The former selects optimal portfolio weights. The latter, independently, initiates market exit in case of excessive risks. The strategy agrees with the stylized fact of stock market major sell-offs during the initial stage of market downturns. The advantages of the new framework are illustrated with an extensive empirical study. It leads to superior multivariate density and Value-at-Risk forecasting, and better portfolio performance. The proposed risk fear portfolio strategy outperforms various competing types of optimal portfolios, even in the presence of conservative transaction costs and frequent rebalancing. The risk monitoring of the optimal portfolio can serve as an early warning system against large market risks. In particular, the new strategy avoids all the losses during the 2008 financial crisis, and it profits from the subsequent market recovery.

Keywords: comfort, financial crises, portfolio optimization, risk monitoring

Procedia PDF Downloads 525

Authors: Agnieszka S. Dzielendziak, Lindsay-Marie Armstrong, Matthew E. Potter, Robert Raja, Pier J. A. Sazio

Abstract:

Reducing carbon dioxide, CO₂, is one of the greatest global challenges. Conversion of CO₂ for utilisation across synthetic fuel, pharmaceutical, and agrochemical industries offers a promising option, yet requires significant research to understanding the complex multiscale processes involved. To experimentally understand and optimize such processes at that catalytic sites and exploring the impact of the process at reactor scale, is too expensive. Computational methods offer significant insight and flexibility but require a more detailed multi-scale approach which is a significant challenge in itself. This work introduces a computational approach which incorporates detailed catalytic models, taken from experimental investigations, into a larger-scale computational flow dynamics framework. The reactor-scale species transport approach is modified near the catalytic walls to determine the influence of catalytic clustering regions. This coupling approach enables more accurate modelling of velocity, pressures, temperatures, species concentrations and near-wall surface characteristics which will ultimately enable the impact of overall reactor design on chemical conversion performance.

Keywords: catalysis, CCU, CO₂, multi-scale model

Procedia PDF Downloads 253