Search results for: passive optical networks (PON)

Authors: Anjan Hazra, Nirjhar Dasgupta, Chandan Sengupta, Sauren Das

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Tea (Camellia sinensis) has received substantial attention from the scientific world time to time, not only for its commercial importance, but also for its demand to the health-conscious people across the world for its extensive use as potential sources of antioxidant supplement. These health-benefit traits primarily rely on some regulatory networks of different metabolic pathways. Development of microsatellite markers from the conserved genomic regions is being worthwhile for studying the genetic diversity of closely related species or self-pollinated species. Although several SSR markers have been reported, in tea the trait-specific Simple Sequence Repeats (SSRs) are yet to be identified, which can be used for marker assisted breeding technique. MicroRNAs are endogenous, noncoding, short RNAs directly involved in regulating gene expressions at the post-transcriptional level. It has been found that diversity in miRNA gene interferes the formation of its characteristic hair pin structure and the subsequent function. In the present study, the precursors of small regulatory RNAs (microRNAs) has been fished out from tea Expressed Sequence Tag (EST) database. Furthermore, the simple sequence repeat motifs within the putative miRNA precursor genes are also identified in order to experimentally validate their existence and function. It is already known that genic-SSR markers are very adept and breeder-friendly source for genetic diversity analysis. So, the potential outcome of this in-silico study would provide some novel clues in understanding the miRNA-triggered polymorphic genic expression controlling specific metabolic pathways, accountable for tea quality.

Keywords: micro RNA, simple sequence repeats, tea quality, trait specific marker

Procedia PDF Downloads 290

Authors: Rangrong Yoksan, Amporn Sane, Nattaporn Khanoonkon, Chanakorn Yokesahachart, Narumol Noivoil, Khanh Minh Dang

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Polylactic acid (PLA) is the most commercially available bio-based and biodegradable plastic at present. PLA has been used in plastic related industries including single-used containers, disposable and environmentally friendly packaging owing to its renewability, compostability, biodegradability, and safety. Although PLA demonstrates reasonably good optical, physical, mechanical, and barrier properties comparable to the existing petroleum-based plastics, its brittleness and mold shrinkage as well as its price are the points to be concerned for the production of rigid and semi-rigid packaging. Blending PLA with other bio-based polymers including thermoplastic starch (TPS) is an alternative not only to achieve a complete bio-based plastic, but also to reduce the brittleness, shrinkage during molding and production cost of the PLA-based products. TPS is a material produced mainly from starch which is cheap, renewable, biodegradable, compostable, and non-toxic. It is commonly prepared by a plasticization of starch under applying heat and shear force. Although glycerol has been reported as one of the most plasticizers used for preparing TPS, its migration caused the surface stickiness of the TPS products. In some cases, mixed plasticizers or natural fibers have been applied to impede the retrogradation of starch or reduce the migration of glycerol. The introduction of fibers into TPS-based materials could reinforce the polymer matrix as well. Therefore, the objective of the present research is to study the effect of starch type (i.e. native starch and phosphate starch), plasticizer type (i.e. glycerol and xylitol with a weight ratio of glycerol to xylitol of 100:0, 75:25, 50:50, 25:75, and 0:100), and fiber content (i.e. in the range of 1-25 % wt) on properties of PLA/TPS blend and composite. PLA/TPS blends and composites were prepared using a twin-screw extruder and then converted into dumbbell-shaped specimens using an injection molding machine. The PLA/TPS blends prepared by using phosphate starch showed higher tensile strength and stiffness than the blends prepared by using the native one. In contrast, the blends from native starch exhibited higher extensibility and heat distortion temperature (HDT) than those from the modified starch. Increasing xylitol content resulted in enhanced tensile strength, stiffness, and water resistance, but decreased extensibility and HDT of the PLA/TPS blend. Tensile properties and hydrophobicity of the blend could be improved by incorporating silane treated-jute fibers.

Keywords: polylactic acid, thermoplastic starch, Jute fiber, composite, blend

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Authors: K. Sathishkumar, V. Thiagarasu

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Due to recent advances in DNA microarray technology, it is now feasible to obtain gene expression profiles of tissue samples at relatively low costs. Many scientists around the world use the advantage of this gene profiling to characterize complex biological circumstances and diseases. Microarray techniques that are used in genome-wide gene expression and genome mutation analysis help scientists and physicians in understanding of the pathophysiological mechanisms, in diagnoses and prognoses, and choosing treatment plans. DNA microarray technology has now made it possible to simultaneously monitor the expression levels of thousands of genes during important biological processes and across collections of related samples. Elucidating the patterns hidden in gene expression data offers a tremendous opportunity for an enhanced understanding of functional genomics. However, the large number of genes and the complexity of biological networks greatly increase the challenges of comprehending and interpreting the resulting mass of data, which often consists of millions of measurements. A first step toward addressing this challenge is the use of clustering techniques, which is essential in the data mining process to reveal natural structures and identify interesting patterns in the underlying data. This work presents an analysis of several clustering algorithms proposed to deals with the gene expression data effectively. The existing clustering algorithms like Support Vector Machine (SVM), K-means algorithm and evolutionary algorithm etc. are analyzed thoroughly to identify the advantages and limitations. The performance evaluation of the existing algorithms is carried out to determine the best approach. In order to improve the classification performance of the best approach in terms of Accuracy, Convergence Behavior and processing time, a hybrid clustering based optimization approach has been proposed.

Keywords: microarray technology, gene expression data, clustering, gene Selection

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Authors: Debatosh Das, Moxian Chen, Jianhua Zhang, Caroline Gutjahr

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Arbuscular mycorrhiza (AM) is a mutualistic symbiosis between plant roots and Glomeromycotina fungi, which is activated under low but inhibited by high phosphate. The effect of phosphate on AM development has been observed for many years, but mechanisms regulating it under contrasting phosphate levels remain unknown. Based on previous observations that promoters of several AM functional genes contain PHR binding motifs, we hypothesized that PHR2, a master regulator of phosphate starvation response in rice, was recruited to regulate AM symbiosis development. We observed a drastic reduction in root colonization and significant AM transcriptome modulation in phr2. PHR2 targets genes required for root colonization and AM signaling. The role of PHR2 in improving root colonization, mycorrhizal phosphate uptake, and growth response was confirmed in field soil. In conclusion, rice PHR2, which is considered a master regulator of phosphate starvation responses, acts as a positive regulator of AM symbiosis between Glomeromycotina fungi and rice roots. PHR2 directly targets the transcription of plant strigolactone and AM genes involved in the establishment of this symbiosis. Our work facilitates an understanding of ways to enhance AMF propagule populations introduced in field soils (as a biofertilizer) in order to restore the natural plant-AMF networks disrupted by modern agricultural practices. We show that PHR2 is required for AM-mediated improvement of rice yield in low phosphate paddy field soil. Thus, our work contributes knowledge for rational application of AM in sustainable agriculture. Our data provide important insights into the regulation of AM by the plant phosphate status, which has a broad significance in agriculture and terrestrial ecosystems.

Keywords: biofertilizer, phosphate, mycorrhiza, rice, sustainable, symbiosis

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Authors: Roman Kulikov, Svetlana Kolesnikova

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Last decade Cloud Computing technologies have been rapidly becoming ubiquitous. Each year more and more organizations, corporations, internet services and social networks trust their business sensitive information to Public Cloud. The data storage in Public Cloud is protected by security mechanisms such as firewalls, cryptography algorithms, backups, etc.. In this way, however, only outsider attacks can be prevented, whereas virtualization tools can be easily compromised by insider. The protection of Public Cloud’s critical elements from internal intruder remains extremely challenging. A hypervisor, also called a virtual machine manager, is a program that allows multiple operating systems (OS) to share a single hardware processor in Cloud Computing. One of the hypervisor's functions is to enforce access control policies. Furthermore, it prevents guest OS from disrupting each other and from accessing each other's memory or disk space. Hypervisor is the one of the most critical and vulnerable elements in Cloud Computing infrastructure. Nevertheless, it has been poorly protected from being compromised by insider. By exploiting certain vulnerabilities, privilege escalation can be easily achieved in insider attacks on hypervisor. In this way, an internal intruder, who has compromised one process, is able to gain control of the entire virtual machine. Thereafter, the consequences of insider attacks in Public Cloud might be more catastrophic and significant to virtual tools and sensitive data than of outsider attacks. So far, almost no preventive security countermeasures have been developed. There has been little attention paid for developing models to assist risks mitigation strategies. In this paper formal model of insider attacks on hypervisor is designed. Our analysis identifies critical hypervisor`s vulnerabilities that can be easily compromised by internal intruder. Consequently, possible conditions for successful attacks implementation are uncovered. Hence, development of preventive security countermeasures can be improved on the basis of the proposed model.

Keywords: insider attack, public cloud, cloud computing, hypervisor

Procedia PDF Downloads 346

Authors: Kasra Majbouri Yazdi, Adel Majbouri Yazdi, Saeid Khodayi, Jingyu Hou, Wanlei Zhou, Saeed Saedy

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Fake news and false information are big challenges of all types of media, especially social media. There is a lot of false information, fake likes, views and duplicated accounts as big social networks such as Facebook and Twitter admitted. Most information appearing on social media is doubtful and in some cases misleading. They need to be detected as soon as possible to avoid a negative impact on society. The dimensions of the fake news datasets are growing rapidly, so to obtain a better result of detecting false information with less computation time and complexity, the dimensions need to be reduced. One of the best techniques of reducing data size is using feature selection method. The aim of this technique is to choose a feature subset from the original set to improve the classification performance. In this paper, a feature selection method is proposed with the integration of K-means clustering and Support Vector Machine (SVM) approaches which work in four steps. First, the similarities between all features are calculated. Then, features are divided into several clusters. Next, the final feature set is selected from all clusters, and finally, fake news is classified based on the final feature subset using the SVM method. The proposed method was evaluated by comparing its performance with other state-of-the-art methods on several specific benchmark datasets and the outcome showed a better classification of false information for our work. The detection performance was improved in two aspects. On the one hand, the detection runtime process decreased, and on the other hand, the classification accuracy increased because of the elimination of redundant features and the reduction of datasets dimensions.

Keywords: clustering, fake news detection, feature selection, machine learning, social media, support vector machine

Procedia PDF Downloads 154

Authors: Elucir Gir, Laelson Rochelle Milanês Sousa, Pedro Henrique Tertuliano Leoni, Carla Aparecida Arena Ventura, Ana Cristina de Oliveira e Silva, Renata Karina Reis

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Context and significance: Resilience is a protective agent for the physical and mental well-being of nursing professionals. Team members are constantly subjected to high levels of work stress that can negatively impact care performance and users of health services. Stress levels have been exacerbated with the COVID-19 pandemic. Objective: The aim of this study was to analyze the resilience of nursing professionals in Brazil during the COVID-19 pandemic. Method: Cross-sectional study with a quantitative approach carried out with professionals from nursing teams from all regions of Brazil. Data collection took place in the first year of the pandemic between October and December 2020. Data were obtained through an online questionnaire posted on social networks. The information collected included the sociodemographic characterization of the nursing professionals and the Brief Resilient Coping Scale was applied. Student's t-test for independent samples and analysis of variance (ANOVA) were used to compare resilience scores with sociodemographic variables. Results: 8,792 nursing professionals participated in the study, 5,767 (65.6%) were nurses, 7,437 (84.6%) were female and 2,643 (30.1%) were from the Northeast region of Brazil, 5,124 (58.8% ) had low levels of resilience. The results showed a statistically significant difference between the resilience score and the variables: professional category (p<0.001); sex (p = 0.003); age range (p<0.001); region of Brazil (p<0.001); marital status (p=0.029) and providing assistance in a field hospital (p<0.001). Conclusion: Participants in this study had, in general, low levels of resilience. There is an urgent need for actions aimed at promoting the psychological health of nursing professionals inserted in pandemic contexts. Descriptors: Psychological Resilience; Nursing professionals; COVID-19; SARSCoV-2.

Keywords: psychological resilience, nursing professionals, COVID-19, SARS-CoV-2

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Authors: H. Parveen Begam, M. A. Maluk Mohamed

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Mobile Grid Computing is an environment that allows sharing and coordinated use of diverse resources in dynamic, heterogeneous and distributed environment using different types of electronic portable devices. In a grid environment the security issues are like authentication, authorization, message protection and delegation handled by GSI (Grid Security Infrastructure). Proving better security between mobile devices and grid infrastructure is a major issue, because of the open nature of wireless networks, heterogeneous and distributed environments. In a mobile grid environment, the individual computing devices may be resource-limited in isolation, as an aggregated sum, they have the potential to play a vital role within the mobile grid environment. Some adaptive methodology or solution is needed to solve the issues like authentication of a base station, security of information flowing between a mobile user and a base station, prevention of attacks within a base station, hand-over of authentication information, communication cost of establishing a session key between mobile user and base station, computing complexity of achieving authenticity and security. The sharing of resources of the devices can be achieved only through the trusted relationships between the mobile hosts (MHs). Before accessing the grid service, the mobile devices should be proven authentic. This paper proposes the dynamic certificate based mutual authentication protocol between two mobile hosts in a mobile grid environment. The certificate generation process is done by CA (Certificate Authority) for all the authenticated MHs. Security (because of validity period of the certificate) and dynamicity (transmission time) can be achieved through the secure service certificates. Authentication protocol is built on communication services to provide cryptographically secured mechanisms for verifying the identity of users and resources.

Keywords: mobile grid computing, certificate authority (CA), SSL/TLS protocol, secured service certificates

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Authors: Irene Yi

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Machine learning, natural language processing, and neural network models of language are becoming more and more prevalent in the fields of technology and linguistics today. Training data for machines are at best, large corpora of human literature and at worst, a reflection of the ugliness in society. Machines have been trained on millions of human books, only to find that in the course of human history, derogatory and sexist adjectives are used significantly more frequently when describing females in history and literature than when describing males. This is extremely problematic, both as training data, and as the outcome of natural language processing. As machines start to handle more responsibilities, it is crucial to ensure that they do not take with them historical sexist and misogynistic notions. This paper gathers data and algorithms from neural network models of language having to deal with syntax, semantics, sociolinguistics, and text classification. Results are significant in showing the existing intentional and unintentional misogynistic notions used to train machines, as well as in developing better technologies that take into account the semantics and syntax of text to be more mindful and reflect gender equality. Further, this paper deals with the idea of non-binary gender pronouns and how machines can process these pronouns correctly, given its semantic and syntactic context. This paper also delves into the implications of gendered grammar and its effect, cross-linguistically, on natural language processing. Languages such as French or Spanish not only have rigid gendered grammar rules, but also historically patriarchal societies. The progression of society comes hand in hand with not only its language, but how machines process those natural languages. These ideas are all extremely vital to the development of natural language models in technology, and they must be taken into account immediately.

Keywords: gendered grammar, misogynistic language, natural language processing, neural networks

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Authors: Izabel Cristina Zattar, Gilberto Passos Lima, Guilherme Schünemann de Oliveira

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In Brazil, there are several institutions committed to the development of new businesses based on product innovation. Among them are business incubators, universities and science institutes. Business incubators can be defined as nurseries for new companies, which may be in the technology segment, discussed in this article. Business incubators provide services related to infrastructure, such as physical space and meeting rooms. Besides these services, incubators also offer assistance in the form of information and communication, access to finance, relationship networks and business monitoring and mentoring processes. Business incubators support not all technology companies. One of the business incubators tasks is to assess the nature and feasibility of new business proposals. To assist this goal, this paper proposes a methodology for evaluating new business using the Analytic Hierarchy Process (AHP). This paper presents the concepts used in the assessing methodology application for new business, concepts that have been tested with positive results in practice. This study counts on three main steps: first, a hierarchy was built, based on new business manuals used by the business incubators. These books and manuals relate business selection requirements, such as the innovation status and other technological aspects. Then, a questionnaire was generated, in order to guide incubator experts in the parity comparisons at all hierarchy levels. The weights of each requirement are calculated from information obtained from the questionnaire responses. Finally, the proposed method was applied to evaluate five new business proposals, which were applying to be part of a company incubator. The main result is the classification of these new businesses, which helped the incubator experts to decide what companies were more eligible to work with. This classification may also be helpful to the decision-making process of business incubators in future selection processes.

Keywords: Analytic Hierarchy Process (AHP), Brazilian companies incubators, technology companies, incubator

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Authors: Ghulamhossein Nezami

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Civilizations based in Iran, especially in the south, have always realized the all-around importance of the Persian Sea and for various reasons, have paid full attention to it. The first of these was the pre-Aryan government, Ilam in the coastal province of Sharihum and the city of Lian (now the port of Bushehr) in terms of trade, defense and religion. With the establishment of the Achaemenids on the entire plateau of Iran to the center of Persia, they created several communication routes from Parseh to the shores of the Persian Gulf, which ended in the present Bushehr province. This coastal area was extended by a road in the coastal plain to the more southern parts of the ports of Ausinze - according to Ptolemy the port of Siraf before the Sassanids - and Epstane and Hormozia in the present-day Strait of Hormuz. Meanwhile, the ancient city of Temukknana, whose new historical documents testify to its extraordinary importance in the Achaemenid period, especially Darius I of the Achaemenids, from a strategic position with the coastal areas, the coasts and on the other hand with the gamers, the political center. - Achaemenid administration, had. New archeological evidence, research, and excavations show that both the famous Achaemenid kings and courtiers paid special attention to Tamukknana. The discovery of a tomb and three Achaemenid palaces from before the reign of Cyrus to Xerxes in this region showed the importance of the strategic, security-defense and commercial position of this region, extraordinary for the Achaemenids. Therefore, the city of Temukkana in the Dashtestan region of present-day Bushehr province became an important Achaemenid center on the Persian Gulf coast and became the political-economic center of gravity of the Achaemenids and the regulator of communication networks on the Persian Gulf coast. This event showed that the Achaemenids attached importance to their economic goals and oversight of their vast territory by the Persian Gulf. Methods: Book resources and field study.

Keywords: Achaemenids, Bushehr, Persian Gulf, Tamukkana

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Authors: Ayse Ozbil, Gorsev Argin, Demet Yesiltepe

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Data for this cross-sectional study were drawn from questionnaires conducted in 10 elementary schools (1000 students, ages 12-14) located in Istanbul, Turkey. School environments (1600 meter buffers around the school) were evaluated through GIS-based land-use data (parcel level land use density) and street-level topography. Street networks within the same buffers were evaluated by using angular segment analysis (Integration and Choice) implemented in Depthmap as well as two segment-based connectivity measures, namely Metric and Directional Reach implemented in GIS. Segment Angular Integration measures how accessible each space from all the others within the radius using the least angle measure of distance. Segment Angular Choice which measures how many times a space is selected on journeys between all pairs of origins and destinations. Metric Reach captures the density of streets and street connections accessible from each individual road segment. Directional Reach measures the extent to which the entire street network is accessible with few direction changes. In addition, socio-economic characteristics (annual income, car ownership, education-level) of parents, obtained from parental questionnaires, were also included in the analysis. It is shown that surrounding street network configuration is strongly associated with both walk-mode shares and average walking distances to/from schools when controlling for parental socio-demographic attributes as well as land-use compositions and topographic features in school environments. More specifically, findings suggest that the scale at which urban form has an impact on pedestrian travel is considerably larger than a few blocks around the school.

Keywords: Istanbul, street network layout, urban form, walking to/from school

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Authors: Jessica Achebe, Susan Tighe

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The reduction of material consumption and greenhouse gas emission when maintain and rehabilitating road networks can achieve added benefits including improved life cycle performance of pavements, reduced climate change impacts and human health effect due to less air pollution, improved productivity due to an optimal allocation of resources and reduced road user cost. This is the essence of incorporating environmental sustainability into pavement management. The functionality of performance measurement approach has made it one of the most valuable tool to Pavement Management Systems (PMSs) to account for different criteria in the decision-making process. However measuring the environmental performance of road network is still a far-fetched practice in road network management, more so an ostensive agency-wide environmental sustainability or sustainable maintenance specifications is missing. To address this challenge, this present research focuses on the environmental sustainability performance of network-level pavement management. The ultimate goal is to develop a framework to incorporate environmental sustainability in pavement management systems for network-level maintenance programming. In order to achieve this goal, this paper present the first step, the intention is to review the previous studies that employed environmental performance measures, as well as the suitability of environmental performance indicators for the evaluation of the sustainability of network-level pavement maintenance strategies. Through an industry practice survey, this paper provides a brief forward regarding the pavement manager motivations and barriers to making more sustainable decisions, and data needed to support the network-level environmental sustainability. The trends in network-level sustainable pavement management are also presented, existing gaps are highlighted, and ideas are proposed for network-level sustainable maintenance and rehabilitation programming.

Keywords: pavement management, environment sustainability, network-level evaluation, performance measures

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Authors: Ahmed Ismail, Mustafa Baysal

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Although the energy source in the world is mainly based on fossil fuels today, there is a need for alternative energy generation systems, which are more economic and environmentally friendly, due to continuously increasing demand of electric energy and lacking power resources and networks. Distributed Energy Resources (DERs) such as fuel cells, wind and solar power have recently become widespread as alternative generation. In order to solve several problems that might be encountered when integrating DERs to power system, the microgrid concept has been proposed. A microgrid can operate both grid connected and island mode to benefit both utility and customers. For most distributed energy resources (DER) which are connected in parallel in LV-grid like micro-turbines, wind plants, fuel cells and PV cells electrical power is generated as a direct current (DC) and converted to an alternative currents (AC) by inverters. So the inverters are assumed to be primary components in a microgrid. There are many control techniques of parallel inverters to manage active and reactive sharing of the loads. Some of them are based on droop method. In literature, the studies are usually focused on improving the transient performance of inverters. In this study, the performance of two different controllers based on droop control method is compared for the inverters operated in parallel without any communication feedback. For this aim, a microgrid in which inverters are controlled by conventional droop controller and modified droop controller is designed. Modified controller is obtained by adding PID into conventional droop control. Active and reactive power sharing performance, voltage and frequency responses of those control methods are measured in several operational cases. Study cases have been simulated by MATLAB-SIMULINK.

Keywords: active and reactive power sharing, distributed generation, droop control, microgrid

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Authors: Robin Anderson, Elizabeth Latham, David Nisbet

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Propagation and dissemination of antimicrobial resistant and pathogenic microbes from spoiled silages and composts represents a serious public health threat to humans and animals. In the present study, the antimicrobial activity of the short chain nitro-compound, 2-nitro-1-propanol (9 mM) as well as the medium chain fatty acid, lauric acid, and its glycerol monoester, monolaurin, (each at 25 and 17 µmol/mL, respectfully) were investigated against select pathogenic and multi-drug resistant antimicrobial resistant Gram-positive bacteria common to spoiled silages and composts. In an initial study, we found that growth rates of a multi-resistant Enterococcus faecalis (expressing resistance against erythromycin, quinupristin/dalfopristin and tetracycline) and Staphylococcus aureus strain 12600 (expressing resistance against erythromycin, linezolid, penicillin, quinupristin/dalfopristin and vancomycin) were more than 78% slower (P < 0.05) by 2-nitro-1-propanol treatment during culture (n = 3/treatment) in anaerobically prepared ½ strength Brain Heart Infusion broth at 37oC when compared to untreated controls (0.332 ± 0.04 and 0.108 ± 0.03 h-1, respectively). The growth rate of 2-nitro-1-propanol-treated Listeria monocytogenes was also decreased by 96% (P < 0.05) when compared to untreated controls cultured similarly (0.171 ± 0.01 h-1). Maximum optical densities measured at 600 nm were lower (P < 0.05) in 2-nitro-1-propanol-treated cultures (0.053 ± 0.01, 0.205 ± 0.02 and 0.041 ± 0.01, respectively) than in untreated controls (0.483 ± 0.02, 0.523 ± 0.01 and 0.427 ± 0.01, respectively) for E. faecalis, S. aureus and L. monocytogenes, respectively. When tested against mixed microbial populations during anaerobic 24 h incubation of spoiled silage, significant effects of treatment with 1 mg 2-nitro-1-propanol (approximately 9.5 µmol/g) or 5 mg lauric acid/g (approximately 25 µmol/g) on populations of wildtype Enterococcus and Listeria were not observed. Mixed populations treated with 5 mg monolaurin/g (approximately 17 µmol/g) had lower (P < 0.05) viable cell counts of wildtype enterococci than untreated controls after 6 h incubation (2.87 ± 1.03 versus 5.20 ± 0.25 log10 colony forming units/g, respectively) but otherwise significant effects of monolaurin were not observed. These results reveal differential susceptibility of multi-drug resistant enterococci and staphylococci as well as L. monocytogenes to the inhibitory activity of 2-nitro-1-propanol and the medium chain fatty acid, lauric acid and its glycerol monoester, monolaurin. Ultimately, these results may lead to improved treatment technologies to preserve the microbiological safety of silages and composts.

Keywords: 2-nitro-1-propanol, lauric acid, monolaurin, gram positive bacteria

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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: Saif Obaid Alkaabi, Nabil Ayad

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Governments constantly seek to offer faster, more secure, efficient and effective services for their citizens. Recent changes and developments to communication services and technologies, mainly due the Internet, have led to immense improvements in the way governments of advanced countries carry out their interior operations Therefore, advances in e-government services have been broadly adopted and used in various developed countries, as well as being adapted to developing countries. The implementation of advances depends on the utilization of the most innovative structures of data techniques, mainly in web dependent applications, to enhance the main functions of governments. These functions, in turn, have spread to mobile and wireless techniques, generating a new advanced direction called m-government. This paper discusses a selection of available m-government applications and several business modules and frameworks in various fields. Practically, the m-government models, techniques and methods have become the improved version of e-government. M-government offers the potential for applications which will work better, providing citizens with services utilizing mobile communication and data models incorporating several government entities. Developing countries can benefit greatly from this innovation due to the fact that a large percentage of their population is young and can adapt to new technology and to the fact that mobile computing devices are more affordable. The use of models of mobile transactions encourages effective participation through the use of mobile portals by businesses, various organizations, and individual citizens. Although the application of m-government has great potential, it does have major limitations. The limitations include: the implementation of wireless networks and relative communications, the encouragement of mobile diffusion, the administration of complicated tasks concerning the protection of security (including the ability to offer privacy for information), and the management of the legal issues concerning mobile applications and the utilization of services.

Keywords: e-government, m-government, system dependability, system security, trust

Procedia PDF Downloads 364

Authors: Riyona Chetty, Raisuyah Bhagwan, Nalini Govender

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Background: A myriad of physical, psychosocial, and environmental sequelae are associated with limb loss. However, there is a paucity of empirical South African data, which focuses on these sequelae, how they interface with the amputee’s quality of life as well as the challenges they experience following amputation. Objective: This study sought to explore the biopsychosocial effects of amputation and how amputation affected the quality of life of transtibial amputees. Setting: Participants were recruited from a medical facility, under the KwaZulu-Natal Department of Health in South Africa. Methods: A qualitative approach guided this study. Data was collected using one-on-one interviews with 14 unilateral transtibial amputees. Data was analysed thematically. Results: Five broad themes emerged from the inquiry, which captured amputees’ experiences of phantom limb pain, body image disturbances, and their challenges related to adapting to daily activities. Participants also expressed the salience of familial support as well as the importance of psychological interventions to cope. Conclusion: The findings suggested that support networks and professional psychological intervention are imperative in facilitating successful adjustment to the amputation experience. Raising awareness of limb loss in both, rural and urban settings may help reduce the stigma attached to it. Contribution: Quality of life comprises several domains, namely physical, psychological, environmental, and social albeit limited local and international data exists regarding the environmental and social effects. This study brought to the fore the positive and negative effects of amputation in each domain, as well as various strategies that facilitate successful adjustment to amputation.

Keywords: amputation, quality of life, biopsychosocial, phantom limb pain, body image, support

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Authors: Nothando Gwazani, K. R. Marembo

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An increase in the atmospheric concentration of carbon dioxide (CO₂) from fossil fuel and land use change necessitates identification of strategies for mitigating threats associated with global warming. Oceans are insufficient to offset the accelerating rate of carbon emission. However, the challenges of oceans as a source of reducing carbon footprint can be effectively overcome by the storage of carbon in terrestrial carbon sinks. The gases with special optical properties that are responsible for climate warming include carbon dioxide (CO₂), water vapors, methane (CH₄), nitrous oxide (N₂O), nitrogen oxides (NOₓ), stratospheric ozone (O₃), carbon monoxide (CO) and chlorofluorocarbons (CFC’s). Amongst these, CO₂ plays a crucial role as it contributes to 50% of the total greenhouse effect and has been linked to climate change. Because plants act as carbon sinks, interest in terrestrial carbon sequestration has increased in an effort to explore opportunities for climate change mitigation. Removal of carbon from the atmosphere is a topical issue that addresses one important aspect of an overall strategy for carbon management namely to help mitigate the increasing emissions of CO₂. Thus, terrestrial ecosystems have gained importance for their potential to sequester carbon and reduce carbon sink in oceans, which have a substantial impact on the ocean species. Field data and electromagnetic spectrum bands were analyzed using ArcGIS 10.2, QGIS 2.8 and ERDAS IMAGINE 2015 to examine the vegetation distribution. Satellite remote sensing data coupled with Normalized Difference Vegetation Index (NDVI) was employed to assess future potential changes in vegetation distributions in Eastern Cape Province of South Africa. The observed 5-year interval analysis examines the amount of carbon absorbed using vegetation distribution. In 2015, the numerical results showed low vegetation distribution, therefore increased the acidity of the oceans and gravely affected fish species and corals. The outcomes suggest that the study area could be effectively utilized for carbon sequestration so as to mitigate ocean acidification. The vegetation changes measured through this investigation suggest an environmental shift and reduced vegetation carbon sink, and that threatens biodiversity and ecosystem. In order to sustain the amount of carbon in the terrestrial ecosystems, the identified ecological factors should be enhanced through the application of good land and forest management practices. This will increase the carbon stock of terrestrial ecosystems thereby reducing direct loss to the atmosphere.

Keywords: remote sensing, vegetation dynamics, carbon sequestration, terrestrial carbon sink

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Authors: Dimpy Shah

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The global economy has substantially changed in last three decades. Now almost all markets are transparent and visible for global customers. The corporates are now no more reliant on local markets for trade. The information technology revolution has changed business dynamics and marketing practices of corporate. The markets are divided into two different formats: traditional and virtual. In very short span of time, many e-commerce portals have captured global market. This strategy is well supported by global delivery system of multinational logistic companies. Now the markets are dealing with global supply chain networks, which are more demand driven and customer oriented. The corporate have realized importance of supply chain integration and marketing in this competitive environment. The Indian markets are also significantly affected with all these changes. In terms of population, India is in second place after China. In terms of demography, almost half of the population is of youth. It has been observed that the Indian youth are more inclined towards e-commerce and prefer to buy goods from web portal. Initially, this trend was observed in Indian service sector, textile and electronic goods and now further extended in other product categories. The FMCG companies have also recognized this change and started integration of their supply chain with e-commerce platform. This paper attempts to understand contemporary marketing practices of corporate in e-commerce business in Indian food and beverages segment and also tries to identify innovative marketing practices for proper execution of their strategies. The findings are mainly focused on supply chain re-integration and brand building strategies with proper utilization of social media.

Keywords: FMCG (Fast Moving Consumer Goods), ISCM (Integrated supply chain management), RFID (Radio Frequency Identification), traditional and virtual formats

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Authors: Oumaima Khlifati, Khadija Baba

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Pavement distress is the main factor responsible for the deterioration of road structure durability, damage vehicles, and driver comfort. Transportation agencies spend a high proportion of their funds on pavement monitoring and maintenance. The auscultation of pavement distress was based on the manual survey, which was extremely time consuming, labor intensive, and required domain expertise. Therefore, the automatic distress detection is needed to reduce the cost of manual inspection and avoid more serious damage by implementing the appropriate remediation actions at the right time. Inspired by recent deep learning applications, this paper proposes an algorithm for automatic road distress detection and classification using on the Deep Convolutional Neural Network (DCNN). In this study, the types of pavement distress are classified as transverse or longitudinal cracking, alligator, pothole, and intact pavement. The dataset used in this work is composed of public asphalt pavement images. In order to learn the structure of the different type of distress, the DCNN models are trained and tested as a multi-label classification task. In addition, to get the highest accuracy for our model, we adjust the structural optimization hyper parameters such as the number of convolutions and max pooling, filers, size of filters, loss functions, activation functions, and optimizer and fine-tuning hyper parameters that conclude batch size and learning rate. The optimization of the model is executed by checking all feasible combinations and selecting the best performing one. The model, after being optimized, performance metrics is calculated, which describe the training and validation accuracies, precision, recall, and F1 score.

Keywords: distress pavement, hyperparameters, automatic classification, deep learning

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Authors: Hadjer Sadoune, Liza Lamini, Scherazade Krim, Amel Djouadi, Rachida Rihani

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Deep neural networks are highly regarded for their accuracy in predicting intricate fermentation processes. Their ability to learn from a large amount of datasets through artificial intelligence makes them particularly effective models. The primary obstacle in improving the performance of these models is to carefully choose the suitable hyperparameters, including the neural network architecture (number of hidden layers and hidden units), activation function, optimizer, learning rate, and other relevant factors. This study predicts biogas production from real wastewater treatment plant data using a sophisticated approach: hybrid Bayesian optimization with a tree-structured Parzen estimator (BO-TPE) for an optimised deep neural network (DNN) model. The plant utilizes an Upflow Anaerobic Sludge Blanket (UASB) digester that treats industrial wastewater from soft drinks and breweries. The digester has a working volume of 1574 m3 and a total volume of 1914 m3. Its internal diameter and height were 19 and 7.14 m, respectively. The data preprocessing was conducted with meticulous attention to preserving data quality while avoiding data reduction. Three normalization techniques were applied to the pre-processed data (MinMaxScaler, RobustScaler and StandardScaler) and compared with the Non-Normalized data. The RobustScaler approach has strong predictive ability for estimating the volume of biogas produced. The highest predicted biogas volume was 2236.105 Nm³/d, with coefficient of determination (R2), mean absolute error (MAE), and root mean square error (RMSE) values of 0.712, 164.610, and 223.429, respectively.

Keywords: anaerobic digestion, biogas production, deep neural network, hybrid bo-tpe, hyperparameters tuning

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Authors: Maria Botero-Uribe, Melissa Fitzgerald, Robert Gilbert, Kim Bryceson, Jocelyn Midgley

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French fry manufacturing involves a series of processes in which structural properties of potatoes are modified to produce crispy french fries which consumers enjoy. In addition to the traditional french fry manufacturing process, the industry is applying a relatively new process called pulsed electrical field (PEF) to the whole potatoes. There is a wealth of information on the technical treatment conditions of PEF, however, there is a lack of information about its effect on the structural properties that affect texture and its synergistic interactions with the other manufacturing steps of french fry production. The effect of PEF on starch gelatinisation properties of Russet Burbank potato was measured using a Differential Scanning Calorimeter. Cation content (K+, Ca2+ and Mg2+) was determined by inductively coupled plasma optical emission spectrophotometry. Firmness, and toughness of raw and blanched potatoes were determined in an uniaxial compression test. Moisture content was determined in a vacuum oven and oil content was measured using the soxhlet system with hexane. The final texture of the french fries – crispness - was determined using a three bend point test. Triangle tests were conducted to determine if consumers were able to perceive sensory differences between French fries that were PEF treated and those without treatment. The concentration of K+, Ca2+ and Mg2+ decreased significantly in the raw potatoes after the PEF treatment. The PEF treatment significantly increased modulus of elasticity, compression strain, compression force and toughness in the raw potato. The PEF-treated raw potato were firmer and stiffer, and its structure integrity held together longer, resisted higher force before fracture and stretched further than the untreated ones. The strain stress relationship exhibited by the PEF-treated raw potato could be due to an increase in the permeability of the plasmalema and tonoplasm allowing Ca2+ and Mg2+ cations to reach the cell wall and middle lamella, and be available for cross linking with the pectin molecule. The PEF-treated raw potato exhibited a slightly higher onset gelatinisation temperatures, similar peak temperatures and lower gelatinisation ranges than the untreated raw potatoes. The final moisture content of the french fries was not significantly affected by the PEF treatment. Oil content in the PEF- treated potatoes was lower than the untreated french fries, however, not statistically significant at 5 %. The PEF treatment did not have an overall significant effect on french fry crispness (modulus of elasticity), flexure stress or strain. The triangle tests show that most consumers could not detect a difference between French fries that received a PEF treatment from those that did not.

Keywords: french fries, mechanical properties, PEF, potatoes

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Authors: P. Barreto, A. Guevara, V. Ibujes

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In this study, the removal of arsenic from synthetic solutions and natural water from Papallacta Lagoon was evaluated, by using the rhizofiltration method with terrestrial and aquatic plant species. Ecuador is a country of high volcanic activity, that is why most of water sources come from volcanic glaciers. Therefore, it is necessary to find new, affordable and effective methods for treating water. The water from Papallacta Lagoon shows levels from 327 µg/L to 803 µg/L of arsenic. The evaluation for the removal of arsenic began with the selection of 16 different species of terrestrial and aquatic plants. These plants were immersed to solutions of 4500 µg/L arsenic concentration, for 48 hours. Subsequently, 3 terrestrial species and 2 aquatic species were selected based on the highest amount of absorbed arsenic they showed, analyzed by plasma optical emission spectrometry (ICP-OES), and their best capacity for adaptation into the arsenic solution. The chosen terrestrial species were cultivated from their seed with hydroponics methods, using coconut fiber and polyurethane foam as substrates. Afterwards, the species that best adapted to hydroponic environment were selected. Additionally, a control of the development for the selected aquatic species was carried out using a basic nutrient solution to provide the nutrients that the plants required. Following this procedure, 30 plants from the 3 types of species selected were exposed to a synthetic solution with levels of arsenic concentration of 154, 375 and 874 µg/L, for 15 days. Finally, the plant that showed the highest level of arsenic absorption was placed in 3 L of natural water, with arsenic levels of 803 µg/L. The plant laid in the water until it reached the desired level of arsenic of 10 µg/L. This experiment was carried out in a total of 30 days, in which the capacity of arsenic absorption of the plant was measured. As a result, the five species initially selected to be used in the last part of the evaluation were: sunflower (Helianthus annuus), clover (Trifolium), blue grass (Poa pratensis), water hyacinth (Eichhornia crassipes) and miniature aquatic fern (Azolla). The best result of arsenic removal was showed by the water hyacinth with a 53,7% of absorption, followed by the blue grass with 31,3% of absorption. On the other hand, the blue grass was the plant that best responded to the hydroponic cultivation, by obtaining a germination percentage of 97% and achieving its full growth in two months. Thus, it was the only terrestrial species selected. In summary, the final selected species were blue grass, water hyacinth and miniature aquatic fern. These three species were evaluated by immersing them in synthetic solutions with three different arsenic concentrations (154, 375 and 874 µg/L). Out of the three plants, the water hyacinth was the one that showed the highest percentages of arsenic removal with 98, 58 and 64%, for each one of the arsenic solutions. Finally, 12 plants of water hyacinth were chosen to reach an arsenic level up to 10 µg/L in natural water. This significant arsenic concentration reduction was obtained in 5 days. In conclusion, it was found that water hyacinth is the best plant to reduce arsenic levels in natural water.

Keywords: arsenic, natural water, plant species, rhizofiltration, synthetic solutions

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Authors: I. Nitoi, P. Oancea, M. Raileanu, M. Crisan, L. Constantin, I. Cristea

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Hazardous organic compounds like nitroaromatics are frequently found in chemical and petroleum industries discharged effluents. Due to their bio-refractory character and high chemical stability cannot be efficiently removed by classical biological or physical-chemical treatment processes. In the past decades, semiconductor photocatalysis has been frequently applied for the advanced degradation of toxic pollutants. Among various semiconductors titania was a widely studied photocatalyst, due to its chemical inertness, low cost, photostability and nontoxicity. In order to improve optical absorption and photocatalytic activity of TiO2 many attempts have been made, one feasible approach consists of doping oxide semiconductor with metal. The degradation of dinitrobenzene (DNB) and dinitrotoluene (DNT) from aqueous solution under UVA-VIS irradiation using heavy metal (0.5% Fe, 1%Co, 1%Ni ) doped titania was investigated. The photodegradation experiments were carried out using a Heraeus laboratory scale UV-VIS reactor equipped with a medium-pressure mercury lamp which emits in the range: 320-500 nm. Solutions with (0.34-3.14) x 10-4 M pollutant content were photo-oxidized in the following working conditions: pH = 5-9; photocatalyst dose = 200 mg/L; irradiation time = 30 – 240 minutes. Prior to irradiation, the photocatalyst powder was added to the samples, and solutions were bubbled with air (50 L/hour), in the dark, for 30 min. Dopant type, pH, structure and initial pollutant concentration influence on the degradation efficiency were evaluated in order to set up the optimal working conditions which assure substrate advanced degradation. The kinetics of nitroaromatics degradation and organic nitrogen mineralization was assessed and pseudo-first order rate constants were calculated. Fe doped photocatalyst with lowest metal content (0.5 wt.%) showed a considerable better behaviour in respect to pollutant degradation than Co and Ni (1wt.%) doped titania catalysts. For the same working conditions, degradation efficiency was higher for DNT than DNB in accordance with their calculated adsobance constants (Kad), taking into account that degradation process occurs on catalyst surface following a Langmuir-Hinshalwood model. The presence of methyl group in the structure of DNT allows its degradation by oxidative and reductive pathways, while DNB is converted only by reductive route, which also explain the highest DNT degradation efficiency. For highest pollutant concentration tested (3 x 10-4 M), optimum working conditions (0.5 wt.% Fe doped –TiO2 loading of 200 mg/L, pH=7 and 240 min. irradiation time) assures advanced nitroaromatics degradation (ηDNB=89%, ηDNT=94%) and organic nitrogen mineralization (ηDNB=44%, ηDNT=47%).

Keywords: hazardous organic compounds, irradiation, nitroaromatics, photocatalysis

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Authors: Yudo Prasetyo, Hana Sugiastu Firdaus, Diyanah Diyanah

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The phenomenon of the lack of clean water supply in several big cities in Indonesia is a major problem in the development of urban areas. Moreover, in the city of Semarang, the population density and growth of physical development is very high. Continuous and large amounts of underground water (aquifer) exposure can result in a drastically aquifer supply declining in year by year. Especially, the intensity of aquifer use in the fulfilment of household needs and industrial activities. This is worsening by the land subsidence phenomenon in some areas in the Semarang city. Therefore, special research is needed to know the spatial correlation of the impact of decreasing aquifer capacity on the land subsidence phenomenon. This is necessary to give approve that the occurrence of land subsidence can be caused by loss of balance of pressure on below the land surface. One method to observe the correlation pattern between the two phenomena is the application of remote sensing technology based on radar and optical satellites. Implementation of Differential Interferometric Synthetic Aperture Radar (DINSAR) or Small Baseline Area Subset (SBAS) method in SENTINEL-1A satellite image acquisition in 2014-2017 period will give a proper pattern of land subsidence. These results will be spatially correlated with the aquifer-declining pattern in the same time period. Utilization of survey results to 8 monitoring wells with depth in above 100 m to observe the multi-temporal pattern of aquifer change capacity. In addition, the pattern of aquifer capacity will be validated with 2 underground water cavity maps from observation of ministries of energy and natural resources (ESDM) in Semarang city. Spatial correlation studies will be conducted on the pattern of land subsidence and aquifer capacity using overlapping and statistical methods. The results of this correlation will show how big the correlation of decrease in underground water capacity in influencing the distribution and intensity of land subsidence in Semarang city. In addition, the results of this study will also be analyzed based on geological aspects related to hydrogeological parameters, soil types, aquifer species and geological structures. The results of this study will be a correlation map of the aquifer capacity on the decrease in the face of the land in the city of Semarang within the period 2014-2017. So hopefully the results can help the authorities in spatial planning and the city of Semarang in the future.

Keywords: aquifer, differential interferometric synthetic aperture radar (DINSAR), land subsidence, small baseline area subset (SBAS)

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Authors: Yehjune Heo

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As biometric systems become widely deployed, the security of identification systems can be easily attacked by various spoof materials. This paper contributes to finding a reliable and practical anti-spoofing method using Convolutional Neural Networks (CNNs) based on the types of loss functions and optimizers. The types of CNNs used in this paper include AlexNet, VGGNet, and ResNet. By using various loss functions including Cross-Entropy, Center Loss, Cosine Proximity, and Hinge Loss, and various loss optimizers which include Adam, SGD, RMSProp, Adadelta, Adagrad, and Nadam, we obtained significant performance changes. We realize that choosing the correct loss function for each model is crucial since different loss functions lead to different errors on the same evaluation. By using a subset of the Livdet 2017 database, we validate our approach to compare the generalization power. It is important to note that we use a subset of LiveDet and the database is the same across all training and testing for each model. This way, we can compare the performance, in terms of generalization, for the unseen data across all different models. The best CNN (AlexNet) with the appropriate loss function and optimizers result in more than 3% of performance gain over the other CNN models with the default loss function and optimizer. In addition to the highest generalization performance, this paper also contains the models with high accuracy associated with parameters and mean average error rates to find the model that consumes the least memory and computation time for training and testing. Although AlexNet has less complexity over other CNN models, it is proven to be very efficient. For practical anti-spoofing systems, the deployed version should use a small amount of memory and should run very fast with high anti-spoofing performance. For our deployed version on smartphones, additional processing steps, such as quantization and pruning algorithms, have been applied in our final model.

Keywords: anti-spoofing, CNN, fingerprint recognition, loss function, optimizer

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Authors: Sulalit Bandyopadhyay, Muhammad Awais Ashfaq Alvi, Anuvansh Sharma, Wilhelm R. Glomm

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Release of drugs from nanogels and nanogel-based systems can occur under the influence of external stimuli like temperature, pH, magnetic fields and so on. pNIPAm-AAc nanogels respond to the combined action of both temperature and pH, the former being mostly determined by hydrophilic-to-hydrophobic transitions above the volume phase transition temperature (VPTT), while the latter is controlled by the degree of protonation of the carboxylic acid groups. These nanogels based systems are promising candidates in the field of drug delivery. Combining nanogels with magneto-plasmonic nanoparticles (NPs) introduce imaging and targeting modalities along with stimuli-response in one hybrid system, thereby incorporating multifunctionality. Fe@Au core-shell NPs possess optical signature in the visible spectrum owing to localized surface plasmon resonance (LSPR) of the Au shell, and superparamagnetic properties stemming from the Fe core. Although there exist several synthesis methods to control the size and physico-chemical properties of pNIPAm-AAc nanogels, yet, there is no comprehensive study that highlights the dependence of incorporation of one or more layers of NPs to these nanogels. In addition, effective determination of volume phase transition temperature (VPTT) of the nanogels is a challenge which complicates their uses in biological applications. Here, we have modified the swelling-collapse properties of pNIPAm-AAc nanogels, by combining with Fe@Au NPs using different solution based methods. The hydrophilic-hydrophobic transition of the nanogels above the VPTT has been confirmed to be reversible. Further, an analytical method has been developed to deduce the average VPTT which is found to be 37.3°C for the nanogels and 39.3°C for nanogel coated Fe@Au NPs. An opposite swelling –collapse behaviour is observed for the latter where the Fe@Au NPs act as bridge molecules pulling together the gelling units. Thereafter, Cyt C, a model protein drug and L-Dopa, a drug used in the clinical treatment of Parkinson’s disease were loaded separately into the nanogels and nanogel coated Fe@Au NPs, using a modified breathing-in mechanism. This gave high loading and encapsulation efficiencies (L Dopa: ~9% and 70µg/mg of nanogels, Cyt C: ~30% and 10µg/mg of nanogels respectively for both the drugs. The release kinetics of L-Dopa, monitored using UV-vis spectrophotometry was observed to be rather slow (over several hours) with highest release happening under a combination of high temperature (above VPTT) and acidic conditions. However, the release of L-Dopa from nanogel coated Fe@Au NPs was the fastest, accounting for release of almost 87% of the initially loaded drug in ~30 hours. The chemical structure of the drug, drug incorporation method, location of the drug and presence of Fe@Au NPs largely alter the drug release mechanism and the kinetics of these nanogels and Fe@Au NPs coated with nanogels.

Keywords: controlled release, nanogels, volume phase transition temperature, l-dopa

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Authors: Carol Anne Hargreaves

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A key issue in stock investment is how to select representative features for stock selection. The objective of this paper is to firstly determine whether an automated stock investment system, using machine learning techniques, may be used to identify a portfolio of growth stocks that are highly likely to provide returns better than the stock market index. The second objective is to identify the technical features that best characterize whether a stock’s price is likely to go up and to identify the most important factors and their contribution to predicting the likelihood of the stock price going up. Unsupervised machine learning techniques, such as cluster analysis, were applied to the stock data to identify a cluster of stocks that was likely to go up in price – portfolio 1. Next, the principal component analysis technique was used to select stocks that were rated high on component one and component two – portfolio 2. Thirdly, a supervised machine learning technique, the logistic regression method, was used to select stocks with a high probability of their price going up – portfolio 3. The predictive models were validated with metrics such as, sensitivity (recall), specificity and overall accuracy for all models. All accuracy measures were above 70%. All portfolios outperformed the market by more than eight times. The top three stocks were selected for each of the three stock portfolios and traded in the market for one month. After one month the return for each stock portfolio was computed and compared with the stock market index returns. The returns for all three stock portfolios was 23.87% for the principal component analysis stock portfolio, 11.65% for the logistic regression portfolio and 8.88% for the K-means cluster portfolio while the stock market performance was 0.38%. This study confirms that an automated stock investment system using machine learning techniques can identify top performing stock portfolios that outperform the stock market.

Keywords: machine learning, stock market trading, logistic regression, cluster analysis, factor analysis, decision trees, neural networks, automated stock investment system

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Authors: Evrim Colak, Andriy E. Serebryannikov, Pavel V. Usik, Ekmel Ozbay

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Working with a dielectric photonic crystal (PC) structure which does not include surface corrugations, unidirectional transmission and dual-beam splitting are observed under normal incidence as a result of the strong diffractions caused by the embedded defect layer. The defect layer has twice the period of the regular PC segments which sandwich the defect layer. Although the PC has even number of rows, the structural symmetry is broken due to the asymmetric placement of the defect layer with respect to the symmetry axis of the regular PC. The simulations verify that efficient splitting and occurrence of strong diffractions are related to the dispersion properties of the Floquet-Bloch modes of the photonic crystal. Unidirectional and bi-directional splitting, which are associated with asymmetric transmission, arise due to the dominant contribution of the first positive and first negative diffraction orders. The effect of the depth of the defect layer is examined by placing single defect layer in varying rows, preserving the asymmetry of PC. Even for deeply buried defect layer, asymmetric transmission is still valid even if the zeroth order is not coupled. This transmission is due to evanescent waves which reach to the deeply embedded defect layer and couple to higher order modes. In an additional selected performance, whichever surface is illuminated, i.e., in both upper and lower surface illumination cases, incident beam is split into two beams of equal intensity at the output surface where the intensity of the out-going beams are equal for both illumination cases. That is, although the structure is asymmetric, symmetric bidirectional transmission with equal transmission values is demonstrated and the structure mimics the behavior of symmetric structures. Finally, simulation studies including the examination of a coupled-cavity defect for two different permittivity values (close to the permittivity values of GaAs or Si and alumina) reveal unidirectional splitting for a wider band of operation in comparison to the bandwidth obtained in the case of a single embedded defect layer. Since the dielectric materials that are utilized are low-loss and weakly dispersive in a wide frequency range including microwave and optical frequencies, the studied structures should be scalable to the mentioned ranges.

Keywords: asymmetric transmission, beam deflection, blazing, bi-directional splitting, defect layer, dual beam splitting, Floquet-Bloch modes, isofrequency contours, line defect, oblique incidence, photonic crystal, unidirectionality

Procedia PDF Downloads 172