Search results for: model of school students’ professional self-determination

Authors: Nicole Pearcy, Jonathan J. Crofts, Nadia Chuzhanova

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In this paper, we employ a directed hypergraph model to investigate the extent to which environmental variability influences the set of available biochemical reactions within a living cell. Such an approach avoids the limitations of the usual complex network formalism by allowing for the multilateral relationships (i.e. connections involving more than two nodes) that naturally occur within many biological processes. More specifically, we extend the concept of network reciprocity to complex hyper-networks, thus enabling us to characterize a network in terms of the existence of mutual hyper-connections, which may be considered a proxy for metabolic network complexity. To demonstrate these ideas, we study 115 metabolic hyper-networks of bacteria, each of which can be classified into one of 6 increasingly varied habitats. In particular, we found that reciprocity increases significantly with increased environmental variability, supporting the view that organism adaptability leads to increased complexities in the resultant biochemical networks.

Keywords: complexity, hypergraphs, reciprocity, metabolism

Procedia PDF Downloads 288

Authors: Sai Maniveer Adapa, Sai Guptha Perla, Adithya Reddy P.

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Thyroid nodules can often be diagnosed with ultrasound imaging, although differentiating between benign and malignant nodules can be challenging for medical professionals. This work suggests a novel approach to increase the precision of thyroid nodule identification by combining machine learning and deep learning. The new approach first extracts information from the ultrasound pictures using a deep learning method known as a convolutional autoencoder. A support vector machine, a type of machine learning model, is then trained using these features. With an accuracy of 92.52%, the support vector machine can differentiate between benign and malignant nodules. This innovative technique may decrease the need for pointless biopsies and increase the accuracy of thyroid nodule detection.

Keywords: thyroid tumor diagnosis, ultrasound images, deep learning, machine learning, convolutional auto-encoder, support vector machine

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Authors: Bibi Somayeh Aliakbari, Niknaz Kachooei, Fatemeh Amiri Najafabadi

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According to the results of contemporary urbanism, social living moved into buildings and the quality of urban space has been declining. But still, there are life in open public space and it is one of reason attendance and activities of people in open public spaces.The purpose of this research is finding reason creation friendly public space in urban spaces and also use these in new urban spaces.The research methodology consisted of a qualitative model based on observation and graphical analysis. In this paper case study is public space historical, moderns in urban scales and local scales in Iran.This paper shows that Existence of friendly public space in cities cause is attendance and activities of people in open public spaces that it is reason the revitalization of public open spaces in cities.

Keywords: public space, public open space, friendly public space, Iran

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Authors: Taha Zakaraia Abdel Wahid

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In the present study, a development of the papers is introduced. The behavior of the unsteady non-equilibrium distribution functions for a rarefied gas mixture under the effect of non-linear thermal radiation field is presented. For the best of our knowledge this is done for the first time at all. The distinction and comparisons between the unsteady perturbed and the unsteady equilibrium velocity distribution functions are illustrated. The equilibrium time for the rarefied gas mixture is determined for the first time. The non-equilibrium thermodynamic properties of the system is investigated. The results are applied to the Argon-Neon binary gas mixture, for various values of both of molar fraction parameters and radiation field intensity. 3D-Graphics illustrating the calculated variables are drawn to predict their behavior and the results are discussed.

Keywords: radiation field, binary gas mixture, exact solutions, travelling wave method, unsteady BGK model, irreversible thermodynamics

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Authors: M. Jathaveda, Joben Leons, G. Vidya

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Reentry from orbit is a critical phase in the entry trajectory. For a non-propulsive ballistic entry, static and dynamic stability play an important role in the trajectory, especially for the safe deployment of parachutes, typically at subsonic Mach numbers. Static stability of flight vehicles are being estimated through CFD techniques routinely. Advances in CFD software as well as computational facilities have enabled the estimation of the dynamic stability derivatives also through CFD techniques. Longitudinal static and dynamic stability of a typical reentry body for subsonic Mach number of 0.6 is predicted using commercial software CFD++ and presented here. Steady state simulations are carried out for α = 2° on an unstructured grid using SST k-ω model. Transient simulation using forced oscillation method is used to compute pitch damping derivatives.

Keywords: stability, typical reentry body, subsonic, static and dynamic

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Authors: Christian Castro Samaniego, Daniel Icaza Alvarez, Juan Portoviejo Brito

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For this research work, hybrid wind-photovoltaic (SHEF) systems were considered as renewable energy sources that take advantage of wind energy and solar radiation to transform into electrical energy. In the present research work, the feasibility of a wind-photovoltaic hybrid generation system was analyzed for the La Tranca tourist viewpoint of the Chordeleg canton in Ecuador. The research process consisted of the collection of data on solar radiation, temperature, wind speed among others by means of a meteorological station. Simulations were carried out in MATLAB/Simulink based on a mathematical model. In the end, we compared the theoretical radiation-power curves and the measurements made at the site.

Keywords: hybrid system, wind turbine, modeling, simulation, validation, experimental data, panel, Ecuador

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Authors: Mohammed Sharaf, Alireza Shokri, Adrian Small, Toby Bridges

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This paper investigates digital transformation challenges in SMEs within the automotive supply chain. A case study approach and participant observation revealed significant data management and process optimization barriers, corroborated by a conceptual model. Stakeholder feedback, visualized through a pie chart, emphasized data management and process efficiency as primary concerns. Recommended strategies include implementing advanced data systems, process simplification, and enhancing digital skills. Despite the single-case study limitation, the findings offer actionable insights for SMEs to leverage Industry 4.0 technologies effectively. This research contributes to the strategic roadmap necessary for SMEs to achieve competitive digital transformation.

Keywords: automotive supply chain, digital transformation, industry 4.0

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Authors: M. Arun, A. Kannan

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Textile industries cater to varied customer preferences and contribute substantially to the economy. However, these textile industries also produce a considerable amount of effluents. Prominent among these are the azo dyes which impart considerable color and toxicity even at low concentrations. Azo dyes are also used as coloring agents in food and pharmaceutical industry. Despite their applications, azo dyes are also notorious pollutants and carcinogens. Popular techniques like photo-degradation, biodegradation and the use of oxidizing agents are not applicable for all kinds of dyes, as most of them are stable to these techniques. Chemical coagulation produces a large amount of toxic sludge which is undesirable and is also ineffective towards a number of dyes. Most of the azo dyes are stable to UV-visible light irradiation and may even resist aerobic degradation. Adsorption has been the most preferred technique owing to its less cost, high capacity and process efficiency and the possibility of regenerating and recycling the adsorbent. Adsorption is also most preferred because it may produce high quality of the treated effluent and it is able to remove different kinds of dyes. However, the adsorption process is influenced by many variables whose inter-dependence makes it difficult to identify optimum conditions. The variables include stirring speed, temperature, initial concentration and adsorbent dosage. Further, the internal diffusional resistance inside the adsorbent particle leads to slow uptake of the solute within the adsorbent. Hence, it is necessary to identify optimum conditions that lead to high capacity and uptake rate of these pollutants. In this work, commercially available activated carbon was chosen as the adsorbent owing to its high surface area. A typical azo dye found in textile effluent waters, viz. the monoazo Acid Orange 10 dye (CAS: 1936-15-8) has been chosen as the representative pollutant. Adsorption studies were mainly focused at obtaining equilibrium and kinetic data for the batch adsorption process at different process conditions. Studies were conducted at different stirring speed, temperature, adsorbent dosage and initial dye concentration settings. The Full Factorial Design was the chosen statistical design framework for carrying out the experiments and identifying the important factors and their interactions. The optimum conditions identified from the experimental model were validated with actual experiments at the recommended settings. The equilibrium and kinetic data obtained were fitted to different models and the model parameters were estimated. This gives more details about the nature of adsorption taking place. Critical data required to design batch adsorption systems for removal of Acid Orange 10 dye and identification of factors that critically influence the separation efficiency are the key outcomes from this research.

Keywords: acid orange 10, activated carbon, optimum adsorption conditions, statistical design

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Authors: Amor Jnifene, Cedric Cocaud

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One of the most constraining problems facing the development of autonomous vehicles is the limitations of current technologies. Guidance and navigation controllers need to be faster and more robust. Communication data links need to be more reliable and secure. For an Unmanned Aerial Vehicles (UAV) to be useful, and fully autonomous, one important feature that needs to be an integral part of the navigation system is autonomous trajectory planning. The work discussed in this paper presents a method for on-line trajectory planning for UAV’s. This method takes into account various constraints of different types including specific vectors of approach close to target points, multiple objectives, and other constraints related to speed, altitude, and obstacle avoidance. The trajectory produced by the proposed method ensures a smooth transition between different segments, satisfies the minimum curvature imposed by the dynamics of the UAV, and finds the optimum velocity based on available atmospheric conditions. Given a set of objective points and waypoints a skeleton of the trajectory is constructed first by linking all waypoints with straight segments based on the order in which they are encountered in the path. Secondly, vectors of approach (VoA) are assigned to objective waypoints and their preceding transitional waypoint if any. Thirdly, the straight segments are replaced by 3D curvilinear trajectories taking into account the aircraft dynamics. In summary, this work presents a method for on-line 3D trajectory generation (TG) of Unmanned Aerial Vehicles (UAVs). The method takes as inputs a series of waypoints and an optional vector of approach for each of the waypoints. Using a dynamic model based on the performance equations of fixed wing aircrafts, the TG computes a set of 3D parametric curves establishing a course between every pair of waypoints, and assembling these sets of curves to construct a complete trajectory. The algorithm ensures geometric continuity at each connection point between two sets of curves. The geometry of the trajectory is optimized according to the dynamic characteristics of the aircraft such that the result translates into a series of dynamically feasible maneuvers. In summary, this work presents a method for on-line 3D trajectory generation (TG) of Unmanned Aerial Vehicles (UAVs). The method takes as inputs a series of waypoints and an optional vector of approach for each of the waypoints. Using a dynamic model based on the performance equations of fixed wing aircraft, the TG computes a set of 3D parametric curves establishing a course between every pair of waypoints, and assembling these sets of curves to construct a complete trajectory. The algorithm ensures geometric continuity at each connection point between two sets of curves. The geometry of the trajectory is optimized according to the dynamic characteristics of the aircraft such that the result translates into a series of dynamically feasible maneuvers.

Keywords: trajectory planning, unmanned autonomous air vehicle, vector of approach, waypoints

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Authors: Kolade Olawande Owoeye, George R. S. Weir

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The widespread of terrorism and extremism activities on the internet has become a major threat to the government and national securities due to their potential dangers which have necessitated the need for intelligence gathering via web and real-time monitoring of potential websites for extremist activities. However, the manual classification for such contents is practically difficult or time-consuming. In response to this challenge, an automated classification system called composite technique was developed. This is a computational framework that explores the combination of both semantics and syntactic features of textual contents of a web. We implemented the framework on a set of extremist webpages dataset that has been subjected to the manual classification process. Therein, we developed a classification model on the data using J48 decision algorithm, this is to generate a measure of how well each page can be classified into their appropriate classes. The classification result obtained from our method when compared with other states of arts, indicated a 96% success rate in classifying overall webpages when matched against the manual classification.

Keywords: extremist, web pages, classification, semantics, posit

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Authors: Asma Ben Yaghlane, Mohamed Naceur Azaiez

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In this paper, we treat a model that falls in the area of protecting targeted systems from intelligent threats including terrorism. We introduce the concept of system survivability, in the context of continuous attacks, as the probability that a system under attack will continue operation up to some fixed time t. We define a constant attack rate (CAR) process as an attack on a targeted system that follows an exponential distribution. We consider the superposition of several CAR processes. From the attacker side, we determine the optimal attack strategy that minimizes the system survivability. We also determine the optimal strengthening strategy that maximizes the system survivability under limited defensive resources. We use operations research techniques to identify optimal strategies of each antagonist. Our results may be used as interesting starting points to develop realistic protection strategies against intentional attacks.

Keywords: CAR processes, defense/attack strategies, exponential failure, survivability

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Authors: Dara Satyadileep, Abdallah S. Berrouk

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Selective absorption of Hydrogen Sulphide (H2S) using methyldiethanol amine (MDEA) has become a point of interest as means of minimizing capital and operating costs of gas sweetening plants. This paper discusses the prominence of optimum design of column internals to best achieve H2S selectivity using MDEA. To this end, a kinetics-based process simulation model has been developed for a commercial gas sweetening unit. Trends of sweet gas H2S & CO2 contents as function of fraction active area (and hence residence time) have been explained through analysis of interdependent heat and mass transfer phenomena. Guidelines for column internals design in order to achieve desired degree of H2S selectivity are provided. Also the effectiveness of various operating conditions in achieving H2S selectivity for an industrial absorber with fixed internals is investigated.

Keywords: gas sweetening, H2S selectivity, methyldiethanol amine, process simulation, residence time

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Authors: Purnomo Yustianto, Robin Doss, Novianto B. Kurniawan Suhardi

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Since its inception, the design activities of Service Oriented Architecture (SOA) has been guided with aspects from the Service Design Principles (SDP), such as cohesion, granularity, loose coupling, discoverability, and autonomy, etc. The goal of this paper is two folds. The first is to examine the position of SDP within the context of software quality, and the second is to reframe the aspects of SDP into a more concise terms and relations. This paper is divided into four parts, in which after the introduction, a review on related software quality is provided to determine the quality context of SDP. The third part reviews the original SDP and offers a relation model among the SDP aspects. The fourth part explores the design quality metrics available for SOA and proposes a relationship representing the design quality. Among the aspects of design principles, the cohesion and coupling aspect is determined to be the two important aspects for achieving reusability of a service.

Keywords: SOA, software quality, service design principle, reusability, cohesion, coupling

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Authors: Zhaoming Yang, Qi Xiang, Qian He, Michael Havbro Faber, Enrico Zio, Huai Su, Jinjun Zhang

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Resilience is one of the key factors for system safety assessment and optimization, and resilience studies of natural gas pipeline systems (NGPS), especially in terms of process descriptions, are still being explored. Based on the three main stages, which are function loss process, recovery process, and waiting process, the paper has built functions and models which are according to the practical characteristics of NGPS and mainly analyzes the characteristics of deterministic interruptions. The resilience of NGPS also considers the threshold of the system function or users' satisfaction. The outcomes, which quantify the resilience of NGPS in different evaluation views, can be combined with the max flow and shortest path methods, help with the optimization of extra gas supplies and gas routes as well as pipeline maintenance strategies, the quick analysis of disturbance effects and the improvement of NGPS resilience evaluation accuracy.

Keywords: natural gas pipeline system, resilience, process modeling, deterministic disturbance

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Authors: Ishu Surender, M. Amez Khair, Ishan Singh

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Urbanization is a multidimensional phenomenon. It is an indication of the long-term process for the shift of economics to industrial from rural. The significance of urbanization in modernization, socio-economic development, and poverty eradication is relevant in modern times. This paper aims to study the urbanization index model in the capital of India, Delhi using aspects such as demographic aspect, infrastructural development aspect, and economic development aspect. The urbanization index of all the nine districts of Delhi will be determined using multiple parameters such as population density and the availability of health and education facilities. The definition of the urban area varies from city to city and requires periodic classification which makes direct comparisons difficult. The urbanization index calculated in this paper can be employed to measure the urbanization of a district and compare the level of urbanization in different districts.

Keywords: multiparameter, population density, multiple regression, normalized urbanization index

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Authors: Ismael Ávila, José Reynaldo F. Filho, Vasco Varanda Picchi

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The ever-growing consumer awareness of food provenance in Brazil is driving the creation of a trusted ecosystem around the animal protein supply chain. The traceability and accountability requirements of such an ecosystem demand a blockchain layer to strengthen the weak links in that chain. For that, direct involvement of the companies in the blockchain transactions, including as validator nodes of the network, implies formalizing a partnership with the consortium behind the ecosystem. Yet, their compliance standards usually require that a formal governance structure is in place before they agree with any membership terms. In light of such a strategic role of blockchain governance, the paper discusses a framework for tailoring a governance model for a blockchain-based solution aimed at the meat supply chain and evaluates principles and attributes in terms of their relevance to the development of a robust trust ecosystem.

Keywords: blockchain, governance, trust ecosystem, supply chain, traceability

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Authors: Yuang Guo, Dewancker Bart

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The primary energy consumption of buildings throughout China was approximately 814 million tons of coal equivalents in 2014, which accounts for 19.12% of China's total primary energy consumption. Also, the energy consumption of public buildings takes a bigger share than urban residential buildings and rural residential buildings among the total energy consumption. To improve the level of energy demand, various design parameters were chosen. Meanwhile, a series of simulations by Energy Plus (EP-Launch) is performed using a base case model established in Open Studio. Through the results, 16%-23% of total energy demand reductions can be found in the severe cold zone of China, and it can also provide a reference for the architectural design of other similar climate zones.

Keywords: energy consumption, design parameters, indoor thermal comfort, simulation study, severe cold climate zone

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Authors: Ummu Salma Al Azizah, Herri Mulyono, Anisa Mauliata Suryana

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The significance of corporate governance regarding to the agency problem have been transparent. This study examine the impact of corporate governance on the performance of Islamic banking in Indonesia. By using fixed effect model and added some control variable, the current study try to explore the correlation between the theoretical framework on corporate governance, such as agency theory and risk management theory. The bank performance (Return on Asset and Return on Equity) which are operational performance and financial performance. And Corporate governance based on Board size, CEO duality, Audit committee and Shariah supervisory board. The limitation of this study only focus on the Islamic banks performance from year 2015 to 2020. The study fill the gap in the literature by addressing the issue of corporate governance on Islamic banks performance in Indonesia.

Keywords: corporate governance, financial performance, islamic banks, listed companies, Indonesia

Procedia PDF Downloads 111

Authors: Julius Oluwayomi Oluwadamilare, Michael Olutayo Olatunji

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In the African traditional societies, women have always played the role of a teacher, albeit informally. This is evident in the upbringing of their babies. As mothers, they also serve as the first teachers to teach their wards lessons through day-to-day activities. Furthermore, women always play the role of a musician during naming ceremonies, in the singing of lullabies, during initiation rites of adolescent boys and girls into adulthood, and in preparing their children especially daughters (and sons) for marriage. They also perform this role during religious and cultural activities, chieftaincy title/coronation ceremonies, singing of dirges during funeral ceremonies, and so forth. This traditional role of the African/Nigerian women puts them at a vantage point to contribute maximally to the teaching and learning of music at every level of education. The need for more women in the field of music education in Nigeria cannot be overemphasized. Today, gender equality is a major discourse in most countries of the world, Nigeria inclusive. Statistical data in the field of education and music education reveal the high ratio of male teachers/lecturers over their female counterparts in Nigerian tertiary institutions. The percentage is put at 80% Male and a distant 20% Female! This paper, therefore, examines feminine gender in Nigerian music education by tracing the involvement of women in musical practice from the pre-colonial to the post-colonial periods. The study employed both primary and secondary sources of data collection. The primary source included interviews conducted with 19 music lecturers from 8 purposively selected tertiary institutions from 4 geo-political zones of Nigeria. In addition, observation method was employed in the selected institutions. The results show, inter alia, that though there is a remarkable improvement in the rate of admission of female students into the music programme of Nigerian tertiary institutions, there is still an imbalance in the job placement in these institutions especially in the Colleges of Education which is the main focus of this research. Religious and socio-cultural factors are highly traceable to this development. This paper recommends the need for more female music teachers to be employed in the Nigerian tertiary institutions in line with the provisions stated in the Millennium Development Goals (MDGs) of the Federal Republic of Nigeria.

Keywords: gender, education, music, women

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Authors: Robert Caulk

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A generalized framework for adaptive machine learning deployments in algorithmic trading is introduced, tested, and released as open-source code. The presented software aims to test the hypothesis that recent data contains enough information to form a probabilistically favorable short-term price prediction. Further, the framework contains various adaptive machine learning techniques that are geared toward generating profit during strong trends and minimizing losses during trend changes. Results demonstrate that this adaptive machine learning approach is capable of capturing trends and generating profit. The presentation also discusses the importance of defining the parameter space associated with the dynamic training data-set and using the parameter space to identify and remove outliers from prediction data points. Meanwhile, the generalized architecture enables common users to exploit the powerful machinery while focusing on high-level feature engineering and model testing. The presentation also highlights common strengths and weaknesses associated with the presented technique and presents a broad range of well-tested starting points for feature set construction, target setting, and statistical methods for enforcing risk management and maintaining probabilistically favorable entry and exit points. The presentation also describes the end-to-end data processing tools associated with FreqAI, including automatic data fetching, data aggregation, feature engineering, safe and robust data pre-processing, outlier detection, custom machine learning and statistical tools, data post-processing, and adaptive training backtest emulation, and deployment of adaptive training in live environments. Finally, the generalized user interface is also discussed in the presentation. Feature engineering is simplified so that users can seed their feature sets with common indicator libraries (e.g. TA-lib, pandas-ta). The user also feeds data expansion parameters to fill out a large feature set for the model, which can contain as many as 10,000+ features. The presentation describes the various object-oriented programming techniques employed to make FreqAI agnostic to third-party libraries and external data sources. In other words, the back-end is constructed in such a way that users can leverage a broad range of common regression libraries (Catboost, LightGBM, Sklearn, etc) as well as common Neural Network libraries (TensorFlow, PyTorch) without worrying about the logistical complexities associated with data handling and API interactions. The presentation finishes by drawing conclusions about the most important parameters associated with a live deployment of the adaptive learning framework and provides the road map for future development in FreqAI.

Keywords: machine learning, market trend detection, open-source, adaptive learning, parameter space exploration

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Authors: Faruk Ortes, Baris Sayin, Tarik Serhat Bozkurt, Cemil Akcay

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The technique using near-surface mounted (NSM) carbon fiber-reinforced polymer (CFRP) composites has proved to be an reliable strengthening technique. However, the effects of different parameters for the use of NSM CFRP are not fully developed yet. This study focuses on the development of a numerical modeling that can predict the behavior of reinforced concrete (RC) beams strengthened with NSM FRP rods exposed to bending loading and the efficiency of various parameters such as CFRP rod size and filling material type are evaluated by using prepared models. For this purpose, three different models are developed and implemented in the ANSYS® software using Finite Element Analysis (FEA). The numerical results indicate that CFRP rod size and filling material type are significant factors in the behavior of the analyzed RC beams.

Keywords: numerical model, FEA, RC beam, NSM technique, CFRP rod, filling material

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Authors: Terence Soule, Tami Al Ghamdi

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To study how to transfer knowledge from multiple source problems to the target problem, we modeled the Transfer Learning (TL) process using Genetic Algorithms as the model solver. TL is the process that aims to transfer learned data from one problem to another problem. The TL process aims to help Machine Learning (ML) algorithms find a solution to the problems. The Genetic Algorithms (GA) give researchers access to information that we have about how the old problem is solved. In this paper, we have five different source problems, and we transfer the knowledge to the target problem. We studied different scenarios of the target problem. The results showed combined knowledge from multiple source problems improves the GA performance. Also, the process of combining knowledge from several problems results in promoting diversity of the transferred population.

Keywords: transfer learning, genetic algorithm, evolutionary computation, source and target

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Authors: Carlos M. Parra, Matthew Sutherland, Ranjita Poudel

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Reduced mental-bandwidth related to low socioeconomic status (low-SES) might lead to impulsivity and risk-taking behavior, which poses as a major hurdle towards asset building (savings) behavior. Understanding the relationship between risk-related personality metrics as well as laboratory risk behavior and real-life savings behavior can help facilitate the development of effective asset building programs, which are vital for mitigating financial vulnerability and income inequality. As such, this study explored the relationship between personality metrics, laboratory behavior in a risky decision-making task and real-life asset building (savings) behaviors among individuals with low-SES from Miami, Florida (FL). Study participants (12 male, 15 female) included racially and ethnically diverse adults (mean age 41.22 ± 12.65 years), with incomplete higher education (18% had High School Diploma, 30% Associates, and 52% Some College), and low annual income (mean $13,872 ± $8020.43). Participants completed eight self-report surveys and played a widely used risky decision-making paradigm called the Balloon Analogue Risk Task (BART). Specifically, participants played three runs of BART (20 trials in each run; total 60 trials). In addition, asset building behavior data was collected for 24 participants who opened and used savings accounts and completed a 6-month savings program that involved monthly matches, and a final reward for completing the savings program without any interim withdrawals. Each participant’s total savings at the end of this program was the main asset building indicator considered. In addition, a new effective use of average pump bet (EUAPB) indicator was developed to characterize each participant’s ability to place winning bets. This indicator takes the ratio of each participant’s total BART earnings to average pump bet (APB) in all 60 trials. Our findings indicated that EUAPB explained more than a third of the variation in total savings among participants. Moreover, participants who managed to obtain BART earnings of at least 30 cents out of their APB, also tended to exhibit better asset building (savings) behavior. In particular, using this criterion to separate participants into high and low EUAPB groups, the nine participants with high EUAPB (mean BART earnings of 35.64 cents per APB) ended up with higher mean total savings ($255.11), while the 15 participants with low EUAPB (mean BART earnings of 22.50 cents per APB) obtained lower mean total savings ($40.01). All mean differences are statistically significant (2-tailed p  .0001) indicating that the relation between higher EUAPB and higher total savings is robust. Overall, these findings can help refine asset building interventions implemented by policy makers and practitioners interested in reducing financial vulnerability among low-SES population. Specifically, by helping identify individuals who are likely to readily take advantage of savings opportunities (such as matched savings programs) and avoiding the stipulation of unnecessary and expensive financial coaching programs to these individuals. This study was funded by J.P. Morgan Chase (JPMC) and carried out by scientists from Florida International University (FIU) in partnership with Catalyst Miami.

Keywords: balloon analogue risk task (BART), matched savings programs, asset building capability, low-SES participants

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Authors: S. Turkan

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In this paper, the Turkey’s Top 500 Industrial Enterprises data in 2014 were analyzed by data envelopment analysis. Data envelopment analysis is used to detect efficient decision-making units such as universities, hospitals, schools etc. by using inputs and outputs. The decision-making units in this study are enterprises. To detect efficient enterprises, some financial ratios are determined as inputs and outputs. For this reason, financial indicators related to productivity of enterprises are considered. The efficient foreign weighted owned capital enterprises are detected via super efficiency model. According to the results, it is said that Mercedes-Benz is the most efficient foreign weighted owned capital enterprise in Turkey.

Keywords: data envelopment analysis, super efficiency, logistic regression, financial ratios

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Authors: Min-Tsai Lai, Taqwa Hariguna

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This paper presents a periodical replacement model for a system, considering the concept of single and cumulative repair cost limits simultaneously. The failures are divided into two types. Minor failure can be corrected by minimal repair and serious failure makes the system breakdown completely. When a minor failure occurs, if the repair cost is less than a single repair cost limit L1 and the accumulated repair cost is less than a cumulative repair cost limit L2, then minimal repair is executed, otherwise, the system is preventively replaced. The system is also replaced at time T or at serious failure. The optimal period T minimizing the long-run expected cost per unit time is verified to be finite and unique under some specific conditions.

Keywords: repair-cost limit, cumulative repair-cost limit, minimal repair, periodical replacement policy

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Authors: Ai Fujii, Kan Shimazaki

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Bicycle users must ride on the street according the law in Japan, but in practice, many bicycle users ride on the sidewalk. Drivers generally feel that bicycles riding in the street are in the way. In contrast, pedestrians generally feel that bicycles riding on the sidewalk are in the way. That seems to make sense. What, then, is the difference between riding a bicycle on the sidewalk or in the street by usual traveling means. We made 3D computer graphics models of pedestrians, a car, and a bicycle at an intersection. The bicycle was positioned to choose between advancing to the sidewalk or the street after a few seconds. We then made a 2D stimulus picture by changing the point of view of the 3DCG model pictures. Attitudes were surveyed using this 2D stimulus picture, and we compared attitudes between three groups, people traveling by car, on foot, or by bicycle. Here we report the survey result.

Keywords: bicycle, sidewalk, pedestrians, driver, intersection, safety

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Authors: Tushar Bhardwaj

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Internet of Things (IoT) possesses a dynamic network where the network nodes (mobile devices) are added and removed constantly and randomly, hence the traffic distribution in the network is quite variable and irregular. The basic but very important part in any network is route searching. We have many conventional route searching algorithms like link-state, and distance vector algorithms but they are restricted to the static point to point network topology. In this paper we propose a model that uses the Ant Colony Algorithm for route searching. It is dynamic in nature and has positive feedback mechanism that conforms to the route searching. We have also embedded the concept of Non-Deterministic Finite Automata [NDFA] minimization to reduce the network to increase the performance. Results show that Ant Colony Algorithm gives the shortest path from the source to destination node and NDFA minimization reduces the broadcasting storm effectively.

Keywords: routing, ant colony algorithm, NDFA, IoT

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Authors: Cheila Pereira, Sara C. Cunha, Miguel A. Faria, José O. Fernandes

Abstract:

The increasing world population brings several concerns, one of which is food security and sustainability. To meet this challenge, aquaculture, the farming of aquatic animals and plants, including fish, mollusks, bivalves, and algae, has experienced sustained growth and development in recent years. Recent advances in this industry have focused on reducing its economic and environmental costs, for example, the substitution of protein sources in fish feed. Plant-based proteins are now a common approach, and while it is a greener alternative to animal-based proteins, there are some disadvantages, such as their putative content and intoxicants such as mycotoxins. These are naturally occurring plant contaminants, and their exposure in fish can cause health problems, stunted growth or even death, resulting in economic losses for the producers and health concerns for the consumers. Different works have demonstrated the presence of both AFB1 (aflatoxin B1) and ENNB1 (enniatin B1) in fish feed and their capacity to be absorbed and bioaccumulate in the fish organism after digestion, further reaching humans through fish ingestion. The aim of this work was to evaluate the bioaccessibility of both mycotoxins in samples of Sparus aurata muscle using a static digestion model based on the INFOGEST protocol. The samples were subjected to different cooking procedures – raw, grilled and fried – and different seasonings – none, thyme and ginger – in order to evaluate their potential reduction effect on mycotoxins bioaccessibility, followed by the evaluation of the intestinal transport of both compounds with an in vitro cell model composed of Caco-2/HT-29 co-culture monolayers, simulating the human intestinal epithelium. The bioaccessible fractions obtained in the digestion studies were used in the transport studies for a more realistic approach to bioavailability evaluation. Results demonstrated the effect of the use of different cooking procedures and seasoning on the toxin's bioavailability. Sparus aurata was chosen in this study for its large production in aquaculture and high consumption in Europe. Also, with the continued evolution of fish farming practices and more common usage of novel feed ingredients based on plants, there is a growing concern about less studied contaminants in aquaculture and their consequences for human health. In pair with greener advances in this industry, there is a convergence towards alternative research methods, such as in vitro applications. In the case of bioavailability studies, both in vitro digestion protocols and intestinal transport assessment are excellent alternatives to in vivo studies. These methods provide fast, reliable and comparable results without ethical restraints.

Keywords: AFB1, aquaculture, bioaccessibility, ENNB1, intestinal transport.

Procedia PDF Downloads 55

Authors: Leyla Sattarzadeh, Shahin Fathi Molk Kian, Maghsoud Peeri, Mohammadali Azarbaijani, Hasan Matin Homaee

Abstract:

Inflammation by various mechanisms may cause atherosclerosis. Systemic circulating inflammatory markers such as C-reactive protein (CRP), pro-inflammatory cytokines such as Interleukin-6 (IL-6), vascular inflammatory markers as adhesion molecules like Intracellular Adhesion Molecule-1 (ICAM-1) and Vascular Cell Adhesion Molecule-1 (VCAM-1) are the predictors of cardiovascular diseases. Regarding the conflicting results about the effect of different patterns of resistance exercise training on these inflammatory markers, present study aimed to examine the effect of different patterns of eight week resistance exercise training on CRP, IL-6, ICAM-1 and VCAM-1 levels in healthy untrained women. 56 healthy volunteered untrained female university students (aged: 21 ± 3 yr., Body Mass Index: 21.5 ± 3.5 kg/m²) were selected purposefully and divided into four groups. At the end of training protocol and after subject drop during the protocol, upper body exercise training (n=11), lower body (n=12) and whole body resistance exercise training group (n=11) completed the eight weeks of training period although the control group (n=7) did anything. Blood samples gathered pre and post-experimental period and CRP, IL-6, ICAM-1 and VCAM-1 levels were evaluated using special laboratory kits, then the difference of pre and post values of each indices analyzed using one-way analysis of variance (α < 0.05). The results of one way ANOVA for difference of pre and post values of CRP, ICAM-1 and VCAM-1 showed no significant changes due to the exercise training, but there were significant differences between groups about IL-6. Tukey post- hoc test indicated that there is significant difference between the differences of pre and post values of IL-6 between lower body exercise training group and control group, and eight weeks of lower body exercise training lead to significant changes in IL-6 values. There were no changes in anthropometric indices. The findings show that the different patterns of upper, lower and whole body exercise training by involving the different amounts of muscles altered the IL-6 values in lower body exercise training group probably because of engaging the bigger amount of muscles, but showed any significant changes about CRP, ICAM-1 and VCAM-1 probably due to intensity and duration of exercise or the lower levels of these markers at baseline of healthy people.

Keywords: resistance training, C-reactive protein, interleukin-6, intracellular adhesion molecule-1, vascular cell adhesion molecule-1

Procedia PDF Downloads 127

Authors: James Moir

Abstract:

The mainstay of Big Data is prediction in that it allows practitioners, researchers, and policy analysts to predict trends based upon the analysis of large and varied sources of data. These can range from changing social and political opinions, patterns in crimes, and consumer behaviour. Big Data has therefore shifted the criterion of success in science from causal explanations to predictive modelling and simulation. The 19th-century science sought to capture phenomena and seek to show the appearance of it through causal mechanisms while 20th-century science attempted to save the appearance and relinquish causal explanations. Now 21st-century science in the form of Big Data is concerned with the prediction of appearances and nothing more. However, this pulls social science back in the direction of a more rule- or law-governed reality model of science and away from a consideration of the internal nature of rules in relation to various practices. In effect Big Data offers us no more than a world of surface appearance and in doing so it makes disappear any context-specific conceptual sensitivity.

Keywords: big data, appearance, disappearance, surface, epistemology

Procedia PDF Downloads 405