Search results for: mitigation techniques

Authors: Hadeer M. Sayed, Hesham E. El Deeb, Shereen A. Taie

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In the big data era, we are facing a diversity of datasets from different sources in different domains that describe a single life event. These datasets consist of multiple modalities, each of which has a different representation, distribution, scale, and density. Multimodal fusion is the concept of integrating information from multiple modalities in a joint representation with the goal of predicting an outcome through a classification task or regression task. In this paper, multimodal fusion techniques are classified into two main classes: model-agnostic techniques and model-based approaches. It provides a comprehensive study of recent research in each class and outlines the benefits and limitations of each of them. Furthermore, the audiovisual speech recognition task is expressed as a case study of multimodal data fusion approaches, and the open issues through the limitations of the current studies are presented. This paper can be considered a powerful guide for interested researchers in the field of multimodal data fusion and audiovisual speech recognition particularly.

Keywords: multimodal data, data fusion, audio-visual speech recognition, neural networks

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Authors: Rashid Mahmood, Muhammad Junaid Sarwar

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Mobile ad hoc networks (MANETs) use has been well-known from the last few years in the many applications, like mission critical applications. In the (MANETS) prevention method is not adequate as the security concerned, so the detection method should be added to the security issues in (MANETs). The authentication and encryption is considered the first solution of the MANETs problem where as now these are not sufficient as MANET use is increasing. In this paper we are going to present the concept of intrusion detection and then survey some of major intrusion detection techniques in MANET and aim to comparing in some important fields.

Keywords: MANET, IDS, intrusions, signature, detection, prevention

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Authors: Mikel Alonso López

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In the first decade of the twenty-first century, advanced imaging techniques began to be applied for neuroscience research. The Functional Magnetic Resonance Imaging (fMRI) is one of the most important and most used research techniques for the investigation of emotions, because of its ease to observe the brain areas that oxygenate when performing certain tasks. In this research, we make a review about the main research carried out on the influence of the emotions in the decision-making process that is exposed by using the fMRI.

Keywords: decision making, emotions, fMRI, consumer behaviour

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Authors: U.K. Sarkar, G. Karnatak, P. Mishal, Lianthuamluaia, S. Kumari, S.K. Das, B.K. Das

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Inland open water fisheries provide food, income, livelihood and nutritional security to millions of fishers across the globe. However, the open water ecosystem and fisheries are threatened due to climate change and anthropogenic pressures, which are more visible in the recent six decades, making the resources vulnerable. Understanding the interaction between meteorological parameters and inland fisheries is imperative to develop mitigation and adaptation strategies. As per IPCC 5th assessment report, the earth is warming at a faster rate in recent decades. Global mean surface temperature (GMST) for the decade 2006–2015 (0.87°C) was 6 times higher than the average over the 1850–1900 period. The direct and indirect impacts of climatic parameters on the ecology of fisheries ecosystem have a great bearing on fisheries due to alterations in fish physiology. The impact of meteorological factors on ecosystem health and fish food organisms brings about changes in fish diversity, assemblage, reproduction and natural recruitment. India’s average temperature has risen by around 0.7°C during 1901–2018. The studies show that the mean air temperature in the Ganga basin has increased in the range of 0.20 - 0.47 °C and annual rainfall decreased in the range of 257-580 mm during the last three decades. The studies clearly indicate visible impacts of climatic and environmental factors on inland open water fisheries. Besides, a significant reduction in-depth and area (37.20–57.68% reduction), diversity of natural indigenous fish fauna (ranging from 22.85 to 54%) in wetlands and progression of trophic state from mesotrophic to eutrophic were recorded. In this communication, different applications of biometeorology in inland fisheries management with special reference to the assessment of ecosystem and species vulnerability to climatic variability and change have been discussed. Further, the paper discusses the impact of climate anomaly and extreme climatic events on inland fisheries and emphasizes novel modeling approaches for understanding the impact of climatic and environmental factors on reproductive phenology for identification of climate-sensitive/resilient fish species for the adoption of climate-smart fisheries in the future. Adaptation and mitigation strategies to enhance fish production and the role of culture-based fisheries and enclosure culture in converting sequestered carbon into blue carbon have also been discussed. In general, the type and direction of influence of meteorological parameters on fish biology in open water fisheries ecosystems are not adequately understood. The optimum range of meteorological parameters for sustaining inland open water fisheries is yet to be established. Therefore, the application of biometeorology in inland fisheries offers ample scope for understanding the dynamics in changing climate, which would help to develop a database on such least, addressed research frontier area. This would further help to project fisheries scenarios in changing climate regimes and develop adaptation and mitigation strategies to cope up with adverse meteorological factors to sustain fisheries and to conserve aquatic ecosystem and biodiversity.

Keywords: biometeorology, inland fisheries, aquatic ecosystem, modeling, India

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Authors: E. A. Krasikov

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As a main barrier against radioactivity outlet reactor pressure vessel (RPV) is a key component in terms of NPP safety. Therefore, present-day demands in RPV reliability enhance have to be met by all possible actions for RPV in-service embrittlement mitigation. Annealing treatment is known to be the effective measure to restore the RPV metal properties deteriorated by neutron irradiation. There are two approaches to annealing. The first one is so-called ‘dry’ high temperature (~475°C) annealing. It allows obtaining practically complete recovery, but requires the removal of the reactor core and internals. External heat source (furnace) is required to carry out RPV heat treatment. The alternative approach is to anneal RPV at a maximum coolant temperature which can be obtained using the reactor core or primary circuit pumps while operating within the RPV design limits. This low temperature «wet» annealing, although it cannot be expected to produce complete recovery, is more attractive from the practical point of view especially in cases when the removal of the internals is impossible. The first RPV «wet» annealing was done using nuclear heat (US Army SM-1A reactor). The second one was done by means of primary pumps heat (Belgian BR-3 reactor). As a rule, there is no recovery effect up to annealing and irradiation temperature difference of 70°C. It is known, however, that along with radiation embrittlement neutron irradiation may mitigate the radiation damage in metals. Therefore, we have tried to test the possibility to use the effect of radiation-induced ductilization in ‘wet’ annealing technology by means of nuclear heat utilization as heat and neutron irradiation sources at once. In support of the above-mentioned conception the 3-year duration reactor experiment on 15Cr3NiMoV type steel with preliminary irradiation at operating PWR at 270°C and following extra irradiation (87 h at 330°C) at IR-8 test reactor was fulfilled. In fact, embrittlement was partly suppressed up to value equivalent to 1,5 fold neutron fluence decrease. The degree of recovery in case of radiation enhanced annealing is equal to 27% whereas furnace annealing results in zero effect under existing conditions. Mechanism of the radiation-induced damage mitigation is proposed. It is hoped that «wet » annealing technology will help provide a better management of the RPV degradation as a factor affecting the lifetime of nuclear power plants which, together with associated management methods, will help facilitate safe and economic long-term operation of PWRs.

Keywords: controlling, embrittlement, radiation, steel, wet annealing

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Authors: Gadisa Demie, Mesele Negash, Zerihun Asrat, Lojka Bohdan

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Deforestation and forest degradation in the tropics have led to significant carbon (C) emissions. Agroforestry (AF) is a suitable land-use option for tackling such declines in ecosystem services, including climate change mitigation. However, it is unclear how biomass models, AF practices, and socio-ecological factors determine these roles, which hinders the implementation of climate change mitigation initiatives. This study aimed to estimate the ecosystem C stocks of the studied AF practices in relation to socio-ecological variables in central Ethiopia. Out of 243 AF farms inventoried, 108 were chosen at random from three AF practices to estimate their biomass and soil organic carbon. A total of 432 soil samples were collected from 0–30 and 30–60 cm soil depths; 216 samples were taken for each soil organic carbon fraction (%C) and bulk density computation. The study found that the currently developed allometric equations were the most accurate to estimate biomass C for trees growing in the landscape when compared to previous models. The study found higher overall biomass C in woodlots (165.62 Mg ha-¹) than in homegardens (134.07 Mg ha-¹) and parklands (19.98 Mg ha-¹). Conversely, overall, SOC was higher for homegardens (143.88 Mg ha-¹), but lower for parklands (53.42 Mg ha-¹). The ecosystem C stock was comparable between homegardens (277.95 Mg ha-¹) and woodlots (275.44 Mg ha-¹). The study found that elevation, wealthy levels, AF farm age, and size have a positive and significant (P < 0.05) effect on overall biomass and ecosystem C stocks but non-significant with slope (P > 0.05). Similarly, SOC increased with increasing elevation, AF farm age, and wealthy status but decreased with slope and non-significant with AF farm size. The study also showed that species diversity had a positive (P <0.05) effect on overall biomass C stocks in homegardens. The overall study highlights that AF practices have a great potential to lock up more carbon in biomass and soils; however, these potentials were determined by socioecological variables. Thus, these factors should be considered in management strategies that preserve trees in agricultural landscapes in order to mitigate climate change and support the livelihoods of farmers.

Keywords: agricultural landscape, biomass, climate change, soil organic carbon

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Authors: Ahlam Elkilani, Bayan Elsheikh Ali, Rasha Abu Romman, Amjed Al-mousa, Belal Sababha

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The amount of energy the world uses doubles every 20 years. Green homes play an important role in reducing the residential energy demand. This paper presents a platform that is intended to learn the behavior of home residents and build a profile about their habits and actions. The proposed resident aware home controller intervenes in the operation of home appliances in order to save energy without compromising the convenience of the residents. The presented platform can be used to simulate the actions and movements happening inside a home. The paper includes several optimization techniques that are meant to save energy in the home. In addition, several test scenarios are presented that show how the controller works. Moreover, this paper shows the computed actual savings when each of the presented techniques is implemented in a typical home. The test scenarios have validated that the techniques developed are capable of effectively saving energy at homes.

Keywords: green home, resident aware, resident profile, activity learning, machine learning

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Authors: C. Manjula, Lilly Florence

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Software technology is developing rapidly which leads to the growth of various industries. Now-a-days, software-based applications have been adopted widely for business purposes. For any software industry, development of reliable software is becoming a challenging task because a faulty software module may be harmful for the growth of industry and business. Hence there is a need to develop techniques which can be used for early prediction of software defects. Due to complexities in manual prediction, automated software defect prediction techniques have been introduced. These techniques are based on the pattern learning from the previous software versions and finding the defects in the current version. These techniques have attracted researchers due to their significant impact on industrial growth by identifying the bugs in software. Based on this, several researches have been carried out but achieving desirable defect prediction performance is still a challenging task. To address this issue, here we present a machine learning based hybrid technique for software defect prediction. First of all, Genetic Algorithm (GA) is presented where an improved fitness function is used for better optimization of features in data sets. Later, these features are processed through Decision Tree (DT) classification model. Finally, an experimental study is presented where results from the proposed GA-DT based hybrid approach is compared with those from the DT classification technique. The results show that the proposed hybrid approach achieves better classification accuracy.

Keywords: decision tree, genetic algorithm, machine learning, software defect prediction

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Authors: Syeda Mainaaz Unnisa, Deepa Suresh

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Due to the increase in usage of public domain channels, such as the internet, and communication technology, there is a concern about the protection of intellectual property and security threats. This interest has led to growth in researching and implementing techniques for information hiding. Steganography is the art and science of hiding information in a private manner such that its existence cannot be recognized. Communication using steganographic techniques makes not only the secret message but also the presence of hidden communication, invisible. Steganalysis is the art of detecting the presence of this hidden communication. Parallel to steganography, steganalysis is also gaining prominence, since the detection of hidden messages can prevent catastrophic security incidents from occurring. Steganalysis can also be incredibly helpful in identifying and revealing holes with the current steganographic techniques, which makes them vulnerable to attacks. Through the formulation of new effective steganalysis methods, further research to improve the resistance of tested steganography techniques can be developed. Feature-based steganalysis method for JPEG images calculates the features of an image using the L1 norm of the difference between a stego image and the calibrated version of the image. This calibration can help retrieve some of the parameters of the cover image, revealing the variations between the cover and stego image and enabling a more accurate detection. Applying this method to various steganographic schemes, experimental results were compared and evaluated to derive conclusions and principles for more protected JPEG steganography.

Keywords: cover image, feature-based steganalysis, information hiding, steganalysis, steganography

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Authors: Zaynab El Khattabi, Youness Tabii, Abdelhamid Benkaddour

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Nowadays, huge amount of multimedia repositories make the browsing, retrieval and delivery of video contents very slow and even difficult tasks. Video summarization has been proposed to improve faster browsing of large video collections and more efficient content indexing and access. In this paper, we focus on approaches to video summarization. The video summaries can be generated in many different forms. However, two fundamentals ways to generate summaries are static and dynamic. We present different techniques for each mode in the literature and describe some features used for generating video summaries. We conclude with perspective for further research.

Keywords: video summarization, static summarization, video skimming, semantic features

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Authors: Amandeep Kaur

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Digital Image processing is a fundamental tool to perform various operations on the digital images for pattern recognition, noise removal and feature extraction. In this paper noise removal technique has been described for various types of noises. This paper comprises discussion about various noises available in the image due to different environmental, accidental factors. In this paper, various de-noising approaches have been discussed that utilize different wavelets and filters for de-noising. By analyzing various papers on image de-noising we extract that wavelet based de-noise approaches are much effective as compared to others.

Keywords: de-noising techniques, edges, image, image processing

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Authors: Abiodun Ladanu Ajala, Wuyi Oke

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Mathematical simulation and optimization models packaged within interactive computer programs provide a common way for planners and managers to predict the behaviour of any proposed water resources system design or management policy before it is implemented. Modeling presents a principal technique of predicting the behaviour of the proposed infrastructural designs or management policies. Models can be developed and used to help identify specific alternative plans that best meet those objectives. This study discusses various types of models, their development, architecture, data requirements, and applications in the field of engineering. It also outlines the advantages and limitations of each the optimization and simulation models presented. The techniques explored in this review include; dynamic programming, linear programming, fuzzy optimization, evolutionary algorithms and finally artificial intelligence techniques. Previous studies carried out using some of the techniques mentioned above were reviewed, and most of the results from different researches showed that indeed optimization and simulation provides viable alternatives and predictions which form a basis for decision making in building engineering structures and also in engineering planning and management.

Keywords: linear programming, mutation, optimization, simulation

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Authors: Isabela Vieira, Leonor Carvalho Garcez, Adalmiro Pereira, Tânia Teixeira

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In the present work, we aim to analyse IKEA as a company, by focusing on its development, financial analysis and future benchmarks, as well as applying some of the knowledge learned in class, namely hedging and other financial risk mitigation solutions, to understand how IKEA navigates and protects itself from risk. The decision that led us to choose IKEA for our casework has to do with the long history of the company since the 1940s and its high internationalization in 63 different markets. The company also has clear financial reports which aided us in the making of the present essay and naturally, was a factor that contributed to our decision.

Keywords: Ikea, financial risk, risk management, hedge

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Authors: Deepak Singh, Rail Kuliev

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This research article explores the significance of advanced data visualization techniques in enhancing decision-making processes within the oil and gas exploration and production domain. With the oil and gas industry facing numerous challenges, effective interpretation and analysis of vast and diverse datasets are crucial for optimizing exploration strategies, production operations, and risk assessment. The article highlights the importance of data visualization in managing big data, aiding the decision-making process, and facilitating communication with stakeholders. Various advanced data visualization techniques, including 3D visualization, augmented reality (AR), virtual reality (VR), interactive dashboards, and geospatial visualization, are discussed in detail, showcasing their applications and benefits in the oil and gas sector. The article presents case studies demonstrating the successful use of these techniques in optimizing well placement, real-time operations monitoring, and virtual reality training. Additionally, the article addresses the challenges of data integration and scalability, emphasizing the need for future developments in AI-driven visualization. In conclusion, this research emphasizes the immense potential of advanced data visualization in revolutionizing decision-making processes, fostering data-driven strategies, and promoting sustainable growth and improved operational efficiency within the oil and gas exploration and production industry.

Keywords: augmented reality (AR), virtual reality (VR), interactive dashboards, real-time operations monitoring

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Authors: Justin P. Pool, Haruyo Yoshida

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This research focuses on the effects of creating an English as a foreign language curriculum that combines online learning and classroom teaching in a complementary manner. Through pre- and post-test results, teacher observation, and learner reflection, it will be shown that learners can benefit from online programs focusing on receptive skills if combined with a communicative classroom environment that encourages learners to develop their productive skills. Much research has lamented the fact that many modern mobile assisted language learning apps do not take advantage of the affordances of modern technology by focusing only on receptive skills rather than inviting learners to interact with one another and develop communities of practice. This research takes into account the realities of the state of such apps and focuses on how to best create a curriculum that complements apps which focus on receptive skills. The research involved 15 adult learners working for a business in Japan simultaneously engaging in 1) a commercial online English language learning application that focused on reading, listening, grammar, and vocabulary and 2) a 15-week class focused on communicative language teaching, presentation skills, and mitigation of error aversion tendencies. Participants of the study experienced large gains on a standardized test, increased motivation and willingness to communicate, and asserted that they felt more confident regarding English communication. Moreover, learners continued to study independently at higher rates after the study than they had before the onset of the program. This paper will include the details of the program, reveal the improvement in test scores, share learner reflections, and critically view current evaluation models for mobile assisted language learning applications.

Keywords: adult learners, communicative language teaching, mobile assisted language learning, motivation

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Authors: Ibtihal D. Mustafa, Mawia A. Hassan

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Tumor segmentation from MRI image is important part of medical images experts. This is particularly a challenging task because of the high assorting appearance of tumor tissue among different patients. MRI images are advance of medical imaging because it is give richer information about human soft tissue. There are different segmentation techniques to detect MRI brain tumor. In this paper, different procedure segmentation methods are used to segment brain tumors and compare the result of segmentations by using correlation and structural similarity index (SSIM) to analysis and see the best technique that could be applied to MRI image.

Keywords: MRI, segmentation, correlation, structural similarity

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Authors: Devatha Kalyan Kumar, Aravindraj D., Sadathulla A.

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Big data are now rapidly expanding in all engineering and science and many other domains. The potential of large or massive data is undoubtedly significant, make sense to require new ways of thinking and learning techniques to address the various big data challenges. Machine learning is continuously unleashing its power in a wide range of applications. In this paper, the latest advances and advancements in the researches on machine learning for big data processing. First, the machine learning techniques methods in recent studies, such as deep learning, representation learning, transfer learning, active learning and distributed and parallel learning. Then focus on the challenges and possible solutions of machine learning for big data.

Keywords: active learning, big data, deep learning, machine learning

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Authors: S. Abera, T. Gidey, W. Terefe

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Introduction: Testing for HIV is the key entry point to HIV prevention, treatment, and care and support services. Hence, predictive data mining techniques can greatly benefit to analyze and discover new patterns from huge datasets like that of EDHS 2011 data. Objectives: The objective of this study is to build a predictive modeling for HIV testing and identify determinants of HIV testing for adults with age above fourteen years using data mining techniques. Methods: Cross-Industry Standard Process for Data Mining (CRISP-DM) was used to predict the model for HIV testing and explore association rules between HIV testing and the selected attributes among adult Ethiopians. Decision tree, Naïve-Bayes, logistic regression and artificial neural networks of data mining techniques were used to build the predictive models. Results: The target dataset contained 30,625 study participants; of which 16, 515 (53.9%) were women. Nearly two-fifth; 17,719 (58%), have never been tested for HIV while the rest 12,906 (42%) had been tested. Ethiopians with higher wealth index, higher educational level, belonging 20 to 29 years old, having no stigmatizing attitude towards HIV positive person, urban residents, having HIV related knowledge, information about family planning on mass media and knowing a place where to get testing for HIV showed an increased patterns with respect to HIV testing. Conclusion and Recommendation: Public health interventions should consider the identified determinants to promote people to get testing for HIV.

Keywords: data mining, HIV, testing, ethiopia

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Authors: Maximilian Elsen, Frank Tietze

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The development and diffusion of green technologies are crucial for achieving our ambitious climate targets. The Paris Agreement commits its members to develop strategies for achieving net zero greenhouse gas emissions by the second half of the century. Governments, executives, and academics are working on net-zero strategies and the business of rating organisations on their environmental, social and governance (ESG) performance has grown tremendously in its public interest. ESG data is now commonly integrated into traditional investment analysis and an important factor in investment decisions. Creating these metrics, however, is inherently challenging as environmental and social impacts are hard to measure and uniform requirements on ESG reporting are lacking. ESG metrics are often incomplete and inconsistent as they lack fully accepted reporting standards and are often of qualitative nature. This study explores the use of patent data for assessing the environmental performance of companies by focusing on their patented inventions in the space of climate change mitigation and adaptation technologies (CCMAT). The present study builds on the successful identification of CCMAT patents. In this context, the study adopts the Y02 patent classification, a fully cross-sectional tagging scheme that is fully incorporated in the Cooperative Patent Classification (CPC), to identify Climate Change Adaptation Technologies. The Y02 classification was jointly developed by the European Patent Office (EPO) and the United States Patent and Trademark Office (USPTO) and provides means to examine technologies in the field of mitigation and adaptation to climate change across relevant technologies. This paper develops sustainability-related metrics for firm-level patent portfolios. We do so by adopting a three-step approach. First, we identify relevant CCMAT patents based on their classification as Y02 CPC patents. Second, we examine the technological strength of the identified CCMAT patents by including more traditional metrics from the field of patent analytics while considering their relevance in the space of CCMAT. Such metrics include, among others, the number of forward citations a patent receives, as well as the backward citations and the size of the focal patent family. Third, we conduct our analysis on a firm level by sector for a sample of companies from different industries and compare the derived sustainability performance metrics with the firms’ environmental and financial performance based on carbon emissions and revenue data. The main outcome of this research is the development of sustainability-related metrics for firm-level environmental performance based on patent data. This research has the potential to complement existing ESG metrics from an innovation perspective by focusing on the environmental performance of companies and putting them into perspective to conventional financial performance metrics. We further provide insights into the environmental performance of companies on a sector level. This study has implications of both academic and practical nature. Academically, it contributes to the research on eco-innovation and the literature on innovation and intellectual property (IP). Practically, the study has implications for policymakers by deriving meaningful insights into the environmental performance from an innovation and IP perspective. Such metrics are further relevant for investors and potentially complement existing ESG data.

Keywords: climate change mitigation, innovation, patent portfolios, sustainability

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Authors: Samira Karimi-Mansoub, Rahem Abri

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Currently, users expect high quality and personalized information from search results. To satisfy user’s needs, personalized approaches to web search have been proposed. These approaches can provide the most appropriate answer for user’s needs by using user context and incorporating information about query provided by combining search technologies. To carry out personalized web search, there is a need to make different techniques on whole of user search process. There are the number of possible deployment of personalized approaches such as personalized web search, personalized recommendation, personalized summarization and filtering systems and etc. but the common feature of all approaches in various domains is that user modeling is utilized to provide personalized information from the Web. So the most important work in personalized approaches is user model mining. User modeling applications and technologies can be used in various domains depending on how the user collected information may be extracted. In addition to, the used techniques to create user model is also different in each of these applications. Since in the previous studies, there was not a complete survey in this field, our purpose is to present a survey on applications and techniques of user modeling from the viewpoint of improvement in search results by considering the existing literature and researches.

Keywords: filtering systems, personalized web search, user modeling, user search behavior

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Authors: Neşe Taşci, Soner Çubuk, Ece Kök Yetimoğlu, M. Vezir Kahraman

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Bisphenol-A (BPA), 2,2-bis(4-hydroxyphenly)propane, is one of the highest usage volume chemicals in the world. Studies showed that BPA maybe has negative effects on the central nervous system, immune and endocrine systems. Several of analytical methods for the analysis of BPA have been reported including electrochemical processes, chemical oxidation, ozonization, spectrophotometric, chromatographic techniques. Compared with other conventional analytical techniques, optic sensors are reliable, providing quick results, low cost, easy to use, stands out as a much more advantageous method because of the high precision and sensitivity. In this work, a new photocured polymeric fluorescence sensor was prepared and characterized for Bisphenol-A (BPA) analysis. Characterization of the membrane was carried out by Attenuated Total Reflectance Fourier Transform Infrared Spectroscopy (ATR-FTIR) and Scanning Electron Microscope (SEM) techniques. The response characteristics of the sensor including dynamic range, pH effect and response time were systematically investigated. Acknowledgment: This work was supported by the Scientific and Technological Research Council of Turkey (TUBITAK) under Grant 115Y469.

Keywords: bisphenol-a, fluorescence, photopolymerization, polymeric sensor

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Authors: Mustapha Yakubu, Ahmed Usman, Hashim Ambursa

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This paper presents the findings of a research which sought to investigate techniques used to improve cost and time control of construction projects management practice in Nigeria. However, there is limited research on issues surrounding the practical usage of these techniques. Data were collected through a questionnaire distributed to construction experts through a survey conducted on the 100 construction organisations and 50 construction consultancy firms in the Nigeria aimed at identifying common project cost and time control practices and factors inhibiting effective project control in practice. The study reveals that despite the vast application of control techniques a high proportion of respondents still experienced cost and time overruns on a significant proportion of their projects. Analysis of the survey results concluded that more effort should be geared at the management of the identified top project control inhibiting factors. This paper has outlined some measures for mitigating these inhibiting factors so that the outcome of project time and cost control can be improved in practice.

Keywords: construction project, cost control, Nigeria, time control

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Authors: V. K. Banga

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Robots are now playing a very promising role in industries. Robots are commonly used in applications in repeated operations or where operation by human is either risky or not feasible. In most of the industrial applications, robotic arm manipulators are widely used. Robotic arm manipulator with two link or three link structures is commonly used due to their low degrees-of-freedom (DOF) movement. As the DOF of robotic arm increased, complexity increases. Instrumentation involved with robotics plays very important role in order to interact with outer environment. In this work, optimal control for movement of various DOFs of robotic arm using various soft computing techniques has been presented. We have discussed about different robotic structures having various DOF robotics arm movement. Further stress is on kinematics of the arm structures i.e. forward kinematics and inverse kinematics. Trajectory planning of robotic arms using soft computing techniques is demonstrating the flexibility of this technique. The performance is optimized for all possible input values and results in optimized movement as resultant output. In conclusion, soft computing has been playing very important role for achieving optimized movement of robotic arm. It also requires very limited knowledge of the system to implement soft computing techniques.

Keywords: artificial intelligence, kinematics, robotic arm, neural networks, fuzzy logic

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Authors: Khadijat T. Bamigbade, Olufade F. W. Onifade

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The field of automatic facial expression analysis has been an active research area in the last two decades. Its vast applicability in various domains has drawn so much attention into developing techniques and dataset that mirror real life scenarios. Many techniques such as Local Binary Patterns and its variants (CLBP, LBP-TOP) and lately, deep learning techniques, have been used for facial expression recognition. However, the problem of occlusion has not been sufficiently handled, making their results not applicable in real life situations. This paper develops a simple, yet highly efficient method tagged Local Binary Pattern-Histogram of Gradient (LBP-HOG) with occlusion detection in face image, using a multi-class SVM for Action Unit and in turn expression recognition. Our method was evaluated on three publicly available datasets which are JAFFE, CK, SFEW. Experimental results showed that our approach performed considerably well when compared with state-of-the-art algorithms and gave insight to occlusion detection as a key step to handling expression in wild.

Keywords: automatic facial expression analysis, local binary pattern, LBP-HOG, occlusion detection

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Authors: Michael Salib, Samer Ezeldin, Ahmed Waly

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There are numerous challenges and problems facing the construction industry in several countries in the Middle East, as a result of numerous economic and political effects. As an example in Egypt, several construction companies have shut down and left the market since 2016. The closure of these companies occurred, as they did not respond with the suitable techniques and strategies that will enable them to survive during this economic turmoil period. A research is conducted in order to identify adequate strategies to be implemented by the Egyptian contractors that could allow them survive and keep competing during such economic hardship period. Two different techniques were used in order to identify these startegies. First, a deep research were conducted on the companies located in countries that suffered similar economic harship to identify the strategies they used in order to survive. Second, interviews were conducted with experts in the construction field in order to list the effective strategies they used that allowed them to survive. Moreover, at the end of each interview, the experts were asked to rate the applicability of the previously identified strategies used in the foreign countries, then the efficiency of each strategy if used in Egypt. A framework model is developed in order to assist the construction companies in choosing the suitable techniques to their company size, through identifying the top ranked strategies and techniques that should be adopted by the company based on the parameters given to the model. In order to verify this framework, the financial statements of two leading companies in the Egyptian construction market were studied. The first Contractor has applied nearly all the top ranked strategies identified in this paper, while the other contractor has applied only few of the identified top ranked strategies. Finally, another expert interviews were conducted in order to validate the framework. These experts were asked to test the model and rate through a questionnaire its applicability and effectiveness.

Keywords: construction management, economic hardship, recession, survive

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Authors: Mohammad Alqahtani, Eric Atwell

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This paper reviews and classifies most of the important types of search techniques that have been applied on the holy Quran. Then, it addresses the limitations in these techniques. Additionally, this paper surveys most existing Quranic ontologies and what are their deficiencies. Finally, it explains a new search tool called: A semantic search tool for Al Quran based on Qur’anic ontologies. This tool will overcome all limitations in the existing Quranic search applications.

Keywords: holy Quran, natural language processing (NLP), semantic search, information retrieval (IR), ontology

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Authors: Venkata Reddy Kota

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These days, the industrial trend is moving away from heavy and bulky passive components to power converter systems that use more and more semiconductor elements. Also, it is difficult to connect the traditional converters to the high and medium voltage. For these reasons, a new family of multilevel inverters has appeared as a solution for working with higher voltage levels. Different modulation topologies like Sinusoidal Pulse Width Modulation (SPWM), Selective Harmonic Elimination Pulse Width Modulation (SHE-PWM) are available for multilevel inverters. In this work, different hybrid modulation techniques which are combination of fundamental frequency modulation and multilevel sinusoidal-modulation are compared. The main characteristic of these modulations are reduction of switching losses with good harmonic performance and balanced power loss dissipation among the device. The proposed hybrid modulation schemes are developed and simulated in Matlab/Simulink for cascaded H-bridge inverter. The results validate the applicability of the proposed schemes for cascaded multilevel inverter.

Keywords: hybrid PWM techniques, cascaded multilevel inverters, switching loss minimization

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Authors: Chloe E. Edwards, Yasaman Shahtaheri

Abstract:

Risk management plays a fundamental role in project planning and delivery. The purpose of incorporating risk management into project management practices is to identify and address uncertainties related to key project-related activities. The uncertainties, known as risk events, can relate to project deliverables that are quantifiable and are often measured by impact to project schedule, cost, or environmental impact. Risk management should be incorporated as an iterative practice throughout the planning, execution, and commissioning phases of a project. This paper specifically examines how risk management contributes to effective project planning and delivery through a case study of a transportation project. This case study focused solely on impacts to project schedule regarding three milestones: readiness for delivery, readiness for testing and commissioning, and completion of the facility. The case study followed the ISO 31000: Risk Management – Guidelines. The key factors that are outlined by these guidelines include understanding the scope and context of the project, conducting a risk assessment including identification, analysis, and evaluation, and lastly, risk treatment through mitigation measures. This process requires continuous consultation with subject matter experts and monitoring to iteratively update the risks accordingly. The risk identification process led to a total of fourteen risks related to design, permitting, construction, and commissioning. The analysis involved running 1,000 Monte Carlo simulations through @RISK 8.0 Industrial software to determine potential milestone completion dates based on the project baseline schedule. These dates include the best case, most likely case, and worst case to provide an estimated delay for each milestone. Evaluation of these results provided insight into which risks were the highest contributors to the projected milestone completion dates. Based on the analysis results, the risk management team was able to provide recommendations for mitigation measures to reduce the likelihood of risks occurring. The risk management team also provided recommendations for managing the identified risks and project activities moving forward to meet the most likely or best-case milestone completion dates.

Keywords: construction management, monte carlo simulation, project delivery, risk assessment, transportation engineering

Procedia PDF Downloads 103

Authors: N. Matveev, A. Pervov

Abstract:

Local utilities often face problems of local industrial wastes, storm water disposal due to existing strict regulations. For many local industries, the problem of wastewater treatment and discharge into surface reservoirs can’t be solved through the use of conventional biological treatment techniques. Current discharge standards require very strict removal of a number of impurities such as ammonia, nitrates, phosphate, etc. To reach this level of removal, expensive reagents and sorbents are used. The modern concept of rational water resources management requires the development of new efficient techniques that provide wastewater treatment and reuse. As RO membranes simultaneously reject all dissolved impurities such as BOD, TDS, ammonia, phosphates etc., they become very attractive for the direct treatment of wastewater without biological stage. To treat wastewater, specially designed membrane "open channel" modules are used that do not possess "dead areas" that cause fouling or require pretreatment. A solution to RO concentrate disposal problem is presented that consists of reducing of initial wastewater volume by 100 times. Concentrate is withdrawn from membrane unit as sludge moisture. The efficient use of membrane RO techniques is connected with a salt balance in water system. Thus, to provide high ecological efficiency of developed techniques, all components of water supply and wastewater discharge systems should be accounted for.

Keywords: reverse osmosis, stormwater treatment, open-channel module, wastewater reuse

Procedia PDF Downloads 315

Authors: Panprapa Yongtrakul, Punyaphat Sirithanabadeekul, Pakjira Siriphan

Abstract:

Background: The thread lift technique has become popular because it is less invasive, requires a shorter operation, less downtime, and results in fewer postoperative complications. The advantage of the technique is that the thread can be inserted under the skin without the need for long incisions. Currently, there are a lot of thread lift techniques with respect to the specific types of thread used on specific areas, such as the mid-face, lower face, or neck area. Objective: To review the thread lift technique for specific areas according to type of thread, patient selection, and how to match the most appropriate to the patient. Materials and Methods: A literature review technique was conducted by searching PubMed and MEDLINE, then compiled and summarized. Result: We have divided our protocols into two sections: Protocols for short suture, and protocols for long suture techniques. We also created 3D pictures for each technique to enhance understanding and application in a clinical setting. Conclusion: There are advantages and disadvantages to short suture and long suture techniques. The best outcome for each patient depends on appropriate patient selection and determining the most suitable technique for the defect and area of patient concern.

Keywords: thread lift, thread lift method, thread lift technique, thread lift procedure, threading

Procedia PDF Downloads 255