Search results for: stock selection

Authors: Tahir Mehmood, Mumtaz Akhter

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The global need for conflict management is greater now in the early 21st century than ever before. According to UNESCO, half of the world’s 195 countries will have to expand their stock of educationist significantly, some by tens of thousands, if the goal development targets are desired to achieve. Socioeconomic inequities, political instability, demographic changes and crises such as the HIV/AIDs epidemic have engendered huge shortfalls in teacher supply and low teacher quality in many developing countries. Education serves as back bone in development process. Open learning and distance education programs are serving as pivotal part of development process. It is now clear that ‘bricks and mortar’ approaches to expanding teacher education may not be adequate if the current and projected shortfalls in teacher supply and low teacher quality are to be properly addressed. The study is designed to measure the perceptions of teaching learning community about conflict management with special reference to open and distance learning. It was descriptive study which targeted teachers, students, community members and experts. Data analysis was carried out by using statistical techniques served by SPSS. Findings reflected that audience perceives open and distance learning as change agent and as development tool. It is noticed that target audience has driven prominent performance by using facility of open and distance learning.

Keywords: conflict management, open and distance learning, teachers, students

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Authors: Agostinho Antunes

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The completion of the human genome sequencing in 2003 opened a new perspective into the importance of whole genome sequencing projects, and currently multiple species are having their genomes completed sequenced, from simple organisms, such as bacteria, to more complex taxa, such as mammals. This voluminous sequencing data generated across multiple organisms provides also the framework to better understand the genetic makeup of such species and related ones, allowing to explore the genetic changes underlining the evolution of diverse phenotypic traits. Here, recent results from our group retrieved from comparative evolutionary genomic analyses of selected marine animal species will be considered to exemplify how gene novelty and gene enhancement by positive selection might have been determinant in the success of adaptive radiations into diverse habitats and lifestyles.

Keywords: comparative genomics, adaptive evolution, bioinformatics, phylogenetics, genome mining

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Authors: Vincent Andrew Cappellano

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In the early phases of critical infrastructure system design, translating distributed computing requirements to an architecture has risk given the multitude of approaches (e.g., cloud, edge, fog). In many systems, a single requirement for system uptime / availability is used to encompass the system’s intended operations. However, when architected systems may perform to those availability requirements only during normal operations and not during component failure, or during outages caused by adversary attacks on critical infrastructure (e.g., physical, cyber). System designers lack a structured method to evaluate availability requirements against candidate system architectures through deep degradation scenarios (i.e., normal ops all the way down to significant damage of communications or physical nodes). This increases risk of poor selection of a candidate architecture due to the absence of insight into true performance for systems that must operate as a piece of critical infrastructure. This research effort proposes a process to analyze critical infrastructure system availability requirements and a candidate set of systems architectures, producing a metric assessing these architectures over a spectrum of degradations to aid in selecting appropriate resilient architectures. To accomplish this effort, a set of simulation and evaluation efforts are undertaken that will process, in an automated way, a set of sample requirements into a set of potential architectures where system functions and capabilities are distributed across nodes. Nodes and links will have specific characteristics and based on sampled requirements, contribute to the overall system functionality, such that as they are impacted/degraded, the impacted functional availability of a system can be determined. A machine learning reinforcement-based agent will structurally impact the nodes, links, and characteristics (e.g., bandwidth, latency) of a given architecture to provide an assessment of system functional uptime/availability under these scenarios. By varying the intensity of the attack and related aspects, we can create a structured method of evaluating the performance of candidate architectures against each other to create a metric rating its resilience to these attack types/strategies. Through multiple simulation iterations, sufficient data will exist to compare this availability metric, and an architectural recommendation against the baseline requirements, in comparison to existing multi-factor computing architectural selection processes. It is intended that this additional data will create an improvement in the matching of resilient critical infrastructure system requirements to the correct architectures and implementations that will support improved operation during times of system degradation due to failures and infrastructure attacks.

Keywords: architecture, resiliency, availability, cyber-attack

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Authors: S. Ghasemi, K. Khorasani

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In this paper, the problem of fault detection and isolation in the attitude control subsystem of spacecraft formation flying is considered. In order to design the fault detection method, an extended Kalman filter is utilized which is a nonlinear stochastic state estimation method. Three fault detection architectures, namely, centralized, decentralized, and semi-decentralized are designed based on the extended Kalman filters. Moreover, the residual generation and threshold selection techniques are proposed for these architectures.

Keywords: component, formation flight of satellites, extended Kalman filter, fault detection and isolation, actuator fault

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Authors: Mojtaba Ghodsi, Hamidreza Ziaifar, Morteza Mohammadzaheri, Payam Soltani

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In this paper, the performance of a medium power (25 kW/25 kHz) hybrid inverter with a reactive transformer is investigated. To analyze the sensitivity of the inverster, the RSM technique is employed to manifest the effective factors in the inverter to minimize current passing through the Insulated Bipolar Gate Transistors (IGBTs) (current stress). It is revealed that the ratio of the axillary inductor to the effective inductance of resonant inverter (N), is the most effective parameter to minimize the current stress in this type of inverter. In practice, proper selection of N mitigates the current stress over IGBTs by five times. This reduction is very helpful to keep the IGBTs at normal temperatures.

Keywords: analytical analysis, hybrid resonant inverter, reactive transformer, response surface method

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Authors: Vilas Warudkar, Dinkar Janghel, Siraj Ahmed

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Rapid development experienced by India requires huge amount of energy. Actual supply capacity additions have been consistently lower than the targets set by the government. According to World Bank 40% of residences are without electricity. In 12th five year plan 30 GW grid interactive renewable capacity is planned in which 17 GW is Wind, 10 GW is from solar and 2.1 GW from small hydro project, and rest is compensated by bio gas. Renewable energy (RE) and energy efficiency (EE) meet not only the environmental and energy security objectives, but also can play a crucial role in reducing chronic power shortages. In remote areas or areas with a weak grid, wind energy can be used for charging batteries or can be combined with a diesel engine to save fuel whenever wind is available. India according to IEC 61400-1 belongs to class IV Wind Condition; it is not possible to set up wind turbine in large scale at every place. So, the best choice is to go for small scale wind turbine at lower height which will have good annual energy production (AEP). Based on the wind characteristic available at MANIT Bhopal, rotor for small scale wind turbine is designed. Various Aero foil data is reviewed for selection of airfoil in the Blade Profile. Airfoil suited of Low wind conditions i.e. at low Reynold’s number is selected based on Coefficient of Lift, Drag and angle of attack. For designing of the rotor blade, standard Blade Element Momentum (BEM) Theory is implanted. Performance of the Blade is estimated using BEM theory in which axial induction factor and angular induction factor is optimized using iterative technique. Rotor performance is estimated for particular designed blade specifically for low wind Conditions. Power production of rotor is determined at different wind speeds for particular pitch angle of the blade. At pitch 15o and velocity 5 m/sec gives good cut in speed of 2 m/sec and power produced is around 350 Watts. Tip speed of the Blade is considered as 6.5 for which Coefficient of Performance of the rotor is calculated 0.35, which is good acceptable value for Small scale Wind turbine. Simple Load Model (SLM, IEC 61400-2) is also discussed to improve the structural strength of the rotor. In SLM, Edge wise Moment and Flap Wise moment is considered which cause bending stress at the root of the blade. Various Load case mentioned in the IEC 61400-2 is calculated and checked for the partial safety factor of the wind turbine blade.

Keywords: annual energy production, Blade Element Momentum Theory, low wind Conditions, selection of airfoil

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Authors: Helena Susana Afonso Alves, Natália Maria Rafael Canadas, Ana Maria Rodrigues

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We examined the impact of governance rules on information asymmetry, using the turnover ratio and the bid-ask spread as proxies for the information asymmetry. We used a SEM model and analyzed the indirect relations through the voluntary disclosure of information and the organizational performance. We built a voluntary disclosure index based on the information firms provided in their annual reports and divided the governance characteristics in two constructs: directors’ and supervisors’ structures and ownership structure. We concluded that the ownership structure exerts a direct influence on share price and share liquidity, Otherwise, the directors’ and supervisors’ structures exert an indirect influence, through the organizational performance and the voluntary disclosure of information. The results also show that for firms with high levels of disclosure the bid-ask spread is lower. However, in firms with a high ownership concentration investors tend to increase the bid-ask spreads and trade less, which, in this case, reduces the liquidity of the stock. The failure to find the relationship between voluntary disclosure of information and the turnover ratio shows us that the liquidity of shares is more related to the greater or lesser concentration of shareholders, with the performance of their companies than with the access to information. Moreover, it is clear that the role that information disclosure plays is mainly at the level of price formation.

Keywords: corporate governance, information asymmetry, voluntary disclosure, structural equation modelling, SEM

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Authors: Pavak Vyas, Hiral Vyas

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Spin-offs commonly known as demergers in India, represents dismantling of conglomerates which is a common phenomenon in financial markets across the world. Demergers are carried out with different motives. A demerger generally refers to a corporate restructuring where, a large company divests its stake in in its subsidiary and distributes the shares of the subsidiary - demerged entity to the existing shareholders without any consideration. Demergers in Indian companies are over a decade old phenomena, with many companies opting for the same. This study examines the demerger regulations in Indian capital markets and the announcement period price reaction of demergers during year 2010-2015. We study total 97 demerger announcements by companies listed in India and try to establish that demergers results into abnormal returns for the shareholders of the parent company. Using event study methodology we have analyzed the security price performance of the announcement day effect 10 days prior to announcement to 10 days post demerger announcement. We find significant out-performance of the security over the benchmark index post demerger announcements. The cumulative average abnormal returns range from 3.71% on the day of announcement of a private demerger to 2.08% following 10 days surrounding the announcement, and cumulative average abnormal returns range from 5.67% on the day of announcement of a public demerger to 4.15% following10 days surrounding the announcement.

Keywords: demergers, event study, spin offs, stock returns

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Authors: Benga Ebouele, Thomas Tengen

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Implementing either periodic or continuous inventory review model within most manufacturing-companies-supply chains as a management tool may incur higher costs. These high costs affect the system flexibility which in turn affects the level of service required to satisfy customers. However, these effects are not clearly understood because the parameters of both inventory review policies (protection demand interval, order quantity, etc.) are not designed to be fully utilized under different and uncertain conditions such as poor manufacturing, supplies and delivery performance. Coming up with a hybrid model which may combine in some sense the feature of both continuous and a periodic inventory review models should be useful. Therefore, there is a need to build and evaluate such hybrid model on the annual total cost, stock out probability and system’s flexibility in order to search for the most cost effective inventory review model. This work also seeks to find the optimal sets of parameters of inventory management under stochastic condition so as to optimise each policy independently. The results reveal that a continuous inventory system always incurs lesser cost than a periodic (R, S) inventory system, but this difference tends to decrease as time goes by. Although the hybrid inventory is the only one that can yield lesser cost over time, it is not always desirable but also natural to use it in order to help the system to meet high performance specification.

Keywords: demand and lead time randomness, hybrid Inventory model, optimization, supply chain

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Authors: Khawla Hlel

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Purpose: This study aims to examine whether critical audit matters (CAM) affect financial information quality. We also investigate the moderating role of the audit committee on the association between CAM and financial information quality. Design/Methodology/Approach: The analysis is based on GLS and GMM regressions explaining the absolute value of discretionary accruals by using 52 Tunisian listed firms on the Tunisia Stock Exchange (TSE) for the period 2017-2020. Findings: We find evidence that managers react to the CAM by increasing the quality of financial disclosures. This study provides insights into how a change in the auditor’s report model might impact the quality of financial information. It suggests that external auditors and audit committees serve as a beneficial mechanism for enhancing financial information quality by reducing information asymmetry. In addition, our results indicate that CAM is an efficient monitoring mechanism that increases financial reporting quality and supervises managers. Originality: This study is important for potential investors who should assess CAM when evaluating firms. Furthermore, the authors expect the findings to be interesting to firms, as this study highlights the effectiveness of the auditor in reducing managerial opportunistic behavior and improving information quality. The results could encourage audit regulators to ameliorate the standards, as this research reinforces the role of the auditor in increasing the quality of financial disclosure by offering the required information for shareholders.

Keywords: critical audit matters, audit committee, information quality, Tunisian firms

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Authors: Ahmed Elsebaay, Maged A. Abu Adma, Mahmoud Ramadan

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This study presents a technique clarifying the effect of ambient air temperature and loads power factor changing from standard values on electric generator power rating. The study introduces an optimized technique for selecting the correct electric generator power rating for certain application and operating site ambient temperature. The de-rating factors due to the previous effects will be calculated to be applied on a generator to select its power rating accurately to avoid unsafe operation and save its lifetime. The information in this paper provides a simple, accurate, and general method for synchronous generator selection and eliminates common errors.

Keywords: ambient temperature, de-rating factor, electric generator, power factor

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Authors: Sophie Oakes-Rogers, Di Bailey, Karen Slade

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Female offenders are a uniquely vulnerable group, who are at high risk of suicide. Whilst the prevention of self-harm and suicide remains a key global priority, we need to better understand the relationship between these challenging behaviours that constitute a pressing problem, particularly in environments designed to prioritise safety and security. Method choice is unlikely to be random, and is instead influenced by a range of cultural, social, psychological and environmental factors, which change over time and between countries. A key aspect of self-harm and suicide in women receiving forensic care is the lack of free access to methods. At a time where self-harm and suicide rates continue to rise internationally, understanding the role of these influencing factors and the impact of current suicide prevention strategies on the use of near-lethal methods is crucial. This poster presentation will present findings from 25 interviews and 3 focus groups, which enlisted a Participatory Action Research approach to explore the differences between self-harming and suicidal behavior. A key element of this research was using the lived experiences of women receiving forensic care from one forensic pathway in the UK, and the staffs who care for them, to discuss the role of near-lethal self-harm (NLSH). The findings and suggestions from the lived accounts of the women and staff will inform a draft assessment tool, which better assesses the risk of suicide based on the lethality of methods. This tool will be the first of its kind, which specifically captures the needs of women receiving forensic services. Preliminary findings indicate women engage in NLSH for two key reasons and is determined by their history of self-harm. Women who have a history of superficial non-life threatening self-harm appear to engage in NLSH in response to a significant life event such as family bereavement or sentencing. For these women, suicide appears to be a realistic option to overcome their distress. This, however, differs from women who appear to have a lifetime history of NLSH, who engage in such behavior in a bid to overcome the grief and shame associated with historical abuse. NLSH in these women reflects a lifetime of suicidality and indicates they pose the greatest risk of completed suicide. Findings also indicate differences in method selection between forensic provisions. Restriction of means appears to play a role in method selection, and findings suggest it causes method substitution. Implications will be discussed relating to the screening of female forensic patients and improvements to the current suicide prevention strategies.

Keywords: forensic mental health, method substitution, restriction of means, suicide

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Authors: B. F. Dada, B. S. Ewulo, M. A. Awodun, S. O. Ajayi

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Aggregate soil carbon distribution and stock in the soil in the form of a carbon pool is important for soil fertility and sequestration. The amount of carbon pool and other nutrients statues of the soil are to benefit plants, animal and the environment in the long run. This study was carried out at Akure North and South Local Government; the study area is one of the 18 Local Government Areas of Ondo State in the Southwest geo-political zone of Nigeria. The sites were divided into Map Grids and geo-referenced with Global Positioning System (GPS). Horizons were designated and morphological description carried out on the field. Pedons were characterized and classified according to USDA soil taxonomy. The local government area shares boundaries with; Ikere Local Government (LG) in the North, Ise Orun LG in the northwest, Ifedore LG in the northeast Akure South LG in the East, Ose LG in the South East, and Owo LG in the South. SOC-pool at Federal College of Agriculture topsoil horizon A2 is significantly higher than all horizons, 67.83 th⁻¹. The chemical properties of the pedons have shown that the soil is very strongly acidic to neutral reaction (4.68 – 6.73). The nutrients status of the soil topsoil A1 and A2 generally indicates that the soils have a low potential for retaining plant nutrients, and therefore call for adequate soil management.

Keywords: soil organic carbon (SOC), horizon, pedon, Akure

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Authors: Anastasiia Yu. Timofeeva

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Two new algorithms for nonparametric estimation of errors-in-variables models are proposed. The first algorithm is based on penalized regression spline. The spline is represented as a piecewise-linear function and for each linear portion orthogonal regression is estimated. This algorithm is iterative. The second algorithm involves locally weighted regression estimation. When the independent variable is measured with error such estimation is a complex nonlinear optimization problem. The simulation results have shown the advantage of the second algorithm under the assumption that true smoothing parameters values are known. Nevertheless the use of some indexes of fit to smoothing parameters selection gives the similar results and has an oversmoothing effect.

Keywords: grade point average, orthogonal regression, penalized regression spline, locally weighted regression

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Authors: B. Marasović, S. Pivac, S. V. Vukasović

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Constructing a portfolio of investments is one of the most significant financial decisions facing individuals and institutions. In accordance with the modern portfolio theory maximization of return at minimal risk should be the investment goal of any successful investor. In addition, the costs incurred when setting up a new portfolio or rebalancing an existing portfolio must be included in any realistic analysis. In this paper rebalancing an investment portfolio in the presence of transaction costs on the Croatian capital market is analyzed. The model applied in the paper is an extension of the standard portfolio mean-variance optimization model in which transaction costs are incurred to rebalance an investment portfolio. This model allows different costs for different securities, and different costs for buying and selling. In order to find efficient portfolio, using this model, first, the solution of quadratic programming problem of similar size to the Markowitz model, and then the solution of a linear programming problem have to be found. Furthermore, in the paper the impact of transaction costs on the efficient frontier is investigated. Moreover, it is shown that global minimum variance portfolio on the efficient frontier always has the same level of the risk regardless of the amount of transaction costs. Although efficient frontier position depends of both transaction costs amount and initial portfolio it can be concluded that extreme right portfolio on the efficient frontier always contains only one stock with the highest expected return and the highest risk.

Keywords: Croatian capital market, Markowitz model, fractional quadratic programming, portfolio optimization, transaction costs

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Authors: Saahith M. S., Sivakami R.

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In the realm of football analytics, particularly focusing on predicting football player performance, the ability to forecast player success accurately is of paramount importance for teams, managers, and fans. This study introduces an elaborate examination of predicting football player performance through the integration of data visualization methods and machine learning algorithms. The research entails the compilation of an extensive dataset comprising player attributes, conducting data preprocessing, feature selection, model selection, and model training to construct predictive models. The analysis within this study will involve delving into feature significance using methodologies like Select Best and Recursive Feature Elimination (RFE) to pinpoint pertinent attributes for predicting player performance. Various machine learning algorithms, including Random Forest, Decision Tree, Linear Regression, Support Vector Regression (SVR), and Artificial Neural Networks (ANN), will be explored to develop predictive models. The evaluation of each model's performance utilizing metrics such as Mean Squared Error (MSE) and R-squared will be executed to gauge their efficacy in predicting player performance. Furthermore, this investigation will encompass a top player analysis to recognize the top-performing players based on the anticipated overall performance scores. Nationality analysis will entail scrutinizing the player distribution based on nationality and investigating potential correlations between nationality and player performance. Positional analysis will concentrate on examining the player distribution across various positions and assessing the average performance of players in each position. Age analysis will evaluate the influence of age on player performance and identify any discernible trends or patterns associated with player age groups. The primary objective is to predict a football player's overall performance accurately based on their individual attributes, leveraging data-driven insights to enrich the comprehension of player success on the field. By amalgamating data visualization and machine learning methodologies, the aim is to furnish valuable tools for teams, managers, and fans to effectively analyze and forecast player performance. This research contributes to the progression of sports analytics by showcasing the potential of machine learning in predicting football player performance and offering actionable insights for diverse stakeholders in the football industry.

Keywords: football analytics, player performance prediction, data visualization, machine learning algorithms, random forest, decision tree, linear regression, support vector regression, artificial neural networks, model evaluation, top player analysis, nationality analysis, positional analysis

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Authors: Dwi Wijiastutik, Tarjo, Yuni Rimawati

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The purpose of this empirical study is to analyse how to the market reaction and the conservative degree changes on the adoption of International Financial Reporting Standart (IFRS) through Jakarta Islamic Index. The study also has given others additional analysis on the profitability, capital structure and size company toward IFRS adoption. The data collection methods used in this study reveals as secondary data and deep analysis to the company’s annual report and daily price stock at yahoo finance. We analyse 40 companies listed on Jakarta Islamic Index from 2010 to 2014. The result of the study concluded that IFRS has given a different on the depth analysis to the two of variance analysis: Moderated Regression Analysis and Wilcoxon Signed Rank to test developed hypotheses. Our result on the regression analysis shows that market response and conservatism principle is not significantly after IFRS Adoption in Jakarta Islamic Index. Furthermore, in addition, analysis on profitability, capital structure, and company size show that significantly after IFRS adoption. The findings of our study help investor by showing the impact of IFRS for making decided investment.

Keywords: IFRS, earnings response coefficient, conservatism principle

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Authors: Satinder Bhatia

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Limited Liability Partnerships (LLPs) was a concept introduced in India in 2009. Ever since then, there has been a fierce growth in the number of organizations registered as LLPs outpacing the number of registrations as private companies. Among other benefits extended to LLPs, the audit being mandated only for LLPs having a turnover of at least Rs 40 lakhs or capital contribution of Rs 25 lakhs, has been a major attraction. This has resulted in only about 10 per cent of LLPs coming under mandatory audit. Even for such companies, the accounting standards to be followed in the preparation of financial statements have not been specified. The Revised Indian Accounting Standards (Revised IndAS) which are aligned with IFRS to a great extent, may apply to LLPs only under limited conditions. Thus, the veracity of even the audited financial statements of LLPs can be questioned. If in future, these LLPs would like to list on a stock exchange to raise capital, there can be serious hurdles if investors do not find the financial statements to be reliable and consistent. LLPs are generally governed by country-specific rules in the area of accounts and audit. Thus, such rules vary across UK, EU and the USA. Some countries have adopted the IFRS for SMEs and since LLPs can be referred to as SMEs; they would come under the ambit of these IFRS provisions. Besides, as the scope of audit widens to cover qualitative information in addition to quantitative data, audit of LLPs has now acquired a new meaning and a new urgency as demands for at least limited purpose audits are arising from different stakeholders including lenders, suppliers, customers and joint venture partners.

Keywords: audit disclosures, audit quality, guidance for SMEs, non-audit services

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Authors: Hamed Jafargholi

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Global warming presents an immense environmental risk, causing detrimental impacts on ecological systems and putting coastal areas at risk. Implementing efficient measures to minimize greenhouse gas emissions and the use of fossil fuels is essential to reducing global warming. Vapor compression heat pumps provide a practical method for harnessing energy from waste heat sources and reducing energy consumption. However, traditional working fluids used in these heat pumps generally contain a significant global warming potential (GWP), which might cause severe greenhouse effects if they are released. The goal of the emphasis on low-GWP (below 150) refrigerants is to further the vapor compression heat pumps. A classification system for vapor compression heat pumps is offered, with different boundaries based on the needed heat temperature and advancements in heat pump technology. A heat pump could be classified as a low temperature heat pump (LTHP), medium temperature heat pump (MTHP), high temperature heat pump (HTHP), or ultra-high temperature heat pump (UHTHP). The HTHP/UHTHP border is 160 °C, the MTHP/HTHP and LTHP/MTHP limits are 100 and 60 °C, respectively. The refrigerant is one of the most important parts of a vapor compression heat pump system. Presently, the main ways to choose a refrigerant are based on ozone depletion potential (ODP) and GWP, with GWP being the lowest possible value and ODP being zero. Pure low-GWP refrigerants, such as natural refrigerants (R718 and R744), hydrocarbons (R290, R600), hydrofluorocarbons (R152a and R161), hydrofluoroolefins (R1234yf, R1234ze(E)), and hydrochlorofluoroolefin (R1233zd(E)), were selected as candidates for vapor compression heat pump systems based on these selection principles. The performance, characteristics, and potential uses of these low-GWP refrigerants in heat pump systems are investigated in this paper. As vapor compression heat pumps with pure low-GWP refrigerants become more common, more and more low-grade heat can be recovered. This means that energy consumption would decrease. The research outputs showed that the refrigerants R718 for UHTHP application, R1233zd(E) for HTHP application, R600, R152a, R161, R1234ze(E) for MTHP, and R744, R290, and R1234yf for LTHP application are appropriate. The selection of an appropriate refrigerant should, in fact, take into consideration two different environmental and thermodynamic points of view. It might be argued that, depending on the situation, a trade-off between these two groups should constantly be considered. The environmental approach is now far stronger than it was previously, according to the European Union regulations. This will promote sustainable energy consumption and social development in addition to assisting in the reduction of greenhouse gas emissions and the management of global warming.

Keywords: vapor compression, global warming potential, heat pumps, greenhouse

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Authors: S. Ping Ho, R. Dahal

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The global economy has drastically changed how firms operate and compete. Although the construction industry is ‘local’ by its nature, the internationalization of the construction industry has become an inevitable reality. As a result of global competition, staying domestic is no longer safe from competition and, on the contrary, to grow and become an MNE (multi-national enterprise) becomes one of the important strategies for a firm to survive in the global competition. For the successful entrance into competing markets, the firms need to re-define their competitive advantages and re-identify the sources of the competitive advantages. A firm’s initiation of internationalization is not necessarily a result of strategic planning but also involves certain idiosyncratic events that pave the path leading to a firm’s internationalization. For example, a local firm’s incidental or unintentional collaboration with an MNE can become the initiating point of its internationalization process. However, because of the intensive competition in today’s global movement, many firms were compelled to initiate their internationalization as a strategic response to the competition. Understandingly stepping in in the process of internationalization and appropriately implementing the strategies (in the process) at different stages lead the construction firms to a successful internationalization journey. This study is carried out to develop a model of the internationalization process, which derives appropriate strategies that the construction firms can implement at each stage. The proposed model integrates two major and complementary views of internationalization and expresses the dynamic process of internationalization in three stages, which are the pre-international (PRE) stage, the foreign direct investment (FDI) stage, and the multi-national enterprise (MNE) stage. The strategies implied in the proposed model are derived, focusing on capability building, market locations, and entry modes based on the resource-based views: value, rareness, imitability, and substitutability (VRIN). With the proposed dynamic process model the potential construction firms which are willing to expand their business market area can be benefitted. Strategies for internationalization, such as core competence strategy, market selection, partner selection, and entry mode strategy, can be derived from the proposed model. The internationalization process is expressed in two different forms. First, we discuss the construction internationalization process, identify the driving factor/s of the process, and explain the strategy formation in the process. Second, we define the stages of internationalization along the process and the corresponding strategies in each stage. The strategies may include how to exploit existing advantages for the competition at the current stage and develop or explore additional advantages appropriate for the next stage. Particularly, the additionally developed advantages will then be accumulated and drive forward the firm’s stage of internationalization, which will further determine the subsequent strategies, and so on and so forth, spiraling up the stages of a higher degree of internationalization. However, the formation of additional strategies for the next stage does not happen automatically, and the strategy evolution is based on the firm’s dynamic capabilities.

Keywords: construction industry, dynamic capabilities, internationalization process, internationalization strategies, strategic management

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Authors: Dmitry Fedoseyev

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The ultimate success of unmanned aerial vehicles (UAVs) depends largely on the effective control of their flight, especially in variable wind conditions. This paper investigates different approaches to selecting the optimal flight level to maximize the range of UAVs. We propose to consider methods based on mathematical models of atmospheric conditions, as well as the use of sensor data and machine learning algorithms to automatically optimize the flight level in real-time. The proposed approaches promise to improve the efficiency and range of UAVs in various wind conditions, which may have significant implications for the application of these systems in various fields, including geodesy, environmental surveillance, and search and rescue operations.

Keywords: drone, UAV, flight trajectory, wind-searching, efficiency

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Authors: C. Recchia, M. Bourel, B. Recchia

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Microorganisms (viruses, bacteria, fungi) are responsible for most communicable diseases, threatening human health. For domestic use, chemical agents are often criticized because of their potential dangerousness, and natural solutions are needed. Application of the “dry microfine steam” (DMS) technology was tested on a selection of common pathogens (SARS-CoV-2, enterovirus EV-71, human coronavirus 229E, E. coli, S. aureus, C. albicans), on different innate surfaces, for 5 to 10 seconds. Quantification of the remaining pathogens was performed, and the reduction rates ranged from 99.8% (S. aureus on plastic) to over 99.999%. DMS showed high efficacy in the elimination of common microorganisms and could be seen as a natural alternative to chemical agents to improve domestic hygiene.

Keywords: steam, SARS-CoV-2, bactericidal, virucidal, fungicidal, sterilization

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Authors: Isabel Moscol, Carlos J. Díaz, Ciro Rodríguez

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Hip replacement becomes necessary when a person suffers severe pain or considerable functional limitations and the best option to enhance their quality of life is through the replacement of the damaged joint. One of the main components in femoral prostheses is the stem which distributes the loads from the joint to the proximal femur. To preserve more bone stock and avoid weakening of the diaphysis, a short starting stem was selected, generated from the intramedullary morphology of the patient's femur. It ensures the implantability of the design and leads to geometric delimitation for personalized optimization with machine learning (ML) and metaheuristic algorithms. The present study attempts to design a cementless short stem to make the strain deviation before and after implantation close to zero, promoting its fixation and durability. Regression models developed to estimate the percentage change of maximum principal stresses were used as objective optimization functions by the metaheuristic algorithm. The latter evaluated different geometries of the short stem with the modification of certain parameters in oblique sections from the osteotomy plane. The optimized geometry reached a global stress shielding (SS) of 18.37% with a determination factor (R²) of 0.667. The predicted results favour implantability integration in the short stem optimization to effectively reduce SS in the proximal femur.

Keywords: machine learning techniques, metaheuristic algorithms, short-stem design, stress shielding, hip replacement

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Authors: Fadila Mohamed Mahmoud., Derkaoui I., Krimi Z.

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Date palm is a plant which presents a very good adaptation to the difficult conditions of the environment in particular to the drought and saline stress even at high temperatures. This adaptation is related on the biology of the plant and to the presence of a microflora endophyte which live inside its tissues. Fifteen endophytics fungi isolated from date palm were tested in vitro in the presence of various NaCl concentrations to select halotolerantes isolates. These same endophytes were tested for their colonizing capacity by the description of the production of secondary metabolites more particularly the enzymes (pectinases, proteases, and phosphorylases), and the production of antibiotics and growth hormones. Significant difference was observed between the isolates with respect to the tests carried out.

Keywords: Date palm, Halotolerantes, endophyte, Secondary metabolites.

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Authors: Igor Jerkovic

Abstract:

Biodiversity of flora provides many different nectar sources for the bees. Unifloral honeys possess distinctive flavours, mainly derived from their nectar sources (characteristic volatile organic components (VOCs)). Specific or nonspecific VOCs (chemical markers) could be used for unifloral honey characterisation as addition to the melissopalynologycal analysis. The main honey volatiles belong, in general, to three principal categories: terpenes, norisoprenoids, and benzene derivatives. Some of these substances have been described as characteristics of the floral source, and other compounds, like several alcohols, branched aldehydes, and furan derivatives, may be related to the microbial purity of honey processing and storage conditions. Selection of the extraction method for the honey volatiles profiling should consider that heating of the honey produce different artefacts and therefore conventional methods of VOCs isolation (such as hydrodistillation) cannot be applied for the honey. Two-way approach for the isolation of the honey VOCs was applied using headspace solid-phase microextraction (HS-SPME) and ultrasonic solvent extraction (USE). The extracts were analysed by gas chromatography and mass spectrometry (GC-MS). HS-SPME (with the fibers of different polarity such as polydimethylsiloxane/ divinylbenzene (PDMS/DVB) or divinylbenzene/carboxene/ polydimethylsiloxane (DVB/CAR/PDMS)) enabled isolation of high volatile headspace VOCs of the honey samples. Among them, some characteristic or specific compounds can be found such as 3,4-dihydro-3-oxoedulan (in Centaurea cyanus L. honey) or 1H-indole, methyl anthranilate, and cis-jasmone (in Citrus unshiu Marc. honey). USE with different solvents (mainly dichloromethane or the mixture pentane : diethyl ether 1 : 2 v/v) enabled isolation of less volatile and semi-volatile VOCs of the honey samples. Characteristic compounds from C. unshiu honey extracts were caffeine, 1H-indole, 1,3-dihydro-2H-indol-2-one, methyl anthranilate, and phenylacetonitrile. Sometimes, the selection of solvent sequence was useful for more complete profiling such as sequence I: pentane → diethyl ether or sequence II: pentane → pentane/diethyl ether (1:2, v/v) → dichloromethane). The extracts with diethyl ether contained hydroquinone and 4-hydroxybenzoic acid as the major compounds, while (E)-4-(r-1’,t-2’,c-4’-trihydroxy-2’,6’,6’-trimethylcyclo-hexyl)but-3-en-2-one predominated in dichloromethane extracts of Allium ursinum L. honey. With this two-way approach, it was possible to obtain a more detailed insight into the honey volatile and semi-volatile compounds and to minimize the risks of compound discrimination due to their partial extraction that is of significant importance for the complete honey profiling and identification of the chemical biomarkers that can complement the pollen analysis.

Keywords: honey chemical biomarkers, honey volatile compounds profiling, headspace solid-phase microextraction (HS-SPME), ultrasonic solvent extraction (USE)

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Authors: Agostinho Antunes

Abstract:

The completion of the human genome sequencing in 2003 opened a new perspective into the importance of whole genome sequencing projects, and currently multiple species are having their genomes completed sequenced, from simple organisms, such as bacteria, to more complex taxa, such as mammals. This voluminous sequencing data generated across multiple organisms provides also the framework to better understand the genetic makeup of such species and related ones, allowing to explore the genetic changes underlining the evolution of diverse phenotypic traits. Here, recent results from our group retrieved from comparative evolutionary genomic analyses of selected marine animal species will be considered to exemplify how gene novelty and gene enhancement by positive selection might have been determinant in the success of adaptive radiations into diverse habitats and lifestyles.

Keywords: marine genomics, evolutionary bioinformatics, human genome sequencing, genomic analyses

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Authors: Haifeng Wang, Haili Zhang

Abstract:

Most movie recommendation systems have been developed for customers to find items of interest. This work introduces a predictive model usable by small and medium-sized enterprises (SMEs) who are in need of a data-based and analytical approach to stock proper movies for local audiences and retain more customers. We used classification models to extract features from thousands of customers’ demographic, behavioral and social information to predict their movie genre preference. In the implementation, a Gaussian kernel support vector machine (SVM) classification model and a logistic regression model were established to extract features from sample data and their test error-in-sample were compared. Comparison of error-out-sample was also made under different Vapnik–Chervonenkis (VC) dimensions in the machine learning algorithm to find and prevent overfitting. Gaussian kernel SVM prediction model can correctly predict movie genre preferences in 85% of positive cases. The accuracy of the algorithm increased to 93% with a smaller VC dimension and less overfitting. These findings advance our understanding of how to use machine learning approach to predict customers’ preferences with a small data set and design prediction tools for these enterprises.

Keywords: computational social science, movie preference, machine learning, SVM

Procedia PDF Downloads 254

Authors: Faten Khanfir, Mohamed Tlili, Ali Medeb Hamrouni, Raki Selmi, M. S. Khalfi, Faten Ben Amor

Abstract:

Introduction: dental implant failure is a major concern for the clinician and the patient. Objectives: The aim of our study is to investigate the way in which 100 Tunisian dentists carried implant treatment for their patients from the early phase of planning and selection of patients to the placement of the implant in order to look for the implant failure factors. Results: significant correlations were found between failure rates > 5 and their corresponding factors as the number of implants placed (p = 0.001<0, 05), smoking (0.046 <0.05), unbalanced diabetes (0.03<0.05), aseptic protocol (= 0.004< 0.05) and the drilling speed (0,002<0.05) Conclusion: It seems that the number of implant placed, smoking, diabetes, aseptic protocol, and the drilling speed may contribute to dental implant failure.

Keywords: failure, implants, survey, risk, osseointegration

Procedia PDF Downloads 179

Authors: Masoumeh Mofarrah, Amir NahanMoghadam

Abstract:

Higher oil prices and increasing oil demand are main reasons for great attention to Enhanced Oil Recovery (EOR). Comprehensive researches have been accomplished to develop, appraise, and improve EOR methods and their application. Recently, Artificial Intelligence (AI) gained popularity in petroleum industry that can help petroleum engineers to solve some fundamental petroleum engineering problems such as reservoir simulation, EOR project risk analysis, well log interpretation and well test model selection. This study presents a historical overview of most popular AI tools including neural networks, genetic algorithms, fuzzy logic, and expert systems in petroleum industry and discusses two case studies to represent the application of two mentioned AI methods for selecting an appropriate EOR method based on reservoir characterization infeasible and effective way.

Keywords: artificial intelligence, EOR, neural networks, expert systems

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Authors: Pirom Thangchitpianpol, Suttipong Jumroonrut

Abstract:

This research investigates the distribution of food demand for animal food and the optimum amount of that food production at minimum cost. The data consist of customer purchase orders for the food of laying hens, price of food for laying hens, cost per unit for the food inventory, cost related to food of laying hens in which the food is out of stock, such as fine, overtime, urgent purchase for material. They were collected from January, 1990 to December, 2013 from a factory in Nakhonratchasima province. The collected data are analyzed in order to explore the distribution of the monthly food demand for the laying hens and to see the rate of inventory per unit. The results are used in a stochastic linear programming model for aggregate planning in which the optimum production or minimum cost could be obtained. Programming algorithms in MATLAB and tools in Linprog software are used to get the solution. The distribution of the food demand for laying hens and the random numbers are used in the model. The study shows that the distribution of monthly food demand for laying has a normal distribution, the monthly average amount (unit: 30 kg) of production from January to December. The minimum total cost average for 12 months is Baht 62,329,181.77. Therefore, the production planning can reduce the cost by 14.64% from real cost.

Keywords: animal food, stochastic linear programming, aggregate planning, production planning, demand uncertainty

Procedia PDF Downloads 374