Search results for: channel estimation

Authors: Serag El Hegazi

Abstract:

The development of an assessment methodological framework for local and institutional sustainability is a key factor for future development plans and visions. This paper develops an approach to local and institutional sustainability assessment (ALISA). The ALISA methodology is a methodological framework that assists in the clarification, formulation, preparation, selection, and ranking of key indicators to facilitate the assessment of the level of sustainability at the local and institutional levels in North African and Middle Eastern cities. According to the literature review, this paper formulates a methodological framework, ALISA, which is a combination of the UNCSD (2001) Theme Indicators Framework and the issue-based Framework illustrated by McLaren (1996). The methodological framework has been implemented to formulate, select, and prioritise key indicators that most directly reflect the issues of a case study at the local community and institutional level. Yet, in the meantime, there is a lack of clear indicators and frameworks that can be developed to apply successfully at the local and institutional levels in the MENA Region, particularly in the cities of Benghazi and Amman. This is an essential issue for sustainability development estimation. Therefore, a conceptual framework was developed to be tested as a methodology to collect and classify data. The Approach to Local and Institutional Sustainability Assessment (ALISA) is a methodological framework that was developed to apply to certain cities in the MENA region. The main goal is to develop the ALISA framework to formulate, choose, and prioritize sustainability key indicators, which then can assist in guiding an assessment progress to improve decisions and policymakers towards the development of sustainable cities at the local and institutional level in the city of Benghazi. The conceptual, methodological framework, which supports this research with joint documentary and analysed data in two case studies, including focus-group discussions, semi-structured interviews, and questionnaires, reflects the approach required to develop a combined framework that assists the development of sustainability indicators. To achieve this progress and reach the aim of this paper, which is developing a practical approach for sustainability indicators framework that could be used as a tool to develop local and institutional sustainability indicators, appropriate stages must be applied to propose a set of local and institutional sustainability indicators as follows: Step one: issues clarifications, Step two: objectives formation/analysing of issues and boundaries, Step three: indicators preparation, First list of proposed indictors, Step four: indicator selection, Step five: indicator rating/ranking.

Keywords: sustainability indicators, approach to local and institutional level, ALISA, policymakers

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Authors: Gaurav Gupta, Jitendra Mahakud

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This study focuses on the significance of internal financing constraints on the determination of corporate fixed investments in the case of Indian manufacturing companies. Financing constraints companies which have less internal fund or retained earnings face more transaction and borrowing costs due to imperfections in the capital market. The period of study is 1999-2000 to 2013-2014 and we consider 618 manufacturing companies for which the continuous data is available throughout the study period. The data is collected from PROWESS data base maintained by Centre for Monitoring Indian Economy Pvt. Ltd. Panel data methods like fixed effect and random effect methods are used for the analysis. The Likelihood Ratio test, Lagrange Multiplier test, and Hausman test results conclude the suitability of the fixed effect model for the estimation. The cash flow and liquidity of the company have been used as the proxies for the internal financial constraints. In accordance with various theories of corporate investments, we consider other firm specific variable like firm age, firm size, profitability, sales and leverage as the control variables in the model. From the econometric analysis, we find internal cash flow and liquidity have the significant and positive impact on the corporate investments. The variables like cost of capital, sales growth and growth opportunities are found to be significantly determining the corporate investments in India, which is consistent with the neoclassical, accelerator and Tobin’s q theory of corporate investment. To check the robustness of results, we divided the sample on the basis of cash flow and liquidity. Firms having cash flow greater than zero are put under one group, and firms with cash flow less than zero are put under another group. Also, the firms are divided on the basis of liquidity following the same approach. We find that the results are robust to both types of companies having positive and negative cash flow and liquidity. The results for other variables are also in the same line as we find for the whole sample. These findings confirm that internal financing constraints play a significant role for determination of corporate investment in India. The findings of this study have the implications for the corporate managers to focus on the projects having higher expected cash inflows to avoid the financing constraints. Apart from that, they should also maintain adequate liquidity to minimize the external financing costs.

Keywords: cash flow, corporate investment, financing constraints, panel data method

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Authors: Mahsa Houshdar, Seyed Mehdi Samimi Ardestani , ُSeyed Saeed Sadr

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Introduction: Online Medicare is a method in which parts of a medical process - whether its diagnostics, monitoring or the treatment itself will be done by using online services. This system has been operated in one boy’s high school, one girl’s high school and one high school in deprived aria. Method: At the first step the students registered for using the system. It was not mandatory and not free. They participated in estimating depression scale, anxiety scale and clinical interview by online medical care system. During this estimation, we could find the existence and severity of depression and anxiety in each one of the participants, also we could find the consequent needs of each one, such as supportive therapy in mild depression or anxiety, need to visited by psychologist in moderate cases, need to visited by psychiatrist in moderate-severe cases, need to visited by psychiatrist and psychologist in severe cases and need to perform medical lab examination tests. The lab examination tests were performed on persons specified by the system. The lab examinations were included: serum level of vitamin D, serum level of vitamin B12, serum level of calcium, fasting blood sugar, HbA1c, thyroid function tests and CBC. All of the students were solely treated by vitamins or minerals therapy and/ or treatment of medical problem (such as hypothyroidism). After a few months, we came back to high schools and estimated the existence and severity of depression and anxiety in treated students. With comparing these results, the affectability of the system could be prof. Results: Totally, we operate this project in 1077 participants in 243 of participant, the lab examination test were performed. In girls high schools: the existence and severity of depression significantly deceased (P value= 0.018<0.05 & P value 0.004< 0.05), but results about anxiety was not significant. In boys high schools: the existence and severity of depression significantly decreased (P value= 0.023<0.05 & P value = 0.004< 0.05 & P value= 0.049< 0.05). In boys high schools: the existence and severity of anxiety significantly decreased (P value= 0.041<0.05 & P value = 0.046< 0.05 &) but in one high school results about anxiety was not significant. In high school in deprived area the students did not have any problem paying for participating in the project, but they could not pay for medical lab examination tests. Thus, operation of the system was not possible in deprived area without a sponsor. Conclusion: This online medical system was successful in creating medical and psychiatric profile without attending physician. It was successful in decreasing depression without using antidepressants, but it was partially successful in decreasing anxiety.

Keywords: depression, diabetes, online medicare, vitamin D deficiency

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Authors: M. Turmanauli, T. Todua, O. Gvaberidze, R. Javakhadze, N. Chkhaidze, N. Khatiashvili

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Background: Mining process has the significant influence on human health and quality of life. In recent years the events in Georgia were reflected on the industry working process, especially minimal requirements of labor safety, hygiene standards of workplace and the regime of work and rest are not observed. This situation is often caused by the lack of responsibility, awareness, and knowledge both of workers and employers. The control of working conditions and its protection has been worsened in many of industries. Materials and Methods: For evaluation of the current situation the prospective epidemiological study by face to face interview method was conducted at Georgian “Manganese Zestafoni Ferroalloy Plant” in 2011-2013. 65.7% of employees (1428 bulletin) were surveyed and the incidence rates of temporary disability days were studied. Results: The average length of a temporary disability single accident was studied taking into consideration as sex groups as well as the whole cohort. According to the classes of harmfulness the following results were received: Class 2.0-10.3%; 3.1-12.4%; 3.2-35.1%; 3.3-12.1%; 3.4-17.6%; 4.0-12.5%. Among the employees 47.5% and 83.1% were tobacco and alcohol consumers respectively. According to the age groups and years of work on the base of previous experience ≥50 ages and ≥21 years of work data prevalence respectively. The obtained data revealed increased morbidity rate according to age and years of work. It was found that the bone and articulate system and connective tissue diseases, aggravation of chronic respiratory diseases, ischemic heart diseases, hypertension and cerebral blood discirculation were the leading among the other diseases. High prevalence of morbidity observed in the workplace with not satisfactory labor conditions from the hygienic point of view. Conclusion: According to received data the causes of morbidity are the followings: unsafety labor conditions; incomplete of preventive medical examinations (preliminary and periodic); lack of access to appropriate health care services; derangement of gathering, recording, and analysis of morbidity data. This epidemiological study was conducted at the JSC “Manganese Ferro Alloy Plant” according to State program “ Prevention of Occupational Diseases” (Program code is 35 03 02 05).

Keywords: occupational health, mining process, morbidity level, cerebral blood discirculation

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Authors: Nina Ponikvar, Katja Zajc Kejžar

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While the positive impacts of heritage on a broad societal spectrum have long been recognized and measured, the economic effects of the heritage sector are often less visible and frequently underestimated. At macro level, economic effects are usually studied based on one of the two mainstream approach, i.e. either the multiplier approach or the value chain approach. Consequently, there is limited comparability of the empirical results due to the use of different methodological approach in the literature. Furthermore, it is also not clear on which criteria the used approach was selected. Our aim is to bring the attention to the difference in the scope of effects that are encompassed by the two most frequent methodological approaches to valuation of economic effects of cultural heritage on macroeconomic level, i.e. the multiplier approach and the value chain approach. We show that while the multiplier approach provides a systematic, theory-based view of economic impacts but requires more data and analysis, the value chain approach has less solid theoretical foundations and depends on the availability of appropriate data to identify the contribution of cultural heritage to other sectors. We conclude that the multiplier approach underestimates the economic impact of cultural heritage, mainly due to the narrow definition of cultural heritage in the statistical classification and the inability to identify part of the contribution of cultural heritage that is hidden in other sectors. Yet it is not possible to clearly determine whether the value chain method overestimates or underestimates the actual economic impact of cultural heritage since there is a risk that the direct effects are overestimated and double counted, but not all indirect and induced effects are considered. Accordingly, these two approaches are not substitutes but rather complementary. Consequently, a direct comparison of the estimated impacts is not possible and should not be done due to the different scope. To illustrate the difference of the impact assessment of the cultural heritage, we apply both approaches to the case of Slovenia in the 2015-2022 period and measure the economic impact of cultural heritage sector in terms of turnover, gross value added and employment. The empirical results clearly show that the estimation of the economic impact of a sector using the multiplier approach is more conservative, while the estimates based on value added capture a much broader range of impacts. According to the multiplier approach, each euro in cultural heritage sector generates an additional 0.14 euros in indirect effects and an additional 0.44 euros in induced effects. Based on the value-added approach, the indirect economic effect of the “narrow” heritage sectors is amplified by the impact of cultural heritage activities on other sectors. Accordingly, every euro of sales and every euro of gross value added in the cultural heritage sector generates approximately 6 euros of sales and 4 to 5 euros of value added in other sectors. In addition, each employee in the cultural heritage sector is linked to 4 to 5 jobs in other sectors.

Keywords: economic value of cultural heritage, multiplier approach, value chain approach, indirect effects, slovenia

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Authors: Soheila Sadeghi

Abstract:

Construction projects are complex and often subject to significant cost overruns due to the multifaceted nature of the activities involved. Accurate cost estimation is crucial for effective budget planning and resource allocation. Traditional methods for predicting overruns often rely on expert judgment or analysis of historical data, which can be time-consuming, subjective, and may fail to consider important factors. However, with the increasing availability of data from construction projects, machine learning techniques can be leveraged to improve the accuracy of overrun predictions. This study applied machine learning algorithms to enhance the prediction of cost overruns in a case study of a construction project. The methodology involved the development and evaluation of two machine learning models: Random Forest and Neural Networks. Random Forest can handle high-dimensional data, capture complex relationships, and provide feature importance estimates. Neural Networks, particularly Deep Neural Networks (DNNs), are capable of automatically learning and modeling complex, non-linear relationships between input features and the target variable. These models can adapt to new data, reduce human bias, and uncover hidden patterns in the dataset. The findings of this study demonstrate that both Random Forest and Neural Networks can significantly improve the accuracy of cost overrun predictions compared to traditional methods. The Random Forest model also identified key cost drivers and risk factors, such as changes in the scope of work and delays in material delivery, which can inform better project risk management. However, the study acknowledges several limitations. First, the findings are based on a single construction project, which may limit the generalizability of the results to other projects or contexts. Second, the dataset, although comprehensive, may not capture all relevant factors influencing cost overruns, such as external economic conditions or political factors. Third, the study focuses primarily on cost overruns, while schedule overruns are not explicitly addressed. Future research should explore the application of machine learning techniques to a broader range of projects, incorporate additional data sources, and investigate the prediction of both cost and schedule overruns simultaneously.

Keywords: cost prediction, machine learning, project management, random forest, neural networks

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Authors: Oshma Chakoory, Sophie Comtet-Marre, Pierre Peyret

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Metagenomic classifiers are widely used for the taxonomic profiling of metagenomic data and estimation of taxa relative abundance. Small subunit rRNA genes are nowadays a gold standard for the phylogenetic resolution of complex microbial communities, although the power of this marker comes down to its use as full-length. We benchmarked the performance and accuracy of rRNA-specialized versus general-purpose read mappers, reference-targeted assemblers and taxonomic classifiers. We then built a pipeline called RiboTaxa to generate a highly sensitive and specific metataxonomic approach. Using metagenomics data, RiboTaxa gave the best results compared to other tools (Kraken2, Centrifuge (1), METAXA2 (2), PhyloFlash (3)) with precise taxonomic identification and relative abundance description, giving no false positive detection. Using real datasets from various environments (ocean, soil, human gut) and from different approaches (metagenomics and gene capture by hybridization), RiboTaxa revealed microbial novelties not seen by current bioinformatics analysis opening new biological perspectives in human and environmental health. In a study focused on corals’ health involving 20 metagenomic samples (4), an affiliation of prokaryotes was limited to the family level with Endozoicomonadaceae characterising healthy octocoral tissue. RiboTaxa highlighted 2 species of uncultured Endozoicomonas which were dominant in the healthy tissue. Both species belonged to a genus not yet described, opening new research perspectives on corals’ health. Applied to metagenomics data from a study on human gut and extreme longevity (5), RiboTaxa detected the presence of an uncultured archaeon in semi-supercentenarians (aged 105 to 109 years) highlighting an archaeal genus, not yet described, and 3 uncultured species belonging to the Enorma genus that could be species of interest participating in the longevity process. RiboTaxa is user-friendly, rapid, allowing microbiota structure description from any environment and the results can be easily interpreted. This software is freely available at https://github.com/oschakoory/RiboTaxa under the GNU Affero General Public License 3.0.

Keywords: metagenomics profiling, microbial diversity, SSU rRNA genes, full-length phylogenetic marker

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Authors: Janne Engblom, Elias Oikarinen

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A panel dataset is one that follows a given sample of individuals over time, and thus provides multiple observations on each individual in the sample. Panel data models include a variety of fixed and random effects models which form a wide range of linear models. A special case of panel data models are dynamic in nature. A complication regarding a dynamic panel data model that includes the lagged dependent variable is endogeneity bias of estimates. Several approaches have been developed to account for this problem. In this paper, the panel models were estimated using the Arellano-Bover/Blundell-Bond Generalized method of moments (GMM) estimator which is an extension of the Arellano-Bond model where past values and different transformations of past values of the potentially problematic independent variable are used as instruments together with other instrumental variables. The Arellano–Bover/Blundell–Bond estimator augments Arellano–Bond by making an additional assumption that first differences of instrument variables are uncorrelated with the fixed effects. This allows the introduction of more instruments and can dramatically improve efficiency. It builds a system of two equations—the original equation and the transformed one—and is also known as system GMM. In this study, Finnish housing price dynamics were examined empirically by using the Arellano–Bover/Blundell–Bond estimation technique together with ordinary OLS. The aim of the analysis was to provide a comparison between conventional fixed-effects panel data models and dynamic panel data models. The Arellano–Bover/Blundell–Bond estimator is suitable for this analysis for a number of reasons: It is a general estimator designed for situations with 1) a linear functional relationship; 2) one left-hand-side variable that is dynamic, depending on its own past realizations; 3) independent variables that are not strictly exogenous, meaning they are correlated with past and possibly current realizations of the error; 4) fixed individual effects; and 5) heteroskedasticity and autocorrelation within individuals but not across them. Based on data of 14 Finnish cities over 1988-2012 differences of short-run housing price dynamics estimates were considerable when different models and instrumenting were used. Especially, the use of different instrumental variables caused variation of model estimates together with their statistical significance. This was particularly clear when comparing estimates of OLS with different dynamic panel data models. Estimates provided by dynamic panel data models were more in line with theory of housing price dynamics.

Keywords: dynamic model, fixed effects, panel data, price dynamics

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Authors: Jakub Berčík, Elena Horská, Ľudmila Nagyová

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Nowadays, the point of sale remains one of the few channels of communication which is not oversaturated yet and has great potential for the future. The fact that purchasing decisions are significantly affected by emotions, while up to 75 % of them are implemented at the point of sale, only demonstrates its importance. The share of impulsive purchases is about 60-75 %, depending on the particular product category. Nevertheless, habits predetermine the content of the shopping cart above all and hence in this regard the role of in-store communication is to disrupt the routine and compel the customer to try something new. This is the reason why it is essential to know how to work with this relatively young branch of marketing communication as efficiently as possible. New global trend in this discipline is evaluating the effectiveness of particular tools in the in-store communication. To increase the efficiency it is necessary to become familiar with the factors affecting the customer both consciously and unconsciously, and that is a task for neuromarketing and sensory marketing. It is generally known that the customer remembers the negative experience much longer and more intensely than the positive ones, therefore it is essential for marketers to avoid this negative experience. The final effect of POP (Point of Purchase) or POS (Point of Sale) tools is conditional not only on their quality and design, but also on the location at the point of sale which contributes to the overall positive atmosphere in the store. Therefore, in-store advertising is increasingly in the center of attention and companies are willing to spend even a third of their marketing communication budget on it. The paper deals with a comprehensive, interdisciplinary research of the impact of traditional as well as innovative tools of in-store communication on the attention and emotional state (valence and arousal) of consumers on the food market. The research integrates measurements with eye camera (Eye tracker) and electroencephalograph (EEG) in real grocery stores as well as in laboratory conditions with the purpose of recognizing attention and emotional response among respondents under the influence of selected tools of in-store communication. The object of the research includes traditional (e.g. wobblers, stoppers, floor graphics) and innovative (e.g. displays, wobblers with LED elements, interactive floor graphics) tools of in-store communication in the fresh unpackaged food segment. By using a mobile 16-channel electroencephalograph (EEG equipment) from the company EPOC, a mobile eye camera (Eye tracker) from the company Tobii and a stationary eye camera (Eye tracker) from the company Gazepoint, we observe the attention and emotional state (valence and arousal) to reveal true consumer preferences using traditional and new unusual communication tools at the point of sale of the selected foodstuffs. The paper concludes with suggesting possibilities for rational, effective and energy-efficient combination of in-store communication tools, by which the retailer can accomplish not only captivating and attractive presentation of displayed goods, but ultimately also an increase in retail sales of the store.

Keywords: electroencephalograph (EEG), emotion, eye tracker, in-store communication

Procedia PDF Downloads 385

Authors: Soheila Sadeghi

Abstract:

Construction projects are complex and often subject to significant cost overruns due to the multifaceted nature of the activities involved. Accurate cost estimation is crucial for effective budget planning and resource allocation. Traditional methods for predicting overruns often rely on expert judgment or analysis of historical data, which can be time-consuming, subjective, and may fail to consider important factors. However, with the increasing availability of data from construction projects, machine learning techniques can be leveraged to improve the accuracy of overrun predictions. This study applied machine learning algorithms to enhance the prediction of cost overruns in a case study of a construction project. The methodology involved the development and evaluation of two machine learning models: Random Forest and Neural Networks. Random Forest can handle high-dimensional data, capture complex relationships, and provide feature importance estimates. Neural Networks, particularly Deep Neural Networks (DNNs), are capable of automatically learning and modeling complex, non-linear relationships between input features and the target variable. These models can adapt to new data, reduce human bias, and uncover hidden patterns in the dataset. The findings of this study demonstrate that both Random Forest and Neural Networks can significantly improve the accuracy of cost overrun predictions compared to traditional methods. The Random Forest model also identified key cost drivers and risk factors, such as changes in the scope of work and delays in material delivery, which can inform better project risk management. However, the study acknowledges several limitations. First, the findings are based on a single construction project, which may limit the generalizability of the results to other projects or contexts. Second, the dataset, although comprehensive, may not capture all relevant factors influencing cost overruns, such as external economic conditions or political factors. Third, the study focuses primarily on cost overruns, while schedule overruns are not explicitly addressed. Future research should explore the application of machine learning techniques to a broader range of projects, incorporate additional data sources, and investigate the prediction of both cost and schedule overruns simultaneously.

Keywords: resource allocation, machine learning, optimization, data-driven decision-making, project management

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Authors: Peter C. St. John, Michael F. Crowley, Yannick J. Bomble

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Production of chemicals from engineered organisms in a batch culture typically involves a trade-off between productivity, yield, and titer. However, strategies for strain design typically involve designing mutations to achieve the highest yield possible while maintaining growth viability. Such approaches tend to follow the principle of designing static networks with minimum metabolic functionality to achieve desired yields. While these methods are computationally tractable, optimum productivity is likely achieved by a dynamic strategy, in which intracellular fluxes change their distribution over time. One can use multi-stage fermentations to increase either productivity or yield. Such strategies would range from simple manipulations (aerobic growth phase, anaerobic production phase), to more complex genetic toggle switches. Additionally, some computational methods can also be developed to aid in optimizing two-stage fermentation systems. One can assume an initial control strategy (i.e., a single reaction target) in maximizing productivity - but it is unclear how close this productivity would come to a global optimum. The calculation of maximum theoretical yield in metabolic engineering can help guide strain and pathway selection for static strain design efforts. Here, we present a method for the calculation of a maximum theoretical productivity of a batch culture system. This method follows the traditional assumptions of dynamic flux balance analysis: that internal metabolite fluxes are governed by a pseudo-steady state and external metabolite fluxes are represented by dynamic system including Michealis-Menten or hill-type regulation. The productivity optimization is achieved via dynamic programming, and accounts explicitly for an arbitrary number of fermentation stages and flux variable changes. We have applied our method to succinate production in two common microbial hosts: E. coli and A. succinogenes. The method can be further extended to calculate the complete productivity versus yield Pareto surface. Our results demonstrate that nearly optimal yields and productivities can indeed be achieved with only two discrete flux stages.

Keywords: A. succinogenes, E. coli, metabolic engineering, metabolite fluxes, multi-stage fermentations, succinate

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Authors: Jaspreet Kaur, Parminder Nain, Vipin Saini, Sumitra Dahiya

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Elaeocarpus sphaericus has been traditionally used in the Indian traditional medicine system for the treatment of stress, anxiety, depression, palpitation, epilepsy, migraine and lack of concentration. The study was investigated to evaluate the neurological potential such as anxiolytic, muscle relaxant and sedative activity of methanolic extract of Elaeocarpus sphaericus stem bark (MEESSB) in mice. Preliminary phytochemical screening and acute oral toxicity of MEESSB was carried out by using standard methods. The anxiety was induced by employing Elevated Plus-Maze (EPM), Light and Dark Test (LDT), Open Field Test (OFT) and Social Interaction test (SIT). The motor coordination and sedative effect was also observed by using actophotometer, rota-rod apparatus and ketamine-induced sleeping time, respectively. Animals were treated with different doses of MEESSB (i.e.100, 200, 400 and 800 mg/kg orally) and diazepam (2 mg/kg i.p) for 21 days. Brain neurotransmitters like dopamine, serotonin and nor-epinephrine level were estimated by validated methods. Preliminary phytochemical analysis of the extract revealed the presence of tannins, phytosterols, steroids and alkaloids. In the acute toxicity studies, MEESSB was found to be non-toxic and with no mortality. In anxiolytic studies, the different doses of MEESSB showed a significant (p<0.05) effect on EPM and LDT. In OFT and SIT, a significant (p<0.05) increase in ambulation, rearing and social interaction time was observed. In the case of motor coordination activity, the MEESSB does not cause any significant effect on the latency to fall off from the rotarod bar as compared to the control group. Moreover, no significant effects on ketamine-induced sleep latency and total sleeping time induced by ketamine were observed. Results of neurotransmitter estimation revealed the increased concentration of dopamine, whereas the level of serotonin and nor-epinephrine was found to be decreased in the mice brain, with MEESSB at dose 800 mg/kg only. The study has validated the folkloric use of the plant as an anxiolytic in Indian traditional medicine while also suggesting potential usefulness in the treatment of stress and anxiety without causing sedation.

Keywords: anxiolytic, behavior experiments, brain neurotransmitters, elaeocarpus sphaericus

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Authors: Tiffany Baetens, Sophie Halliez, Luc Buée, Emiliano Pallecchi, Vincent Thomy, Steve Arscott

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We demonstrate here a viable stretchable bio-microelectromechanical systems (BioMEMS) technology for use with biological studies concerned with the effect of high mechanical strains on living cells. An example of this is traumatic brain injury (TBI) where neurons are damaged with physical force to the brain during, e.g., accidents and sports. Robust, miniaturized integrated systems are needed by biologists to be able to study the effect of TBI on neuron cells in vitro. The major challenges in this area are (i) to develop micro, and nanofabrication processes which are based on stretchable substrates and to (ii) create systems which are robust and performant at very high mechanical strain values—sometimes as high as 100%. At the time of writing, such processes and systems were rapidly evolving subject of research and development. The BioMEMS which we present here is composed of an elastomer substrate (low Young’s modulus ~1 MPa) onto which is patterned robust electrodes and insulators. The patterning of the thin films is achieved using standard photolithography techniques directly on the elastomer substrate—thus making the process generic and applicable to many materials’ in based systems. The chosen elastomer used is commercial ‘Sylgard 184’ polydimethylsiloxane (PDMS). It is spin-coated onto a silicon wafer. Multistep ultra-violet based photolithography involving commercial photoresists are then used to pattern robust thin film metallic electrodes (chromium/gold) and insulating layers (parylene) on the top of the PDMS substrate. The thin film metals are deposited using thermal evaporation and shaped using lift-off techniques The BioMEMS has been characterized mechanically using an in-house strain-applicator tool. The system is composed of 12 electrodes with one reference electrode transversally-orientated to the uniaxial longitudinal straining of the system. The electrical resistance of the electrodes is observed to remain very stable with applied strain—with a resistivity approaching that of evaporated gold—up to an interline strain of ~50%. The mechanical characterization revealed some interesting original properties of such stretchable BioMEMS. For example, a Poisson effect induced electrical ‘self-healing’ of cracking was identified. Biocompatibility of the commercial photoresist has been studied and is conclusive. We will present the results of the BioMEMS, which has also characterized living cells with a commercial Multi Electrode Array (MEA) characterization tool (Multi Channel Systems, USA). The BioMEMS enables the cells to be strained up to 50% and then characterized electrically and optically.

Keywords: BioMEMS, elastomer, electrical impedance measurements of living cells, high mechanical strain, microfabrication, stretchable systems, thin films, traumatic brain injury

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Authors: O. E. Akinyemi, P. O. Alege, O. O. Ajayi, L. A. Amaghionyediwe, A. A. Ogundipe

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Nigeria as an oil-producing developing country in Africa is one of the many countries that had been subsidizing consumption of fossil fuel. Despite the numerous advantage of this policy ranging from increased energy access, fostering economic and industrial development, protecting the poor households from oil price shocks, political considerations, among others; they have been found to impose economic cost, wasteful, inefficient, create price distortions discourage investment in the energy sector and contribute to environmental pollution. These negative consequences coupled with the fact that the policy had not been very successful at achieving some of its stated objectives, led to a number of organisations and countries such as the Group of 7 (G7), World Bank, International Monetary Fund (IMF), International Energy Agency (IEA), Organisation for Economic Co-operation and Development (OECD), among others call for global effort towards reforming fossil fuel subsidies. This call became necessary in view of seeking ways to harmonise certain existing policies which may by design hamper current effort at tackling environmental concerns such as climate change. This is in addition to driving a green growth strategy and low carbon development in achieving sustainable development. The energy sector is identified to play a vital role. This study thus investigates the prospects of using fuel subsidy reform as a viable tool in driving an economy that de-emphasizes carbon growth in Nigeria. The method used is the Johansen and Engle-Granger two-step Co-integration procedure in order to investigate the existence or otherwise of a long-run equilibrium relationship for the period 1971 to 2011. Its theoretical framework is rooted in the Environmental Kuznet Curve (EKC) hypothesis. In developing three case scenarios (case of subsidy payment, no subsidy payment and effective subsidy), findings from the study supported evidence of a long run sustainable equilibrium model. Also, estimation results reflected that the first and the second scenario do not significantly influence the indicator of environmental quality. The implication of this is that in reforming fuel subsidy to drive environmental quality for an economy like Nigeria, strong and effective regulatory framework (measure that was interacted with fuel subsidy to yield effective subsidy) is essential.

Keywords: environmental quality, fuel subsidy, green growth, low carbon growth strategy

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Authors: V. I. Kozyavkin, L. F. Shestopalova, T. B. Voloshyn

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Introduction: Rehabilitation of children with Autism is the issue of the day in psychiatry and neurology. It is attributed to constantly increasing quantity of autistic children - Autistic Spectrum Disorders (ASD) Existing rehabilitation approaches in treatment of children with Autism improve their medico- social and social- psychological adjustment. Experience of treatment for different kinds of Autistic disorders in International Clinic of Rehabilitation (ICR) reveals the necessity of complex intensive approach for healing this malady and wider implementation of a Kozyavkin method for treatment of children with ASD. Methods: 19 children aged from 3 to 14 years were examined. They were diagnosed ‘Autism’ (F84.0) with comorbid neurological pathology (from pyramidal insufficiency to para- and tetraplegia). All patients underwent rehabilitation in ICR during two weeks, where INRS approach was used. INRS included methods like biomechanical correction of the spine, massage, physical therapy, joint mobilization, wax-paraffin applications. They were supplemented by art- therapy, ergotherapy, rhythmical group exercises, computer game therapy, team Olympic games and other methods for improvement of motivation and social integration of the child. Estimation of efficacy was conducted using parent’s questioning and done twice- on the onset of INRS rehabilitation course and two weeks afterward. For efficacy assessment of rehabilitation of autistic children in ICR standardized tool was used, namely Autism Treatment Evaluation Checklist (ATEC). This scale was selected because any rehabilitation approaches for the child with Autism can be assessed using it. Results: Before the onset of INRS treatment mean score according to ATEC scale was 64,75±9,23, it reveals occurrence in examined children severe communication, speech, socialization and behavioral impairments. After the end of the rehabilitation course, the mean score was 56,5±6,7, what indicates positive dynamics in comparison to the onset of rehabilitation. Generally, improvement of psychoemotional state occurred in 90% of cases. Most significant changes occurred in the scope of speech (16,5 before and 14,5 after the treatment), socialization (15.1 before and 12,5 after) and behavior (20,1 before and 17.4 after). Conclusion: As a result of INRS rehabilitation course reduction of autistic symptoms was noted. Particularly improvements in speech were observed (children began to spell out new syllables, words), there was some decrease in signs of destructiveness, quality of contact with the surrounding people improved, new skills of self-service appeared. The prospect of the study is further, according to evidence- based medicine standards, deeper examination of INRS and assessment of its usefulness in treatment for Autism and ASD.

Keywords: intensive neurophysiological rehabilitation system (INRS), international clinic od rehabilitation, ASD, rehabilitation

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Authors: Lopamudra D. Satpathy, Bani Chatterjee, Jitendra Mahakud

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The rapid economic growth in terms of output and investment necessitates a substantial growth of Total Factor Productivity (TFP) of firms which is an indicator of an economy’s technological change. The strong empirical relationship between financial sector development and economic growth clearly indicates that firms financing decisions do affect their levels of output via their investment decisions. Hence it establishes a linkage between the financial factors and productivity growth of the firms. To achieve the smooth and continuous economic growth over time, it is imperative to understand the financial channel that serves as one of the vital channels. The theoretical or logical argument behind this linkage is that when the internal financial capital is not sufficient enough for the investment, the firms always rely upon the external sources of finance. But due to the frictions and existence of information asymmetric behavior, it is always costlier for the firms to raise the external capital from the market, which in turn affect their investment sentiment and productivity. This kind of financial position of the firms puts heavy pressure on their productive activities. Keeping in view this theoretical background, the present study has tried to analyze the role of both external and internal financial factors (leverage, cash flow and liquidity) on the determination of total factor productivity of the firms of manufacturing industry and its sub-industries, maintaining a set of firm specific variables as control variables (size, age and disembodied technological intensity). An estimate of total factor productivity of the Indian manufacturing industry and sub-industries is computed using a semi-parametric approach, i.e., Levinsohn- Petrin method. It establishes the relationship between financial factors and productivity growth of 652 firms using a dynamic panel GMM method covering the time period between 1997-98 and 2012-13. From the econometric analyses, it has been found that the internal cash flow has a positive and significant impact on the productivity of overall manufacturing sector. The other financial factors like leverage and liquidity also play the significant role in the determination of total factor productivity of the Indian manufacturing sector. The significant role of internal cash flow on determination of firm-level productivity suggests that access to external finance is not available to Indian companies easily. Further, the negative impact of leverage on productivity could be due to the less developed bond market in India. These findings have certain implications for the policy makers to take various policy reforms to develop the external bond market and easily workout through which the financially constrained companies will be able to raise the financial capital in a cost-effective manner and would be able to influence their investments in the highly productive activities, which would help for the acceleration of economic growth.

Keywords: dynamic panel, financial factors, manufacturing sector, total factor productivity

Procedia PDF Downloads 327

Authors: D. Signore, M. Ferraiuolo, P. Caramuta, O. Petrella, C. Toscano

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The Digital Image Correlation (DIC) is a newly developed optical technique that is spreading in all engineering sectors because it allows the non-destructive estimation of the entire surface deformation without any contact with the component under analysis. These characteristics make the DIC very appealing in all the cases the global deformation state is to be known without using strain gages, which are the most used measuring device. The DIC is applicable to any material subjected to distortion caused by either thermal or mechanical load, allowing to obtain high-definition mapping of displacements and deformations. That is why in the civil and the transportation industry, DIC is very useful for studying the behavior of metallic materials as well as of composite materials. DIC is also used in the medical field for the characterization of the local strain field of the vascular tissues surface subjected to uniaxial tensile loading. DIC can be carried out in the two dimension mode (2D DIC) if a single camera is used or in a three dimension mode (3D DIC) if two cameras are involved. Each point of the test surface framed by the cameras can be associated with a specific pixel of the image, and the coordinates of each point are calculated knowing the relative distance between the two cameras together with their orientation. In both arrangements, when a component is subjected to a load, several images related to different deformation states can be are acquired through the cameras. A specific software analyzes the images via the mutual correlation between the reference image (obtained without any applied load) and those acquired during the deformation giving the relative displacements. In this paper, a metrological characterization of the digital image correlation is performed on aluminum and composite targets both in static and dynamic loading conditions by comparison between DIC and strain gauges measures. In the static test, interesting results have been obtained thanks to an excellent agreement between the two measuring techniques. In addition, the deformation detected by the DIC is compliant with the result of a FEM simulation. In the dynamic test, the DIC was able to follow with a good accuracy the periodic deformation of the specimen giving results coherent with the ones given by FEM simulation. In both situations, it was seen that the DIC measurement accuracy depends on several parameters such as the optical focusing, the parameters chosen to perform the mutual correlation between the images and, finally, the reference points on image to be analyzed. In the future, the influence of these parameters will be studied, and a method to increase the accuracy of the measurements will be developed in accordance with the requirements of the industries especially of the aerospace one.

Keywords: accuracy, deformation, image correlation, mechanical analysis

Procedia PDF Downloads 304

Authors: Jesús Horacio, Alfredo Ollero, Víctor Bouzas-Blanco, Augusto Pérez-Alberti

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Rivers have traditionally been classified, assessed and managed in terms of hydrological, chemical and / or biological criteria. Geomorphological classifications had in the past a secondary role, although proposals like River Styles Framework, Catchment Baseline Survey or Stroud Rural Sustainable Drainage Project did incorporate geomorphology for management decision-making. In recent years many studies have been attracted to the geomorphological component. The geomorphological processes and their associated forms determine the structure of a river system. Understanding these processes and forms is a critical component of the sustainable rehabilitation of aquatic ecosystems. The fluvial auto-classification approach suggests that a river is a self-built natural system, with processes and forms designed to effectively preserve their ecological function (hydrologic, sedimentological and biological regime). Fluvial systems are formed by a wide range of elements with multiple non-linear interactions on different spatial and temporal scales. Besides, the fluvial auto-classification concept is built using data from the river itself, so that each classification developed is peculiar to the river studied. The variables used in the classification are specific stream power and mean grain size. A discriminant analysis showed that these variables are the best characterized processes and forms. The statistical technique applied allows to get an individual discriminant equation for each geomorphological type. The geomorphological classification was developed using sites with high naturalness. Each site is a control point of high ecological and geomorphological quality. The changes in the conditions of the control points will be quickly recognizable, and easy to apply a right management measures to recover the geomorphological type. The study focused on Galicia (NW Spain) and the mapping was made analyzing 122 control points (sites) distributed over eight river basins. In sum, this study provides a method for fluvial geomorphological classification that works as an open and flexible tool underlying the fluvial auto-classification concept. The hydrographic mapping is the visual expression of the results, such that each river has a particular map according to its geomorphological characteristics. Each geomorphological type is represented by a particular type of hydraulic geometry (channel width, width-depth ratio, hydraulic radius, etc.). An alteration of this geometry is indicative of a geomorphological disturbance (whether natural or anthropogenic). Hydrographic mapping is also dynamic because its meaning changes if there is a modification in the specific stream power and/or the mean grain size, that is, in the value of their equations. The researcher has to check annually some of the control points. This procedure allows to monitor the geomorphology quality of the rivers and to see if there are any alterations. The maps are useful to researchers and managers, especially for conservation work and river restoration.

Keywords: fluvial auto-classification concept, mapping, geomorphology, river

Procedia PDF Downloads 363

Authors: Atheer Al-Nuaimi, Harry Evdorides

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Every day many people die or get disabled or injured on roads around the world, which necessitates more specific treatments for transportation safety issues. International road assessment program (iRAP) model is one of the comprehensive road safety models which accounting for many factors that affect road safety in a cost-effective way in low and middle income countries. In iRAP model road safety has been divided into five star ratings from 1 star (the lowest level) to 5 star (the highest level). These star ratings are based on star rating score which is calculated by iRAP methodology depending on road attributes, traffic volumes and operating speeds. The outcome of iRAP methodology are the treatments that can be used to improve road safety and reduce fatalities and serious injuries (FSI) numbers. These countermeasures can be used separately as a single countermeasure or mix as multiple countermeasures for a location. There is general agreement that the adequacy of a countermeasure is liable to consistent losses when it is utilized as a part of mix with different countermeasures. That is, accident diminishment appraisals of individual countermeasures cannot be easily added together. The iRAP model philosophy makes utilization of a multiple countermeasure adjustment factors to predict diminishments in the effectiveness of road safety countermeasures when more than one countermeasure is chosen. A multiple countermeasure correction factors are figured for every 100-meter segment and for every accident type. However, restrictions of this methodology incorporate a presumable over-estimation in the predicted crash reduction. This study aims to adjust this correction factor by developing new models to calculate the effect of using multiple countermeasures on the number of fatalities for a location or an entire road. Regression models have been used to establish relationships between crash frequencies and the factors that affect their rates. Multiple linear regression, negative binomial regression, and Poisson regression techniques were used to develop models that can address the effectiveness of using multiple countermeasures. Analyses are conducted using The R Project for Statistical Computing showed that a model developed by negative binomial regression technique could give more reliable results of the predicted number of fatalities after the implementation of road safety multiple countermeasures than the results from iRAP model. The results also showed that the negative binomial regression approach gives more precise results in comparison with multiple linear and Poisson regression techniques because of the overdispersion and standard error issues.

Keywords: international road assessment program, negative binomial, road multiple countermeasures, road safety

Procedia PDF Downloads 235

Authors: A. A. Purohit, A. Basu, K. A. Chavan, M. D. Kudale

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Many estuarine harbours in the world are facing the problem of siltation in docks, channel entrances, etc. The harbours in India are not an exception and require maintenance dredging to achieve navigable depths for keeping them operable. Hence, dredging is inevitable and is a costly affair. The heavy siltation in docks in well mixed tide dominated estuaries is mainly due to settlement of cohesive sediments in suspension. As such there is a need to have a permanent solution for minimising the siltation in such docks to alter the hydrodynamic flow field responsible for siltation by constructing structures outside the dock. One of such docks on the west coast of India, wherein siltation of about 2.5-3 m/annum prevails, was considered to understand the hydrodynamic flow field responsible for siltation. The dock is situated in such a region where macro type of semi-diurnal tide (range of about 5m) prevails. In order to change the flow field responsible for siltation inside the dock, suitability of Current Deflecting Wall (CDW) outside the dock was studied, which will minimise the sediment exchange rate and siltation in the dock. The well calibrated physical tidal model was used to understand the flow field during various phases of tide for the existing dock in Mumbai harbour. At the harbour entrance where the tidal flux exchanges in/out of the dock, measurements on water level and current were made to estimate the sediment transport capacity. The distorted scaled model (1:400 (H) & 1:80 (V)) of Mumbai area was used to study the tidal flow phenomenon, wherein tides are generated by automatic tide generator. Hydraulic model studies carried out under the existing condition (without CDW) reveal that, during initial hours of flood tide, flow hugs the docks breakwater and part of flow which enters the dock forms number of eddies of varying sizes inside the basin, while remaining part of flow bypasses the entrance of dock. During ebb, flow direction reverses, and part of the flow re-enters the dock from outside and creates eddies at its entrance. These eddies do not allow water/sediment-mass to come out and result in settlement of sediments in dock both due to eddies and more retention of sediment. At latter hours, current strength outside the dock entrance reduces and allows the water-mass of dock to come out. In order to improve flow field inside the dockyard, two CDWs of length 300 m and 40 m were proposed outside the dock breakwater and inline to Pier-wall at dock entrance. Model studies reveal that, during flood, major flow gets deflected away from the entrance and no eddies are formed inside the dock, while during ebb flow does not re-enter the dock, and sediment flux immediately starts emptying it during initial hours of ebb. This reduces not only the entry of sediment in dock by about 40% but also the deposition by about 42% due to less retention. Thus, CDW is a promising solution to significantly reduce siltation in dock.

Keywords: current deflecting wall, eddies, hydraulic model, macro tide, siltation

Procedia PDF Downloads 287

Authors: Staņislavs Gendelis, Andris Jakovičs, Jānis Ratnieks, Aigars Laizāns, Dāvids Vardanjans

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Dehumidification of biogas at the biomass plants is very important to provide the energy efficient burning of biomethane at the outlet. A few methods are widely used to reduce the water content in biogas, e.g. chiller/heat exchanger based cooling, usage of different adsorbents like PSA, or the combination of such approaches. A quite different method of biogas dehumidification is offered and analyzed in this paper. The main idea is to direct the flow of biogas from the plant around it downwards; thus, creating additional insulation layer. As the temperature in gas shell layer around the plant will decrease from ~ 38°C to 20°C in the summer or even to 0°C in the winter, condensation of water vapor occurs. The water from the bottom of the gas shell can be collected and drain away. In addition, another upward shell layer is created after the condensate drainage place on the outer side to further reducing heat losses. Thus, counterflow biogas heat exchanger is created around the biogas plant. This research work deals with the numerical modelling of biogas flow, taking into account heat exchange and condensation on cold surfaces. Different kinds of boundary conditions (air and ground temperatures in summer/winter) and various physical properties of constructions (insulation between layers, wall thickness) are included in the model to make it more general and useful for different biogas flow conditions. The complexity of this problem is fact, that the temperatures in both channels are conjugated in case of low thermal resistance between layers. MATLAB programming language is used for multiphysical model development, numerical calculations and result visualization. Experimental installation of a biogas plant’s vertical wall with an additional 2 layers of polycarbonate sheets with the controlled gas flow was set up to verify the modelling results. Gas flow at inlet/outlet, temperatures between the layers and humidity were controlled and measured during a number of experiments. Good correlation with modelling results for vertical wall section allows using of developed numerical model for an estimation of parameters for the whole biogas dehumidification system. Numerical modelling of biogas counterflow heat exchanger system placed on the plant’s wall for various cases allows optimizing of thickness for gas layers and insulation layer to ensure necessary dehumidification of the gas under different climatic conditions. Modelling of system’s defined configuration with known conditions helps to predict the temperature and humidity content of the biogas at the outlet.

Keywords: biogas dehumidification, numerical modelling, condensation, biogas plant experimental model

Procedia PDF Downloads 544

Authors: Faustina Masocha, Vusani Moyo

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For decades, dividend announcements have been presumed to contain important signals about the future prospects of companies. Similarly, the same has been presumed about management earnings announcements. Despite both dividend and earnings announcements being considered informative, a number of researchers questioned their credibility and found both to contain short-term signals. Pertaining to dividend announcements, some authors argued that although they might contain important information that can result in changes in share prices, which consequently results in the accumulation of abnormal returns, their degree of informativeness is less compared to other signaling tools such as earnings announcements. Yet, this claim in favor has been refuted by other researchers who found the effect of earnings to be transitory and of little value to shareholders as indicated by the little abnormal returns earned during the period surrounding earnings announcements. Considering the above, it is apparent that both dividends and earnings have been hypothesized to have a signaling impact. This prompts one to question which between these two signaling tools is more informative. To answer this question, two follow-up questions were asked. The first question sought to determine the event which results in the most effect on share prices, while the second question focused on the event that influenced trading volume the most. To answer the first question and evaluate the effect that each of these events had on share prices, an event study methodology was employed on a sample made up of the top 10 JSE-listed companies for data collected from 2012 to 2019 to determine if shareholders gained abnormal returns (ARs) during announcement dates. The event that resulted in the most persistent and highest amount of ARs was considered to be more informative. Looking at the second follow-up question, an investigation was conducted to determine if either dividends or earnings announcements influenced trading patterns, resulting in abnormal trading volumes (ATV) around announcement time. The event that resulted in the most ATV was considered more informative. Using an estimation period of 20 days and an event window of 21 days, and hypothesis testing, it was found that announcements pertaining to the increase of earnings resulted in the most ARs, Cumulative Abnormal Returns (CARs) and had a lasting effect in comparison to dividend announcements whose effect lasted until day +3. This solidifies some empirical arguments that the signaling effect of dividends has become diminishing. It was also found that when reported earnings declined in comparison to the previous period, there was an increase in trading volume, resulting in ATV. Although dividend announcements did result in abnormal returns, they were lesser than those acquired during earnings announcements which refutes a number of theoretical and empirical arguments that found dividends to be more informative than earnings announcements.

Keywords: dividend signaling, event study methodology, information content of earnings, signaling theory

Procedia PDF Downloads 162

Authors: Ole-Christian Galbo Engstrøm, Erik Schou Dreier, Birthe Møller Jespersen, Kim Steenstrup Pedersen

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Grain quality analysis is a multi-parameterized problem that includes a variety of qualitative and quantitative parameters such as grain type classification, damage type classification, and nutrient regression. Currently, these parameters require human inspection, a multitude of instruments employing a variety of sensor technologies, and predictive model types or destructive and slow chemical analysis. This paper investigates the feasibility of applying near-infrared hyperspectral imaging (NIR-HSI) to grain quality analysis. For this study two datasets of NIR hyperspectral images in the wavelength range of 900 nm - 1700 nm have been used. Both datasets contain images of sparsely and densely packed grain kernels. The first dataset contains ~87,000 image crops of bulk wheat samples from 63 harvests where protein value has been determined by the FOSS Infratec NOVA which is the golden industry standard for protein content estimation in bulk samples of cereal grain. The second dataset consists of ~28,000 image crops of bulk grain kernels from seven different wheat varieties and a single rye variety. In the first dataset, protein regression analysis is the problem to solve while variety classification analysis is the problem to solve in the second dataset. Deep convolutional neural networks (CNNs) have the potential to utilize spatio-spectral correlations within a hyperspectral image to simultaneously estimate the qualitative and quantitative parameters. CNNs can autonomously derive meaningful representations of the input data reducing the need for advanced preprocessing techniques required for classical chemometric model types such as artificial neural networks (ANNs) and partial least-squares regression (PLS-R). A comparison between different CNN architectures utilizing 2D and 3D convolution is conducted. These results are compared to the performance of ANNs and PLS-R. Additionally, a variety of preprocessing techniques from image analysis and chemometrics are tested. These include centering, scaling, standard normal variate (SNV), Savitzky-Golay (SG) filtering, and detrending. The results indicate that the combination of NIR-HSI and CNNs has the potential to be the foundation for an automatic system unifying qualitative and quantitative grain quality analysis within a single sensor technology and predictive model type.

Keywords: deep learning, grain analysis, hyperspectral imaging, preprocessing techniques

Procedia PDF Downloads 93

Authors: M. L. Gaol, I. W. Mudita

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Indonesia is well known as a country covered by lush tropical rain forests, but in fact, the northeastern part of the country, within the areas geologically known as Lesser Sunda, the dominant vegetation is tropical savanna. Lesser Sunda is a chain of islands located closer to Australia than to islands in the other parts of the country. Among those of islands in the chain which is closes to Australia, and thereby most strongly affected by the hot and dry Australian climate, is the island of Timor, the western part of which belongs to Indonesia and the eastern part is a sovereign state East Timor. Regardless of being the most dominant vegetation cover, tropical savanna in West Timor, especially its understory, is rarely investigated. This research was therefore carried out to investigate the structure, composition and diversity of the understory of this tropical savanna as the basis for looking at the possibility of introducing other spesieis for various purposes. For this research, 14 terrestrial communities representing major types of the existing savannas in West Timor was selected with aid of the most recently available satellite imagery. At each community, one stand of the size of 50 m x 50 m most likely representing the community was as the site of observation for the type of savanna under investigation. At each of the 14 communities, 20 plots of 1 m x 1 m in size was placed at random to identify understory species and to count the total number of individuals and to estimate the cover of each species. Based on such counts and estimation, the important value of each species was later calculated. The results of this research indicated that the understory of savanna in West Timor consisted of 73 understory species. Of this number of species, 18 species are grasses and 55 are non-grasses. Although lower than non-grass species, grass species indeed dominated the savanna as indicated by their number of individuals (65.33 vs 34.67%), species cover (57.80 vs 42.20%), and important value (123.15 vs 76.85). Of the 14 communities, the lowest density of grass was 13.50/m2 and the highest was 417.50/m2. Of 18 grass species found, all were commonly found as agricultural weeds, whereas of 55 non-grass, 10 species were commonly found as agricultural weeds, environmental weeds, or invasive species. In terms of better managing the savanna in the region, these findings provided the basis for planning a more integrated approach in managing such agricultural and environmental weeds as well as invasive species by considering the structure, composition, and species diversity of the understory species existing in each site. These findings also provided the basis for better understanding the flora of the region as a whole and for developing a flora database of West Timor in future.

Keywords: tropical savanna, understory species, integrated management, weedy and invasive species

Procedia PDF Downloads 125

Authors: Yanping Liao, Zenan Wu, Ruigang Zhao

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Frequency diverse array (FDA) beamforming is a technology developed in recent years, and its antenna pattern has a unique angle-distance-dependent characteristic. However, the beam is always required to have strong concentration, high resolution and low sidelobe level to form the point-to-point interference in the concentrated set. In order to eliminate the angle-distance coupling of the traditional FDA and to make the beam energy more concentrated, this paper adopts a multi-carrier FDA structure based on proposed power exponential frequency offset to improve the array structure and frequency offset of the traditional FDA. The simulation results show that the beam pattern of the array can form a dot-shape beam with more concentrated energy, and its resolution and sidelobe level performance are improved. However, the covariance matrix of the signal in the traditional adaptive beamforming algorithm is estimated by the finite-time snapshot data. When the number of snapshots is limited, the algorithm has an underestimation problem, which leads to the estimation error of the covariance matrix to cause beam distortion, so that the output pattern cannot form a dot-shape beam. And it also has main lobe deviation and high sidelobe level problems in the case of limited snapshot. Aiming at these problems, an adaptive beamforming technique based on exponential correction for multi-carrier FDA is proposed to improve beamforming robustness. The steps are as follows: first, the beamforming of the multi-carrier FDA is formed under linear constrained minimum variance (LCMV) criteria. Then the eigenvalue decomposition of the covariance matrix is ​​performed to obtain the diagonal matrix composed of the interference subspace, the noise subspace and the corresponding eigenvalues. Finally, the correction index is introduced to exponentially correct the small eigenvalues ​​of the noise subspace, improve the divergence of small eigenvalues ​​in the noise subspace, and improve the performance of beamforming. The theoretical analysis and simulation results show that the proposed algorithm can make the multi-carrier FDA form a dot-shape beam at limited snapshots, reduce the sidelobe level, improve the robustness of beamforming, and have better performance.

Keywords: adaptive beamforming, correction index, limited snapshot, multi-carrier frequency diverse array, robust

Procedia PDF Downloads 123

Authors: Tamanna Rimi

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A large body of literature has discussed the determinants of migration decision. However, the potential role of individual risk attitudes on migration decision has so far been overlooked. The research on migration literature has studied how the expected income differential influences migration flows for a risk neutral individual. However, migration takes place when there is no expected income differential or even the variability of income appears as lower than in the current location. This migration puzzle motivates a recent trend in the literature that analyzes how attitudes towards risk influence the decision to migrate. However, the significance of risk attitudes on migration decision has been addressed mostly in a theoretical perspective in the mainstream migration literature. The efficient outcome of labor market and overall economy are largely influenced by migration in many countries. Therefore, attitudes towards risk as a determinant of migration should get more attention in empirical studies. To author’s best knowledge, this is the first study that has examined the relationship between relative risk aversion and migration decision in US market. This paper considers movement across United States as a means of migration. In addition, this paper also explores the network effect due to the increasing size of one’s own ethnic group to a source location on the migration decision and how attitudes towards risk vary with network effect. Two ethnic groups (i.e. Asian and Hispanic) have been considered in this regard. For the empirical estimation, this paper uses two sources of data: 1) U.S. census data for social, economic, and health research, 2010 (IPUMPS) and 2) University of Michigan Health and Retirement Study, 2010 (HRS). In order to measure relative risk aversion, this study uses the ‘Two Sample Two-Stage Instrumental Variable (TS2SIV)’ technique. This is a similar method of Angrist (1990) and Angrist and Kruegers’ (1992) ‘Two Sample Instrumental Variable (TSIV)’ technique. Using a probit model, the empirical investigation yields the following results: (i) risk attitude has a significantly large impact on migration decision where more risk averse people are less likely to migrate; (ii) the impact of risk attitude on migration varies by other demographic characteristics such as age and sex; (iii) people with higher concentration of same ethnic households living in a particular place are expected to migrate less from their current place; (iv) the risk attitudes on migration vary with network effect. The overall findings of this paper relating risk attitude, migration decision and network effect can be a significant contribution addressing the gap between migration theory and empirical study in migration literature.

Keywords: migration, network effect, risk attitude, U.S. market

Procedia PDF Downloads 160

Authors: Sakshi Piplani, Ajit Kumar

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Valproic acid (VPA) is the first synthetic therapeutic agent used to treat epileptic disorders, which account for affecting nearly 1% world population. Teratogenicity caused by VPA has prompted the search for next generation drug with better efficacy and lower side effects. Recent studies have posed HDAC8 as direct target of VPA that causes the teratogenic effect in foetus. We have employed molecular dynamics (MD) and docking simulations to understand the binding mode of VPA and their analogues onto HDAC8. A total of twenty 3D-structures of human HDAC8 isoforms were selected using BLAST-P search against PDB. Multiple sequence alignment was carried out using ClustalW and PDB-3F07 having least missing and mutated regions was selected for study. The missing residues of loop region were constructed using MODELLER and energy was minimized. A set of 216 structural analogues (>90% identity) of VPA were obtained from Pubchem and ZINC database and their energy was optimized with Chemsketch software using 3-D CHARMM-type force field. Four major neurotransmitters (GABAt, SSADH, α-KGDH, GAD) involved in anticonvulsant activity were docked with VPA and its analogues. Out of 216 analogues, 75 were selected on the basis of lower binding energy and inhibition constant as compared to VPA, thus predicted to have anti-convulsant activity. Selected hHDAC8 structure was then subjected to MD Simulation using licenced version YASARA with AMBER99SB force field. The structure was solvated in rectangular box of TIP3P. The simulation was carried out with periodic boundary conditions and electrostatic interactions and treated with Particle mesh Ewald algorithm. pH of system was set to 7.4, temperature 323K and pressure 1atm respectively. Simulation snapshots were stored every 25ps. The MD simulation was carried out for 20ns and pdb file of HDAC8 structure was saved every 2ns. The structures were analysed using castP and UCSF Chimera and most stabilized structure (20ns) was used for docking study. Molecular docking of 75 selected VPA-analogues with PDB-3F07 was performed using AUTODOCK4.2.6. Lamarckian Genetic Algorithm was used to generate conformations of docked ligand and structure. The docking study revealed that VPA and its analogues have more affinity towards ‘hydrophobic active site channel’, due to its hydrophobic properties and allows VPA and their analogues to take part in van der Waal interactions with TYR24, HIS42, VAL41, TYR20, SER138, TRP137 while TRP137 and SER138 showed hydrogen bonding interaction with VPA-analogues. 14 analogues showed better binding affinity than VPA. ADMET SAR server was used to predict the ADMET properties of selected VPA analogues for predicting their druggability. On the basis of ADMET screening, 09 molecules were selected and are being used for in-vivo evaluation using Danio rerio model.

Keywords: HDAC8, docking, molecular dynamics simulation, valproic acid

Procedia PDF Downloads 243

Authors: Mustafa Alhamdi

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Industrial application to classify gamma rays and neutron events is investigated in this study using deep machine learning. The identification using a convolutional neural network and recursive neural network showed a significant improvement in predication accuracy in a variety of applications. The ability to identify the isotope type and activity from spectral information depends on feature extraction methods, followed by classification. The features extracted from the spectrum profiles try to find patterns and relationships to present the actual spectrum energy in low dimensional space. Increasing the level of separation between classes in feature space improves the possibility to enhance classification accuracy. The nonlinear nature to extract features by neural network contains a variety of transformation and mathematical optimization, while principal component analysis depends on linear transformations to extract features and subsequently improve the classification accuracy. In this paper, the isotope spectrum information has been preprocessed by finding the frequencies components relative to time and using them as a training dataset. Fourier transform implementation to extract frequencies component has been optimized by a suitable windowing function. Training and validation samples of different isotope profiles interacted with CdTe crystal have been simulated using Geant4. The readout electronic noise has been simulated by optimizing the mean and variance of normal distribution. Ensemble learning by combing voting of many models managed to improve the classification accuracy of neural networks. The ability to discriminate gamma and neutron events in a single predication approach using deep machine learning has shown high accuracy using deep learning. The paper findings show the ability to improve the classification accuracy by applying the spectrogram preprocessing stage to the gamma and neutron spectrums of different isotopes. Tuning deep machine learning models by hyperparameter optimization of neural network models enhanced the separation in the latent space and provided the ability to extend the number of detected isotopes in the training database. Ensemble learning contributed significantly to improve the final prediction.

Keywords: machine learning, nuclear physics, Monte Carlo simulation, noise estimation, feature extraction, classification

Procedia PDF Downloads 143

Authors: Mohamed Dakki, Genevieve Robin, Marie Suet, Abdeljebbar Qninba, Mohamed A. El Agbani, Asmâa Ouassou, Rhimou El Hamoumi, Hichem Azafzaf, Sami Rebah, Claudia Feltrup-Azafzaf, Nafouel Hamouda, Wed a.L. Ibrahim, Hosni H. Asran, Amr A. Elhady, Haitham Ibrahim, Khaled Etayeb, Essam Bouras, Almokhtar Saied, Ashrof Glidan, Bakar M. Habib, Mohamed S. Sayoud, Nadjiba Bendjedda, Laura Dami, Clemence Deschamps, Elie Gaget, Jean-Yves Mondain-Monval, Pierre Defos Du Rau

Abstract:

In biodiversity monitoring, large datasets are becoming more and more widely available and are increasingly used globally to estimate species trends and con- servation status. These large-scale datasets challenge existing statistical analysis methods, many of which are not adapted to their size, incompleteness and heterogeneity. The development of scalable methods to impute missing data in incomplete large-scale monitoring datasets is crucial to balance sampling in time or space and thus better inform conservation policies. We developed a new method based on penalized Poisson models to impute and analyse incomplete monitoring data in a large-scale framework. The method al- lows parameterization of (a) space and time factors, (b) the main effects of predic- tor covariates, as well as (c) space–time interactions. It also benefits from robust statistical and computational capability in large-scale settings. The method was tested extensively on both simulated and real-life waterbird data, with the findings revealing that it outperforms six existing methods in terms of missing data imputation errors. Applying the method to 16 waterbird species, we estimated their long-term trends for the first time at the entire North African scale, a region where monitoring data suffer from many gaps in space and time series. This new approach opens promising perspectives to increase the accuracy of species-abundance trend estimations. We made it freely available in the r package ‘lori’ (https://CRAN.R-project.org/package=lori) and recommend its use for large- scale count data, particularly in citizen science monitoring programmes.

Keywords: biodiversity monitoring, high-dimensional statistics, incomplete count data, missing data imputation, waterbird trends in North-Africa

Procedia PDF Downloads 148

Authors: R. M. Shabbir Ahmed, Mohamed Haneef, A. R. Anwar Khan

Abstract:

Analysis of stresses plays important role in the optimization of structures. Prior stress estimation helps in better design of the products. Composites find wide usage in the industrial and home applications due to its strength to weight ratio. Especially in the air craft industry, the usage of composites is more due to its advantages over the conventional materials. Composites are mainly made of orthotropic materials having unequal strength in the different directions. Composite materials have the drawback of delamination and debonding due to the weaker bond materials compared to the parent materials. So proper analysis should be done to the composite joints before using it in the practical conditions. In the present work, a composite plate with elastic pin is considered for analysis using finite element software Ansys. Basically the geometry is built using Ansys software using top down approach with different Boolean operations. The modelled object is meshed with three dimensional layered element solid46 for composite plate and solid element (Solid45) for pin material. Various combinations are considered to find the strength of the composite joint under uniaxial loading conditions. Due to symmetry of the problem, only quarter geometry is built and results are presented for full model using Ansys expansion options. The results show effect of pin diameter on the joint strength. Here the deflection and load sharing of the pin are increasing and other parameters like overall stress, pin stress and contact pressure are reducing due to lesser load on the plate material. Further material effect shows, higher young modulus material has little deflection, but other parameters are increasing. Interference analysis shows increasing of overall stress, pin stress, contact stress along with pin bearing load. This increase should be understood properly for increasing the load carrying capacity of the joint. Generally every structure is preloaded to increase the compressive stress in the joint to increase the load carrying capacity. But the stress increase should be properly analysed for composite due to its delamination and debonding effects due to failure of the bond materials. When results for an isotropic combination is compared with composite joint, isotropic joint shows uniformity of the results with lesser values for all parameters. This is mainly due to applied layer angle combinations. All the results are represented with necessasary pictorial plots.

Keywords: bearing force, frictional force, finite element analysis, ANSYS

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