Search results for: James Patrick A. Diaz

Authors: Anli Du Preez, James Bekker

Abstract:

Data is currently one of the most critical and influential emerging technologies. However, the true potential of data is yet to be exploited since, currently, about 1% of generated data are ever actually analyzed for value creation. There is a data gap where data is not explored due to the lack of data analytics infrastructure and the required data analytics skills. This study developed a decision support framework for data analytics by following Jabareen’s framework development methodology. The study focused on machine learning algorithms, which is a subset of data analytics. The developed framework is designed to assist data analysts with little experience, in choosing the appropriate machine learning algorithm given the purpose of their application.

Keywords: Data analytics, Industrial engineering, Machine learning, Value creation

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Authors: Patrick Nüß

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I estimate management opposition against unions in terms of hiring discrimination by a large scale field experiment in the German labor market. The results show that callback rates for union members decrease significantly in the presence of high sectoral union density and large firm size. I further explore how this effect varies with regional and sectoral labor dispute intensity and find that management opposition is stronger when a sector is exposed to an intense labor dispute. There is evidence that the observed management opposition can be explained by sectoral union threat effects. Sectors with lower hiring discrimination have lower coverage of collective agreements, and in the absence of a collective agreement, they are less likely to follow the collective agreement wage setting.

Keywords: trade unions, Industrial relations, management opposition, union threat, labor disputes, field experiments

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Authors: Agnese Natali, Francesco Morelli, Walter Salvatore, José Humberto Matias de Paula Filho, Patrick Pol

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In the framework of the evaluation of the applicability of high strength steel to produce thin-walled elements to be used in Automated Rack Supported Warehouses, an experimental campaign is carried outto evaluate the structural performance of typical profile shapes adopted for such purposes and made of high strength steel. Numerical models are developed to fit the observed failure modes, stresses, and deformation patterns, and proper directions are proposed to simplify the numerical simulations to be used in further applications and to evaluate the mechanical behavior and performance of profiles.

Keywords: Steel racks, Automated Rack Supported Warehouse, thin walled cold-formed elements, high strength steel.

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Authors: Katja Ignatieva, Patrick Wong

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We investigated the dynamics of high frequency energy prices, including crude oil and electricity prices. The returns of underlying quantities are modelled using various parametric models such as stochastic framework with jumps and stochastic volatility (SVCJ) as well as non-parametric alternatives, which are purely data driven and do not require specification of the drift or the diffusion coefficient function. Using different statistical criteria, we investigate the performance of considered parametric and nonparametric models in their ability to forecast price series and volatilities. Our models incorporate possible seasonalities in the underlying dynamics and utilise advanced estimation techniques for the dynamics of energy prices.

Keywords: stochastic volatility, affine jump-diffusion models, high frequency data, model specification, markov chain monte carlo

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Authors: Rosie S. Abalos, John Patrick M. Dizon

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The operation of fish cages in mariculture parks for milkfish production remains a lucrative business for aquaculture operators. Three areas in the Philippines where mariculture parks are still in active operation were identified as study sites for this research. Financial analysis was used to estimate profitability of mariculture operations in the selected study sites. Based on the result of this research, milkfish production in mariculture parks remains profitable both in terms of net profit generation and the return on investment. To improve the profitability of aquaculture operations in mariculture parks, the relatively high price of operational inputs should be managed. As a recommendation, further studies should be conducted on the profitability of aquaculture operations in mariculture parks in the country to include other factors which may cause losses on the part of the operator and factors that may affect price of produce upon harvest.

Keywords: mariculture parks, milkfish production, aquaculture, profitability

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Authors: Temidayo James Aransiola, Vania Ceccato, Marcelo Justus

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This paper investigates the effect of absolute deprivation (proxy unemployment) and relative deprivation (proxy income inequality) on homicide levels in Brazil. A database from the Brazilian Information System about Mortality and Census of the year 2000 and 2010 was used to estimate negative binomial models of homicide levels controlling for socioeconomic, demographic and geographic factors. Findings show that unemployment and income inequality affect homicides levels and that the effect of the former is more pronounced compared to the latter. Moreover, the combination of income inequality and unemployment exacerbates the overall effect of deprivation on homicide levels.

Keywords: deprivation, inequality, interaction, unemployment, violence

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Authors: Enyia James Diwa, Dodeye Ina Igbong, Archibong Eso Archibong

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The creep life of an industrial gas turbine is determined through a physics-based model used to investigate the high pressure temperature (HPT) of the blade in use. A performance model was carried out via the Cranfield University TURBOMATCH simulation software to size the blade and to determine the corresponding stress. Various effects such as radial temperature distortion factor, turbine entry temperature, ambient temperature, blade metal temperature, and compressor degradation on the blade creep life were investigated. The output results show the difference in creep life and the location of failure along the span of the blade enabling better-informed advice for the gas turbine operator.

Keywords: creep, living, performance, degradation

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Authors: Dan James

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The Medial Axis of the Main Canyon of Valles Marineris is determined geometrically with maximally inscribed discs aligned with the boundaries or rims of the Main Canyon. Inscribed discs are placed at evenly spaced longitude intervals and, using the radius function, the locus of the centre of all discs is determined, together with disc centre co-ordinates. These centre co-ordinates result in arrays of x, y co-ordinates which are curve fitted to a Sinusoidal function and residuals appropriate for nonlinear regression are evaluated using the R-squared value (R2) and the Root Mean Squared Error (RMSE). This evaluation demonstrates that a Sinusoidal Curve closely fits to the co-ordinate data

Keywords: medial axis, MAT, valles marineris, sinusoidal

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Authors: Thomas Chinwe Urama, Patrick Oseloka Ezepue, Peters Chimezie Nnanwa

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This paper investigates the universal financial dynamics in two dominant stock markets in Sub-Saharan Africa, through an in-depth analysis of the cross-correlation matrix of price returns in Nigerian Stock Market (NSM) and Johannesburg Stock Exchange (JSE), for the period 2009 to 2013. The strength of correlations between stocks is known to be higher in JSE than that of the NSM. Particularly important for modelling Nigerian derivatives in the future, the interactions of other stocks with the oil sector are weak, whereas the banking sector has strong positive interactions with the other sectors in the stock exchange. For the JSE, it is the oil sector and beverages that have greater sectorial correlations, instead of the banks which have the weaker correlation with other sectors in the stock exchange.

Keywords: random matrix theory, cross-correlations, emerging markets, option pricing, eigenvalues eigenvectors, inverse participation ratios and implied volatility

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Authors: Patrick T. I. Lam, Wenjing Yang

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A smart city project embraces benefits and costs which can be classified under direct and indirect categories. Externalities come into the picture, but they are often difficult to quantify. Despite this barrier, policy makers need to carry out cost-benefit analysis to justify the huge investments needed to make a city smart. The recent trend is towards the engagement of the private sector to utilize their resources and expertise, especially in the Information and Communication Technology (ICT) areas, where innovations blossom. This study focuses on the identification of costs (on a life cycle basis) and benefits associated with smart city project developments based on a comprehensive literature review and case studies, where public-private partnerships would warrant consideration, the related costs and benefits are highlighted. The findings will be useful for policy makers of cities.

Keywords: smart city projects, costs and benefits, identification, public-private partnerships

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Authors: Enayat Davoudi

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This paper is about the study of media literacy and its role in Iranian society. Determine the research hypothesis by the use of James Patter theory and us stratification and also culture theory. By the use of traversal method and by the aim of the survey on 375 students in Mahmoudabad which was selected randomly, the data was gathered and analyzed by SPSS software. Coefficient alpha for Crohn Bach is used in order to reach to the justifiability of indexes. The research findings show that the variable like duration, rate and type of media use, the realization of media content, audience goal and motivation, economical and social base and the rate of education has a meaningful relation with media literacy.

Keywords: media, media literacy, Iranian society, Mahmoudabad students

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Authors: Thomas Paris, Vincent Bruyere, Patrick Namy

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A numerical model is developed to simulate gas blowdowns through a thin tube and a filter (porous media), separating a high pressure gas filled reservoir to low pressure ones. Based on a previous work, a one-dimensional approach is developed by using the finite element method to solve the transient compressible flow and to predict the pressure and temperature evolution in space and time. Mass, momentum, and energy conservation equations are solved in a fully coupled way in the reservoirs, the pipes and the porous media. Numerical results, such as pressure and temperature evolutions, are firstly compared with experimental data to validate the model for different configurations. Couplings between porous media and pipe flow are then validated by checking mass balance. The influence of the porous media and the nature of the gas is then studied for different initial high pressure values.

Keywords: compressible flow, fluid mechanics, heat transfer, porous media

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Authors: Javier Fernandez, Mayte Medina, Rafael Fernandez de Canete, Nuria Alcain, Juan Carlos Ramos-Diaz

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At present, the experimentation of anesthetic drugs on patients requires a regulation protocol, and the response of each patient to several doses of entry drug must be well known. Therefore, the development of pharmacological dose control systems is a promising field of research in anesthesiology. In this paper, it has been developed a non-linear compartmental the pharmacokinetic-pharmacodynamical model which describes the anesthesia depth effect in a sufficiently reliable way over a set of patients with the depth effect quantified by the Bi-Spectral Index. Afterwards, an Artificial Neural Network (ANN) predictive controller has been designed based on the depth of anesthesia model so as to keep the patient in the optimum condition while he undergoes surgical treatment. For the purpose of quantifying the efficiency of the neural predictive controller, a classical proportional-integral-derivative controller has also been developed to compare both strategies. Results show the superior performance of predictive neural controller during BiSpectral Index reference tracking.

Keywords: anesthesia, bi-spectral index, neural network control, pharmacokinetic-pharmacodynamical model

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Authors: C. Fernandez-Plaza, I. Abad, E. Diaz, I. Diaz

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Births occurring before the 37th week of gestation are considered preterm births. A threat of preterm is defined as the beginning of regular uterine contractions, dilation and cervical effacement between 23 and 36 gestation weeks. To author's best knowledge, the factors that determine the beginning of the birth are not completely defined yet. In particular, the incidence of sleep habits on preterm births is weekly studied. The aim of this study is to develop a model to predict the factors affecting premature delivery on pregnancy, based on the above potential risk factors, including those derived from sleep habits and light exposure at night (introduced as 12 variables obtained by a telephone survey using two questionnaires previously used by other authors). Thus, three groups of variables were included in the study (maternal, fetal and sleep habits). The study was approved by Research Ethics Committee of the Principado of Asturias (Spain). An observational, retrospective and descriptive study was performed with 481 births between January 1, 2015 and May 10, 2016 in the University Central Hospital of Asturias (Spain). A statistical analysis using SPSS was carried out to compare qualitative and quantitative variables between preterm and term delivery. Chi-square test qualitative variable and t-test for quantitative variables were applied. Statistically significant differences (p < 0.05) between preterm vs. term births were found for primiparity, multi-parity, kind of conception, place of residence or premature rupture of membranes and interruption during nights. In addition to the statistical analysis, machine learning methods to look for a prediction model were tested. In particular, tree based models were applied as the trade-off between performance and interpretability is especially suitable for this study. C5.0, recursive partitioning, random forest and tree bag models were analysed using caret R-package. Cross validation with 10-folds and parameter tuning to optimize the methods were applied. In addition, different noise reduction methods were applied to the initial data using NoiseFiltersR package. The best performance was obtained by C5.0 method with Accuracy 0.91, Sensitivity 0.93, Specificity 0.89 and Precision 0.91. Some well known preterm birth factors were identified: Cervix Dilation, maternal BMI, Premature rupture of membranes or nuchal translucency analysis in the first trimester. The model also identifies other new factors related to sleep habits such as light through window, bedtime on working days, usage of electronic devices before sleeping from Mondays to Fridays or change of sleeping habits reflected in the number of hours, in the depth of sleep or in the lighting of the room. IF dilation < = 2.95 AND usage of electronic devices before sleeping from Mondays to Friday = YES and change of sleeping habits = YES, then preterm is one of the predicting rules obtained by C5.0. In this work a model for predicting preterm births is developed. It is based on machine learning together with noise reduction techniques. The method maximizing the performance is the one selected. This model shows the influence of variables related to sleep habits in preterm prediction.

Keywords: machine learning, noise reduction, preterm birth, sleep habit

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Authors: Hsiao-Tseng Lin, Nguyen Ngoc Dat, Szu-Mei Hsiao, R. J. Hernández-Díaz

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The practitioner system has become an important tool for universities working to shrink the gap between industry and vocational education. Beginning in 2015, Meiho University conducted a consecutive three-year program for teaching excellence, funded in part by Taiwan’s Ministry of Education, with a total project funding of over $2.5 million USD. One of the highlights of this program is the recruitment of 300 industry practitioners to participate in collaborative teaching, a dual-mentor system, and curriculum planning. More than 60% of the practitioners boast more than 10 years of practical industry experience, and 52% of them have earned master's degree or higher. Students rated their overall program satisfaction over 4.5(out of 5.0) on average. This study explores the perspectives of academics and industry practitioners using in-depth interviews and surveys, along with an examination of the challenges of the practitioner system. The paper enables the framing of practitioner system policies by vocational education institutions and industry to facilitate more effective and efficient transfer of knowledge between academics and practitioners, leading to enhanced university competitive advantage, which would ultimately benefit society.

Keywords: collaborative teaching, industry practitioners, practitioner system, vocational education

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Authors: Arwa Khannoussi, Alexandru-Liviu Olteanu, Pritesh Narayan, Catherine Dezan, Jean-Philippe Diguet, Patrick Meyer, Jacques Petit-Frere

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Due to the nature of autonomous Unmanned Aerial Vehicles (UAV) missions, it is important that the decisions of a UAV stay consistent with the priorities of an operator, while at the same time allowing them to be easily audited and explained. We propose a multi-layer decision engine that integrates the operator (human) preferences by using the Multi-Criteria Decision Aiding (MCDA) methods. A software implementation of a UAV simulator and of the decision engine is presented to highlight the advantage of using such techniques on high-level decisions. We demonstrate that, with such a preference-based decision engine, the decisions of the UAV are compatible with the priorities of the operator, which in turn increases her/his confidence in its autonomous behavior.

Keywords: autonomous UAV, multi-criteria decision aiding, multi-layers decision engine, operator's preferences, traceable decisions, UAV simulation

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Authors: Laura M. F. Bertens

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The houses we inhabit are important containers of memory. As homes, they take on meaning for those who live inside, and memories of family life become intimately tied up with rooms, windows, and gardens. Each new family creates a new layer of meaning, resulting in a palimpsest of family memory. These houses function quite literally as memory palaces, as a walk through a childhood home will show; each room conjures up images of past events. Over time, these personal memories become woven together with the cultural memory of countries and generations. The importance of the home is a central theme in art, and several contemporary artists have a special interest in the relationship between memory and the home. This paper analyses three case studies in order to get a deeper understanding of the ways in which the home functions and feels like a memory palace, both on an individual and on a collective, cultural level. Close reading of the artworks is performed on the theoretical intersection between Art History and Cultural Memory Studies. The first case study concerns works from the exhibition Mnemosyne by the artist duo Anne and Patrick Poirier. These works combine interests in architecture, archaeology, and psychology. Models of cities and fantastical architectural designs resemble physical structures (such as the brain), architectural metaphors used in representing the concept of memory (such as the memory palace), and archaeological remains, essential to our shared cultural memories. Secondly, works by Do Ho Suh will help us understand the relationship between the home and memory on a far more personal level; outlines of rooms from his former homes, made of colourful, transparent fabric and combined into new structures, provide an insight into the way these spaces retain individual memories. The spaces have been emptied out, and only the husks remain. Although the remnants of walls, light switches, doors, electricity outlets, etc. are standard, mass-produced elements found in many homes and devoid of inherent meaning, together they remind us of the emotional significance attached to the muscle memory of spaces we once inhabited. The third case study concerns an exhibition in a house put up for sale on the Dutch real estate website Funda. The house was built in 1933 by a Jewish family fleeing from Germany, and the father and son were later deported and killed. The artists Anne van As and CA Wertheim have used the history and memories of the house as a starting point for an exhibition called (T)huis, a combination of the Dutch words for home and house. This case study illustrates the way houses become containers of memories; each new family ‘resets’ the meaning of a house, but traces of earlier memories remain. The exhibition allows us to explore the transition of individual memories into shared cultural memory, in this case of WWII. Taken together, the analyses provide a deeper understanding of different facets of the relationship between the home and memory, both individual and collective, and the ways in which art can represent these.

Keywords: Anne and Patrick Poirier, cultural memory, Do Ho Suh, home, memory palace

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Authors: Diego Diaz H., Cristian Larroulet

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Although a large earthquake has clear and immediate consequences such as deaths, destruction of infrastructure and displacement (at least temporary) of part of the population, scientific research about the impact of a geological disaster in economic activity is inconclusive, especially when looking beyond the very short term. Estimating the economic impact years after a disaster strike is non-trivial since there is an unavoidable difficulty in attributing the observed effect to the disaster and not to other economic shocks. Case studies are performed that determine the impact of earthquakes in Chile, Japan, and New Zealand at a regional level by applying the synthetic control method, using the natural disaster as treatment. This consisted in constructing a counterfactual from every region in the same country that is not affected (or is slightly affected) by the earthquake. The results show that the economies of Canterbury and Tohoku achieved greater levels of GDP per capita in the years after the disaster than they would have in the absence of the disaster. For the case of Chile, however, the region of Maule experiences a decline in GDP per capita because of the earthquake. All the results are robust according to the placebo tests. Also, the results suggest that national institutional quality improve the growth process after the disaster.

Keywords: earthquake, economic growth, institutional quality, synthetic control

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Authors: Luis Miguel Méndez Díaz

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In this article, a mathematics teaching-learning strategy will be presented, specifically differential calculus in one variable, in a fun and competitive space in which the action on the part of the student is manifested and not only the repetition of information on the part of the teacher. Said action refers to motivating, problematizing, summarizing, and coordinating a game of dominoes whose thematic cards are designed around the basic and main contents of differential calculus. The strategies for teaching this area are diverse and precisely the game of dominoes is one of the most used strategies in the practice of mathematics because it stimulates logical reasoning and mental abilities. The objective on this investigation is to identify the way in which the game of dominoes affects the learning and understanding of fundamentals concepts of differential calculus in one variable through experimentation carried out on students of the first semester of the School of Engineering and Sciences of the Technological Institute of Monterrey Campus Querétaro. Finally, the results of this study will be presented and the use of this strategy in other topics around mathematics will be recommended to facilitate logical and meaningful learning in students.

Keywords: collaborative learning, logical-mathematical intelligence, mathematical games, multiple intelligences

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Authors: James Newman

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Process mining is an emerging technology that looks to serialize enterprise data in time series data. It has been used by many companies and has been the subject of a variety of research papers. However, the majority of current efforts have looked at how to best create process mining from standard relational databases. This paper is the first pass at outlining a database custom-built for the minimal viable product of process mining. We present Block Miner, a blockchain protocol to store process mining data across a distributed network. We demonstrate the feasibility of storing process mining data on the blockchain. We present a proof of concept and show how the intersection of these two technologies helps to solve a variety of issues, including but not limited to ransomware attacks, tax documentation, and conflict resolution.

Keywords: blockchain, process mining, memory optimization, protocol

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Authors: Babagana Mohammed, Fidelis Patrick Afangide

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This research investigated the possibility of using waste low-dense polyethylene and waste plastic bottles for the production of interlock pavement blocks. In many parts of the world, interlock pavement block is used widely as modern day solution to outdoor flooring applications and the blocks have different shapes, sizes and colours suiting the imagination of landscape architects. Using suitable and conventional mould having a 220 x 135 x 50 mm³ shape, the interlock blocks were produced. The material constituents of the produced blocks were waste low-dense polyethylene and waste plastic bottles mixed in varying, respective percentage-weight proportions of; 100%+0%, 75%+25%, 50%+50% and 25%+75%. The blocks were then tested for unconfined compressive strength and water absorption properties. The test results compared well with those of conventional concrete interlock blocks and the research demonstrates the possibility of value recovery from the waste streams which are currently dumped in open-spaces thereby affecting the environment.

Keywords: pavement blocks, polyethylene, plastic bottle, wastes, valorization

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

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

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

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Authors: S. Bayona, J. Nunez, D. Paez, C. Diaz

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The application of Lean manufacturing tools continues to be an effective solution for increasing productivity, reducing costs and eliminating waste in the manufacture of goods and services. This article analyzes the production process of a hot asphalt manufacturing company from an administrative and technical perspective. Three main phases were analyzed, the first phase was related to the determination of the risk priority number of the main operations in asphalt mix production process by an FMEA (Failure Mode Effects Analysis), in the second phase the Value Stream Mapping (VSM) of the production line was performed and in the third phase a SWOT (Strengths, Weaknesses Opportunities, Threats) matrix was constructed. Among the most valued failure modes were the lack training of workers in occupational safety and health issues, the lack of signaling and classification of granulated material, and the overweight of vehicles loaded. The analysis of the results in the three phases agree on the importance of training operational workers, improve communication with external actors in order to minimize delays in material orders and strengthen control suppliers.

Keywords: asphalt, lean manufacturing, productivity, process

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Authors: Mark Harmon, Abdolghani Ebrahimi, Patrick Lucey, Diego Klabjan

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In this paper, we predict the likelihood of a player making a shot in basketball from multiagent trajectories. Previous approaches to similar problems center on hand-crafting features to capture domain-specific knowledge. Although intuitive, recent work in deep learning has shown, this approach is prone to missing important predictive features. To circumvent this issue, we present a convolutional neural network (CNN) approach where we initially represent the multiagent behavior as an image. To encode the adversarial nature of basketball, we use a multichannel image which we then feed into a CNN. Additionally, to capture the temporal aspect of the trajectories, we use “fading.” We find that this approach is superior to a traditional FFN model. By using gradient ascent, we were able to discover what the CNN filters look for during training. Last, we find that a combined FFN+CNN is the best performing network with an error rate of 39%.

Keywords: basketball, computer vision, image processing, convolutional neural network

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Authors: Veronica Sri Lestari, Asmuddin Natsir, Hasmida Karim, Ian Patrick

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The research aimed at investigating the factors affecting consumers’ willingness to pay for chicken meat from biosecure farms. The research was conducted in Makassar City, South Sulawesi Province, Indonesia. Samples were taken using random sampling technique in two supermarkets namely Lotte Mart and Gelael. Total samples were 50 respondents which comprised the chicken meat consumers. To find out the consumers’ willingness to pay for chicken meat from the biosecure farms, the contingent valuation method was utilized. Data were collected through interviews and questionnaires. Probit Logistic was estimated to examine the factors affecting the consumers’ willingness to pay for at the premium price for chicken meat from the biosecure farms. The research indicates that the education and income affect significantly the consumers’ willingness to pay for chicken meat from the biosecure farms (P < 0.05). The results of the study will be beneficial for the policy makers, producers, consumers and those conducting research.

Keywords: biosecure, chicken, farms, consumer, willingness-to-pay

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Authors: Minna Salim, James Brady

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Postoperative nausea and pain (PONV) are adverse effects following surgical procedures. It is especially pronounced in patients undergoing orthognathic surgery, as their mouth is closed postoperatively using wires or rubber bands. Postoperative mouth closure increases the discomfort and risk of complications associated with nausea and vomiting. Many surgeons and institutions apply gastric decompression in hopes of aspirating stomach contents and, therefore, decreasing PONV incidence. This review observed that PONV incidence was not affected by gastric decompression overall. However, the effect of gastric decompression on PONV in orthognathic surgery was variable. This paper aims to summarize the findings of gastric decompression on PONV and to determine the need for it in orthognathic surgery.

Keywords: gastric decompression, nasogastric tube, orthognathic surgery, postoperative nausea, vomiting

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Authors: Alexandre Gonzalez, Zohra Benfodda, David Bénimélis, Jean-Xavier Fontaine, Roland Molinié, Patrick Meffre

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Volatile organic compounds (VOCs) emitted by flowers play an important role in plant ecology. However, the Tillandsia genus has been scarcely studied according to the VOCs emitted by flowers. Tillandsia are epiphytic flowering plants belonging to the Bromeliaceae family. The VOCs composition of twelve unscented and two faint-scented Tillandsia species was studied. The headspace solid phase microextraction coupled with gas chromatography combined with mass spectrometry method was used to explore the chemical diversity of the VOCs. This study allowed the identification of 65 VOCs among the fourteen species, and between six to twenty-five compounds were identified in each of the species.

Keywords: tillandsia, headspace solid phase microextraction (HS-SPME), gas chromatography-mass spectrometry (GC-MS), scentless flowers, volatile organic compounds (VOCs), PCA analysis, heatmap

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Authors: Zhenyu Du, Ed Collings, James Meredith

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The high cost of composite materials versus conventional materials remains a major barrier to uptake in the transport sector. This is exacerbated by a shortage of skilled labour which makes the labour content of a hand laid composite component (~40 % of total cost) an obvious target for reduction. Automation is a method to remove labour cost and improve quality. This work focuses on the challenges and benefits to automating the manufacturing process from raw fibre to trimmed component. It will detail the experimental work required to complete an automation cell, the control strategy used to integrate all machines and the final benefits in terms of throughput and cost.

Keywords: automation, low cost technologies, processing and manufacturing technologies, resin transfer moulding

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Authors: Verónica Díaz Quezada

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The importance given to the development of the problem solving skill and the requirement to solve problems framed in mathematical or real life contexts, in practice, they are not evidence in relation to the teaching of proportional variations. This qualitative and descriptive study aims to (1) to improve problem solving ability of high school students in Chile, (ii) to elaborate and describe a didactic intervention strategy based on learning situations in proportional variations, focused on solving types of routine problems of various contexts and non-routine problems. For this purpose, participant observation was conducted, test of mathematics problems and an opinion questionnaire to thirty-six high school students. Through the results, the highest academic performance is evidenced in the routine problems of purely mathematical context, realistic, fantasy context, and non-routine problems, except in the routine problems of real context and compound proportionality problems. The results highlight the need to consider in the curriculum different types of problems in the teaching of mathematics that relate the discipline to everyday life situations

Keywords: algebra, high school, proportion variations, nonroutine problem solving, routine problem solving

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Authors: Patrick Ahern, David Collery

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The implementation of lean thinking in the manufacturing industry revolutionized the traditional approach to large-scale production through the process of identifying the waste in each task and putting in place mitigation measures to eliminate the waste in all its forms. The Irish construction industry, however, has been much slower to adopt the principles of lean, opting instead to stick with the traditional approach to construction project delivery which is inherently wasteful. Lean thinking holds the potential to revolutionize the construction industry in a similar manner to the adoption of lean manufacturing. Lean principles present opportunities for reduced project duration, reduced project cost, improved quality, and elimination of re-works and non-value-added activities. The following research has been designed to accumulate research data through available literature, electronic surveys, and interviews. The results show an industry reluctant to accept change and an undefined path to successful lean construction implementation.

Keywords: barriers, lean construction, lean implementation, lean manufacturing, lean philosophy

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