Search results for: type-2 fuzzy sets

Authors: Vivian A. Galindo, Maria C. Galvis, Jaime R. Obando, Alvaro Guarin

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In Colombia, most of the roads of the national tertiary road network are unpaved roads with granular rolling surface. These are very important ways of guaranteeing the mobility of people, products, and inputs from the agricultural sector from the most remote areas to urban centers; however, it has not paid much attention to the search for alternatives to avoid the occurrence of deteriorations that occur shortly after its commissioning. In recent years, geosynthetics have been used satisfactorily to reinforce unpaved roads on soft soils, with geotextiles and geogrids being the most widely used. The interaction of the geogrid and the aggregate minimizes the lateral movement of the aggregate particles and increases the load capacity of the material, which leads to a better distribution of the vertical stresses, consequently reducing the vertical deformations in the subgrade. Taking into account the above, the research aimed at the mechanical behavior of the granular material, used in unpaved roads with and without the presence of geogrids, from the development of laboratory tests through the loaded wheel tester (LWT). For comparison purposes, the reinforced conditions and traffic conditions to which this type of material can be accessed in practice were simulated. In total four types of geogrids, were tested with granular material; this means that five test sets, the reinforced material and the non-reinforced control sample were evaluated. The results of the numbers of load cycles and depth rutting supported by each test body showed the influence of the properties of the reinforcement on the mechanical behavior of the assembly and the significant increases in the number of load cycles of the reinforced specimens in relation to those without reinforcement.

Keywords: geosynthetics, load wheel tester LWT, tertiary roads, unpaved road, vertical deformation

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Authors: David Cook, Brynhildur Davidsdottir, Dadi. M. Kristofersson

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The approval of development projects with significant environmental impacts implies that the economic costs of the affected environmental resources must be less than the financial benefits, but such irreversible decisions are frequently made without ever attempting to estimate the monetary value of the losses. Due to this knowledge gap in the processes informing decision-making, development projects are commonly approved despite the potential for social welfare to be undermined. Heeding a repeated call by the OECD to commence economic accounting of environmental impacts as part of the cost-benefit analysis process for Icelandic energy projects, this paper sets out the results pertaining to the nation’s first two contingent valuation studies of geothermal areas likely to be developed in the near future. Interval regression using log-transformation was applied to estimate willingness to pay (WTP) for the preservation of the high-temperature Eldvörp and Hverahlíð fields. The estimated mean WTP was 8,333 and 7,122 ISK for Eldvörp and Hverahlíð respectively. Scaled up to the Icelandic population of national taxpayers, this equates to estimated total economic value of 2.10 and 1.77 billion ISK respectively. These results reinforce arguments in favour of accounting for the environmental impacts of Iceland’s future geothermal power projects as a mandatory component of the exploratory and production license application process. Further research is necessary to understand the economic impacts to specific ecosystem services associated with geothermal environments, particularly connected to changes in recreational amenity. In so doing, it would be possible to gain greater comprehension of the various components of total economic value, evolving understanding of why one geothermal area – in this case, Eldvörp – has a higher preservation value than another.

Keywords: decision-making, contingent valuation, geothermal energy, preservation

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Authors: Zhaopeng Zhu, Xianzhi Song, Gensheng Li, Shuo Zhu, Shiming Duan, Xuezhe Yao

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Accurate calculation of wellbore pressure is of great significance to prevent wellbore risk during drilling. The traditional mechanism model needs a lot of iterative solving procedures in the calculation process, which reduces the calculation efficiency and is difficult to meet the demand of dynamic control of wellbore pressure. In recent years, many scholars have introduced artificial intelligence algorithms into wellbore pressure calculation, which significantly improves the calculation efficiency and accuracy of wellbore pressure. However, due to the ‘black box’ property of intelligent algorithm, the existing intelligent calculation model of wellbore pressure is difficult to play a role outside the scope of training data and overreacts to data noise, often resulting in abnormal calculation results. In this study, the multi-phase flow mechanism is embedded into the objective function of the neural network model as a constraint condition, and an intelligent prediction model of wellbore pressure under the constraint condition is established based on more than 400,000 sets of pressure measurement while drilling (MPD) data. The constraint of the multi-phase flow mechanism makes the prediction results of the neural network model more consistent with the distribution law of wellbore pressure, which overcomes the black-box attribute of the neural network model to some extent. The main performance is that the accuracy of the independent test data set is further improved, and the abnormal calculation values basically disappear. This method is a prediction method driven by MPD data and multi-phase flow mechanism, and it is the main way to predict wellbore pressure accurately and efficiently in the future.

Keywords: multiphase flow mechanism, pressure while drilling data, wellbore pressure, mechanism constraints, combined drive

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Authors: Ajibola Isola, Shattri Mansor, Ojogbane Sani, Olugbemi Tope

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The cadastral map is the rationale behind city management planning and development. For years, cadastral maps have been produced by ground and photogrammetry platforms. Recent evolution in photogrammetry and remote sensing sensors ignites the use of Unmanned Aerial Vehicle systems (UAVs) for cadastral mapping. Despite the time-saving and multi-dimensional cost-effectiveness of the UAV platform, issues related to cadastral map accuracy are a hindrance to the wide applicability of UAVs' cadastral mapping. This study aims to present an approach leading to the generation and assessing the credibility of UAV cadastral mapping. Different sets of Red, Green, and Blue (RGB) photos were obtained from the Tarot 680-hexacopter UAV platform flown over the Universiti Putra Malaysia campus sports complex at an altitude range of 70 m, 100 m, and 250. Before flying the UAV, twenty-eight ground control points were evenly established in the study area with a real-time kinematic differential global positioning system. The second phase of the study utilizes an image-matching algorithm for photos alignment wherein camera calibration parameters and ten of the established ground control points were used for estimating the inner, relative, and absolute orientations of the photos. The resulting orthoimages are exported to ArcGIS software for digitization. Visual, tabular, and graphical assessments of the resulting cadastral maps showed a different level of accuracy. The results of the study show a gradual approach for generating UAV cadastral mapping and that the cadastral map acquired at 70 m altitude produced better results.

Keywords: aerial mapping, orthomosaic, cadastral map, flying altitude, image processing

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Authors: Stephen L. Roberts

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This article offers a critical analysis of the rise of syndromic surveillance systems for the advanced detection of pandemic threats within contemporary global health security frameworks. The article traces the iterative evolution and ascendancy of three such novel syndromic surveillance systems for the strengthening of health security initiatives over the past two decades: 1) The Program for Monitoring Emerging Diseases (ProMED-mail); 2) The Global Public Health Intelligence Network (GPHIN); and 3) HealthMap. This article demonstrates how each newly introduced syndromic surveillance system has become increasingly oriented towards the integration of digital algorithms into core surveillance capacities to continually harness and forecast upon infinitely generating sets of digital, open-source data, potentially indicative of forthcoming pandemic threats. This article argues that the increased centrality of the algorithm within these next-generation syndromic surveillance systems produces a new and distinct form of infectious disease surveillance for the governing of emergent pathogenic contingencies. Conceptually, the article also shows how the rise of this algorithmic mode of infectious disease surveillance produces divergences in the governmental rationalities of global health security, leading to the rise of an algorithmic governmentality within contemporary contexts of Big Data and these surveillance systems. Empirically, this article demonstrates how this new form of algorithmic infectious disease surveillance has been rapidly integrated into diplomatic, legal, and political frameworks to strengthen the practice of global health security – producing subtle, yet distinct shifts in the outbreak notification and reporting transparency of states, increasingly scrutinized by the algorithmic gaze of syndromic surveillance.

Keywords: algorithms, global health, pandemic, surveillance

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Authors: Kazi Enamul Hoque

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Initiatives on sustainable development are currently aggressively pursued throughout the world. The Malaysian government has developed key policies and strategies for over 30 years to achieve the nation’s policy objectives which are designed to mitigate the issues of security, energy efficiency and environmental impact to meet the rising energy demand. Malaysia’s current focus is on developing effective policies on renewable energy (RE) in order to reduce dependency on fossil fuel and contribute towards mitigating the effects of climate change. In this light mass awareness should be considered as the highest priority to protect the environment and to escape disaster due to climate change. Schools can be the reliable and effective foundation to prepare students to get familiar with environmental issues such as renewable and non-renewable energy sources. Teachers can play a vital role to create awareness among students about the advantages and disadvantages of using different renewable and nonrenewable energy resources. Thus, this study aims to investigate teachers’ knowledge, perceptions and attitudes towards renewable energy through a survey aiming a sustainable energy future. Five hundred sets of questionnaires were distributed to the school teachers in Malaysia. Total 420 questionnaires were returned of which 410 were complete to analyze. Finding shows that teachers are very familiar with the renewable energy like solar, wind and also geothermal. Most teachers were not sure about the Photovoltaics and biodiesel. Furthermore, teachers are also aware that primary energy in Malaysia is imported fossil fuels. Most teachers heard about the renewable energy in Malaysia and only few claims that they did not hear of such things and the others said that they never heard of it. The outcomes of the study will assist the energy policy makers to use teachers to create mass awareness of energy usages for future planning.

Keywords: Malaysia, non-renewable energy, renewable energy, school teacher

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Authors: Gregory Michael McMahon

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Risk management for the purposes of minimizing the damages from the operations of dams has met with opposition emerging from organisations and authorities, and their practitioners. It appears that the cause may be a misunderstanding of risk management arising from exchanges that mix deterministic thinking with risk-centric thinking and that do not separate uncertainty from reliability and accuracy from probability. This paper sets out those misunderstandings that arose from dam operations at Wivenhoe in 2011, using a comparison of outcomes that have been based on the methodology and its rules and those that have been operated by applying misunderstandings of the rules. The paper addresses the performance of one risk-centric Flood Manual for Wivenhoe Dam in achieving a risk management outcome. A mixture of engineering, administrative, and legal factors appear to have combined to reduce the outcomes from the risk approach. These are described. The findings are that a risk-centric Manual may need to assist administrations in the conduct of scenario training regimes, in responding to healthy audit reporting, and in the development of decision-support systems. The principal assistance needed from the Manual, however, is to assist engineering and the law to a good understanding of how risks are managed – do not assume that risk management is understood. The wider findings are that the critical profession for decision-making downstream of the meteorologist is not dam engineering or hydrology, or hydraulics; it is risk management. Risk management will provide the minimum flood damage outcome where actual rainfalls match or exceed forecasts of rainfalls, that therefore risk management will provide the best approach for the likely history of flooding in the life of a dam, and provisions made for worst cases may be state of the art in risk management. The principal conclusion is the need for training in both risk management as a discipline and also in the application of risk management rules to particular dam operational scenarios.

Keywords: risk management, flood control, dam operations, deterministic thinking

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Authors: S. M. Ishtiaque, V. K. Yadav, S. D. Joshi, J. K. Chatterjee

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The DIAMETRIC FAULTS system has been developed that captures a bi-directional image of yarn continuously in sequentially manner and provides the detailed classification of faults. A novel mathematical framework developed on the acquired bi-directional images forms the basis of fault classification in four broad categories, namely, Thick1, Thick2, Thin and Normal Yarn. A discretised version of Radon transformation has been used to convert the bi-directional images into one-dimensional signals. Images were divided into training and test sample sets. Karhunen–Loève Transformation (KLT) basis is computed for the signals from the images in training set for each fault class taking top six highest energy eigen vectors. The fault class of the test image is identified by taking the Euclidean distance of its signal from its projection on the KLT basis for each sample realization and fault class in the training set. Euclidean distance applied using various techniques is used for classifying an unknown fault class. An accuracy of about 90% is achieved in detecting the correct fault class using the various techniques. The four broad fault classes were further sub classified in four sub groups based on the user set boundary limits for fault length and fault volume. The fault cross-sectional area and the fault length defines the total volume of fault. A distinct distribution of faults is found in terms of their volume and physical dimensions which can be used for monitoring the yarn faults. It has been shown from the configurational based characterization and classification that the spun yarn faults arising out of mass variation, exhibit distinct characteristics in terms of their contours, sizes and shapes apart from their frequency of occurrences.

Keywords: Euclidean distance, fault classification, KLT, Radon Transform

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Authors: Navneet Singh Deora, Aastha Deswal, H. N. Mishra

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Pasta is one of the most widely consumed food products around the world. Rapid determination of the moisture content in pasta will assist food processors to provide online quality control of pasta during large scale production. Rapid Fourier transform near-infrared method (FT-NIR) was developed for determining moisture content in pasta. A calibration set of 150 samples, a validation set of 30 samples and a prediction set of 25 samples of pasta were used. The diffuse reflection spectra of different types of pastas were measured by FT-NIR analyzer in the 4,000-12,000 cm-1 spectral range. Calibration and validation sets were designed for the conception and evaluation of the method adequacy in the range of moisture content 10 to 15 percent (w.b) of the pasta. The prediction models based on partial least squares (PLS) regression, were developed in the near-infrared. Conventional criteria such as the R2, the root mean square errors of cross validation (RMSECV), root mean square errors of estimation (RMSEE) as well as the number of PLS factors were considered for the selection of three pre-processing (vector normalization, minimum-maximum normalization and multiplicative scatter correction) methods. Spectra of pasta sample were treated with different mathematic pre-treatments before being used to build models between the spectral information and moisture content. The moisture content in pasta predicted by FT-NIR methods had very good correlation with their values determined via traditional methods (R2 = 0.983), which clearly indicated that FT-NIR methods could be used as an effective tool for rapid determination of moisture content in pasta. The best calibration model was developed with min-max normalization (MMN) spectral pre-processing (R2 = 0.9775). The MMN pre-processing method was found most suitable and the maximum coefficient of determination (R2) value of 0.9875 was obtained for the calibration model developed.

Keywords: FT-NIR, pasta, moisture determination, food engineering

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Authors: Herke Csongor

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The artificial intelligence (AI) has been used in the United States of America in the decisionmaking process of the criminal justice system for decades. In the field of law, including criminal law, AI can provide serious assistance in decision-making in many places. The paper reviews four main areas where AI still plays a role in the criminal justice system and where it is expected to play an increasingly important role. The first area is the predictive policing: a number of algorithms are used to prevent the commission of crimes (by predicting potential crime locations or perpetrators). This may include the so-called linking hot-spot analysis, crime linking and the predictive coding. The second area is the Big Data analysis: huge amounts of data sets are already opaque to human activity and therefore unprocessable. Law is one of the largest producers of digital documents (because not only decisions, but nowadays the entire document material is available digitally), and this volume can only and exclusively be handled with the help of computer programs, which the development of AI systems can have an increasing impact on. The third area is the criminal statistical data analysis. The collection of statistical data using traditional methods required enormous human resources. The AI is a huge step forward in that it can analyze the database itself, based on the requested aspects, a collection according to any aspect can be available in a few seconds, and the AI itself can analyze the database and indicate if it finds an important connection either from the point of view of crime prevention or crime detection. Finally, the use of AI during decision-making in both investigative and judicial fields is analyzed in detail. While some are skeptical about the future role of AI in decision-making, many believe that the question is not whether AI will participate in decision-making, but only when and to what extent it will transform the current decision-making system.

Keywords: artificial intelligence, international criminal cooperation, planning and organizing of the investigation, risk assessment

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Authors: Kolawole Oyekan Jamiu

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In Nigerian criminal law, a victim of an offence plays little or no role in the prosecution of an offender. The state concentrates only on imposing punishment on the offender while the victims of crime and abuse of power by security agencies are abandoned without any compensation either from the State or the offender. It has been stated that the victim of crime is the forgotten man in our criminal justice system. He sets the criminal law in motion but then goes into oblivion. Our present criminal law does not recognise the right of the victim to take part in the prosecution of the case or his right to compensation. The victim is merely a witness in a state versus case. This paper examines the meaning of the phrase ‘the victims of crime and abuse of power’. It needs to be noted that there is no definition of these two categories of victims in any statute in Nigeria. The paper also considers the United Nations General Assembly Declaration of Basic Principle of Justice for Victims and abuse of power. This declaration was adopted by the United Nations General Assembly on the 25th of November 1985. The declaration contains copious provisions on compensation for the victims of crime and abuse of power. Unfortunately, the declaration is not, in itself a legally binding instrument and has been given little or no attention since the coming into effect in1985. This paper examines the role of the judiciary in ensuring that victims of crime and abuse of power in Nigeria are compensated. While some Judges found it difficult to award damages to victims of abuse of power others have given some landmark rulings and awarded substantial damages. The criminal justice ( victim’s remedies) Bill shall also be examined. The Bill comprises of 74 sections and it spelt out the procedures for compensating the victims of crime and abuse of power in Nigeria. Finally, the paper also examines the practicability of awarding damages to victims of crime whether the offender is convicted or not and in addition, the possibility of granting all equitable remedies available in civil cases to victims of crime and abuse of power so that the victims will be restored to the earlier position before the crime.

Keywords: compensation, damages, restitution, victims

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Authors: Shashank Gupta, Iteash Agarwal, Vijayalaxmi Mogiligidda, Rajesh Kumar Krishnan, Sruthi Chennuri, Deepika Aggarwal, Anwesha Hoodati, Sheroy Cooper, Ranjan, Mohammad Bilal Sheik, Bhavya K. M., Manasa Hegde, M. Naveen Krishna, Amit Kumar Chauhan, Mallikarjun Korrapati, Sumit Singh, J. B. Singh, Sunil Sud, Sunil Gupta, Sidhartha Pant, Sankar, Neha Agrawal, Ashish Ranjan, Piyush Mohapatra, Roopak T., Arsh Ahmad, Nanjunda M., Dilip Singh

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Major research interests on quantum key distribution (QKD) are primarily focussed on increasing 1. point-to-point transmission distance (1000 Km), 2. secure key rate (Mbps), 3. security of quantum layer (device-independence). It is great to push the boundaries on these fronts, but these isolated approaches are neither scalable nor cost-effective due to the requirements of specialised hardware and different infrastructure. Current and future QKD network requires addressing different sets of challenges apart from distance, key rate, and quantum security. In this regard, we present ChaQra -a sub-quantum network with core features as 1) Crypto agility (integration in the already deployed telecommunication fibres), 2) Software defined networking (SDN paradigm for routing different nodes), 3) reliability (addressing denial-of-service with hybrid quantum safe cryptography), 4) upgradability (modules upgradation based on scientific and technological advancements), 5) Beyond QKD (using QKD network for distributed computing, multi-party computation etc). Our results demonstrate a clear path to create and accelerate quantum secure Indian subcontinent under the national quantum mission.

Keywords: quantum network, quantum key distribution, quantum security, quantum information

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Authors: Marco Sewald

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WWSE School Development (Wahrnehmungs- und wertorientierte Schulentwicklung) contains surveys on pupils, teachers and parents and enables schools to align the development to the requirements mentioned by these three stakeholders. WWSE includes a derivative set of questions for Christian schools, meeting their specific needs. The conducted research on WWSE is reflecting contemporary questions on school development, questioning the quality of the implementation of the results of past surveys, delivered by WWSE School Development in Christian schools in Germany. The research focused on questions connected to organizational development, including leadership and change management. This is done contoured to the two other areas of WWSE: human resources development and development of school teaching methods. The chosen research methods are: (1) A quantitative triangulation on three sets of data. Data from a past evaluation taken in 2011, data from a second evaluation covering the same school conducted in 2014 and a structured survey among the teachers, headmasters and members of the school board taken within the research. (2) Interviews with teachers and headmasters have been conducted during the research as a second stage to fortify the result of the quantitative first stage. Results: WWSE is supporting modern school development. While organizational development, leadership, and change management are proofed to be important for modern school development, these areas are widespread underestimated by teachers and headmasters. Especially in comparison to the field of human resource development and to an even bigger extent in comparison to the area of development of school teaching methods. The research concluded, that additional efforts in the area of organizational development are necessary to meet modern demands and the research also shows which areas are the most important ones.

Keywords: school as a social organization, school development, school leadership, WWSE, Wahrnehmungs- und wertorientierte Schulentwicklung

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Authors: Ayan Handulle, Memory J. Tembo-Pankuku

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The social work profession utilizes Western and Eurocentric perspectives on social structures, culture, history, belief systems, and education. This affects social work practice with indigenous groups as well as other minorities who have different perspectives. Some of the challenges that characterize social work with families, especially immigrants in western countries, are a result of different world views on child-rearing practices in the global north and the global south. A shift towards cultural sensitivity and the promotion of cultural competence has been a move towards addressing some of the challenges in child welfare practice with immigrants. However, emphasis on cultural differences presents other challenges of stereotyping and discrimination, which call for the examination of current practices to fit other groups of people. In this paper, we introduce the need for emancipatory social work in child welfare practice with immigrant parents. Emancipatory social work is directed at heightening awareness of external sources of oppression and/or privilege that hold the possibility of increasing self-esteem and courage to confront structural sources of marginalization, oppression, and exclusion. This paper draws on two research projects, respectively, “Immigrant parents’ perceptions and experiences of the welfare system” and “Norwegian- Somali parents’ fears of the Norwegian Child welfare service. The first data set comprises 15 in-depth interviews with 18 nonWestern immigrant parents, representing 10 families. The second data set consists of nine months of ethnography, seven months in Oslo, and two months in Somalia among returnees from Norway. Based on these data sets, we explore how immigrant parents’ child-rearing practices might be perceived through a racialized lens.

Keywords: child welfare, immigrants, racialization, social work

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Authors: Numair Ahmed Siddiqui, Abdul Hadi bin Abd Rahman, Chow Weng Sum, Wan Ismail Wan Yousif, Asif Zameer, Joel Ben-Awal

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Advances in data capturing from outcrop studies have made possible the acquisition of high-resolution digital data, offering improved and economical reservoir modelling methods. Terrestrial laser scanning utilizing LiDAR (Light detection and ranging) provides a new method to build outcrop based reservoir models, which provide a crucial piece of information to understand heterogeneities in sandstone facies with high-resolution images and data set. This study presents the detailed application of outcrop based sandstone facies connectivity model by acquiring information gathered from traditional fieldwork and processing detailed digital point-cloud data from LiDAR to develop an intermediate small-scale reservoir sandstone facies model of the Miocene Sandakan Formation, Sabah, East Malaysia. The software RiScan pro (v1.8.0) was used in digital data collection and post-processing with an accuracy of 0.01 m and point acquisition rate of up to 10,000 points per second. We provide an accurate and descriptive workflow to triangulate point-clouds of different sets of sandstone facies with well-marked top and bottom boundaries in conjunction with field sedimentology. This will provide highly accurate qualitative sandstone facies connectivity model which is a challenge to obtain from subsurface datasets (i.e., seismic and well data). Finally, by applying this workflow, we can build an outcrop based static connectivity model, which can be an analogue to subsurface reservoir studies.

Keywords: LiDAR, outcrop, high resolution, sandstone faceis, connectivity model

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Authors: Pratik Acharya

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Scour is the natural phenomenon brought about by erosive action of the flowing stream in alluvial channels. Frequent scouring around bridge piers may cause damage to the structures. In alluvial channels, a complex interaction between the streamflow and the bed particles results in scouring around piers. Thus, the study of characteristics of flow around piers can give sound knowledge about the scouring process. The present research has been done to investigate the turbulent flow characteristics around bridge piers and corresponding changes in bed morphology. Laboratory experiments were carried out in a tilting flume with a sand bed. The velocities around the pier are measured by Acoustic Doppler Velocimeter. Measurements show that at upstream of the pier velocity and Reynolds stresses are negative near the bed and near the free surface at downstream of the pier. At the downstream of the pier, Reynolds stresses changes rapidly due to the formation of wake vortices. Experimental results show that secondary currents are more predominant at the downstream of the pier. As the flowing stream hits the pier, the flow gets separated in the form of downflow along the face of the pier due to a strong pressure gradient and along the sides of the piers. Separation of flow around the pier leads to scour the bed material and develop the vortex. The downflow hits the bed and removes the bed material, which can be carried forward by the flow circulations along sides of the piers. Eroded bed material is deposited along the centerline at the rear side of the pier and produces hump in the downstream region. Initially, the rate of scouring is high and reduces gradually with increasing time. After a certain limit, equilibrium sets between the erosive capacity of the flowing stream and resistance to the motion by bed particles.

Keywords: acoustic doppler velocimeter, pier, Reynolds stress, scour depth, velocity

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Authors: B. Vijay Antony Raj, Umarani Gunasekaran, R. Thiru Kumara Raja Vallaban

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Fibrous autoclaved aerated concrete (FAAC) is concrete containing fibrous material in it which helps to increase its structural integrity when compared to that of convention autoclaved aerated concrete (CAAC). These short discrete fibers are uniformly distributed and randomly oriented, which enhances the bond strength within the aerated concrete matrix. Conventional red-clay bricks create larger impact to the environment due to red soil depletion and it also consumes large amount to time for construction. Whereas, AAC are larger in size, lighter in weight and it is environmentally friendly in nature and hence it is a viable replacement for red-clay bricks. Internal micro cracks and corner cracks are the only disadvantages of conventional autoclaved aerated concrete, to resolve this particular issue it is preferable to make use of fibers in it.These fibers are bonded together within the matrix and they induce the aerated concrete to withstand considerable stresses, especially during the post cracking stage. Hence, FAAC has the capability of enhancing the mechanical properties and energy absorption capacity of CAAC. In this research work, individual fibers like glass, nylon, polyester and polypropylene are used they generally reduce the brittle fracture of AAC.To study the fibre’s surface topography and composition, SEM analysis is performed and then to determine the composition of a specimen as a whole as well as the composition of individual components EDAX mapping is carried out and then an experimental approach was performed to determine the effect of hybrid (multiple) fibres at various dosage (0.5%, 1%, 1.5%) and curing temperature of 180-2000 C is maintained to determine the mechanical properties of autoclaved aerated concrete. As an analytical part, the outcome experimental results is compared with fuzzy logic using MATLAB.

Keywords: fiberous AAC, crack control, energy absorption, mechanical properies, SEM, EDAX, MATLAB

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Authors: Padmini S. Navalgund, Manasi Naik, Ujwala Patil

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Road hump is a semi-cylindrical elevation on the road made across specific locations of the road. The vehicle needs to maneuver the hump by reducing the speed to avoid car damage and pass over the road hump safely. Road Humps on road surfaces, if identified in advance, help to maintain the security and stability of vehicles, especially in adverse visibility conditions, viz. night scenarios. We have proposed a deep learning architecture adaptation by implementing the MISH activation function and developing a new classification loss function called "Effective Focal Loss" for Indian road humps detection in adverse light scenarios. We captured images comprising of marked and unmarked road humps from two different types of cameras across South India to build a heterogeneous dataset. A heterogeneous dataset enabled the algorithm to train and improve the accuracy of detection. The images were pre-processed, annotated for two classes viz, marked hump and unmarked hump. The dataset from these images was used to train the single-stage object detection algorithm. We utilised an algorithm to synthetically generate reduced visible road humps scenarios. We observed that our proposed framework effectively detected the marked and unmarked hump in the images in clear and ad-verse light environments. This architectural adaptation sets up an option for early detection of Indian road humps in reduced visibility conditions, thereby enhancing the autonomous driving technology to handle a wider range of real-world scenarios.

Keywords: Indian road hump, reduced visibility condition, low light condition, adverse light condition, marked hump, unmarked hump, YOLOv9

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Authors: Yolanda Havenga

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Being a competent nurse requires clinical, general, and moral competencies. Moral competence is a culmination of moral perceptions, moral judgment, moral behaviour, and moral identity. Moral identity is the values, images, and fundamental principles held in the collective minds and memories of nurses about what it means to be a ‘good nurse’. It is important to explore and describe South African nurses’ moral identities and excavate the post-colonial counter-narrative to nurses moral identities as a better understanding of these identities will enable means to positively address nurses’ moral behaviours. This study explored the moral identity of nurses within the South African context. A qualitative approach was followed triangulating with phenomenological and narrative designs with the same purposively sampled group of professional nurses. In-depth interviews were conducted until saturation of data occurred about the sampled nurses lived experiences of being a nurse in South Africa. They were probed about their core personal-, social-, and professional values. Data were analysed based on the steps used by Colaizzi. These nurses were then asked to write a narrative telling a personal story that portrayed a significant time in their professional career that defines their identity as a nurse. This data were analysed using a critical narrative approach and findings of the two sets of data were merged. Ethical approval was obtained and approval from all relevant gate keepers. In the findings, themes emerged related to personal, social and professional values, images and fundamental principles of being a nurse within the South African context. The findings of this study will inform a future national study including a representative sample of South African nurses.

Keywords: moral behaviour, moral identity, nurses, qualitative research

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Authors: Jingwen Shi, Qi Wu

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We study the role played interest rate risk in carry trade return in order to understand the forward premium puzzle. In this study, our goal is to investigate to what extent carry trade return is indeed due to compensation for risk taking and, more important, to reveal the nature of these risks. Using option data not only on exchange rates but also on interest rate swaps (swaptions), our first finding is that, besides the consensus currency risks, interest rate risks also contribute a non-negligible portion to the carry trade return. What strikes us is our second finding. We find that large downside risks of future exchange rate movements are, in fact, priced significantly in option market on interest rates. The role played by interest rate risk differs structurally from the currency risk. There is a unique premium associated with interest rate risk, though seemingly small in size, which compensates the tail risks, the left tail to be precise. On the technical front, our study relies on accurately retrieving implied distributions from currency options and interest rate swaptions simultaneously, especially the tail components of the two. For this purpose, our major modeling work is to build a new international asset pricing model where we use an orthogonal setup for pricing kernels and specify non-Gaussian dynamics in order to capture three sets of option skew accurately and consistently across currency options and interest rate swaptions, domestic and foreign, within one model. Our results open a door for studying forward premium anomaly through implied information from interest rate derivative market.

Keywords: carry trade, forward premium anomaly, FX option, interest rate swaption, implied volatility skew, uncovered interest rate parity

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Authors: Chen Jun

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Antonia Susan Byatt’s Possession: A Romance has been misread as a postmodern pastiche novel since its publication because there are epics, epigraphs, lyrics, fairy tales, epistles, and even critical articles swollen in this work. The word ‘pastiche’ suggests messy, disorganized, and chaotic, which buries its artistic excellence while overlooking its subtitle, A Romance. The center of romance is the quest that the hero sets forth to conquer the adversity, hardship, and danger to accomplish a task to prove his identity or social worth. This paper argues that Byatt’s Possession is not a postmodern pastiche novel but rather a postmodern romance in which the characters in the academic world set forth their quest into the Victorian literary past that is nostalgically identified by Byatt as the Golden Age of English literature. In doing so, these five following issues are addressed: first, the origin of the protagonist Roland, and consequently, the nature of his quest; second, the central image of the dragon created by the fictional Victorian poet Henry Ash; third, Melusine as an image of female serpent created by the fictional Victorian poet Christabel LaMotte; fourth, the images of the two ladies; last, the image of water that links the dragon and the serpent. In Possession, the past is reinvented not as an unfortunate fall but as a Golden Age presented in the imaginative academic adventure. The dragon, a stereotypical symbol of evil, becomes the symbol of life in Byatt’s work, which parallels with the image of the mythical phoenix that can resurrect from its own ash. At the same time, the phoenix symbolizes Byatt’s efforts to revive the Victorian poetic art that is supposed to be dead in the post-capitalism society when the novel is the dominating literary genre and poetry becomes the minority. The fictional Victorian poet Ash is in fact Byatt’s own poetic mask through which she breathes life into the lost poetic artistry in the postmodern era.

Keywords: Byatt, possession, postmodern romance, literary past

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Authors: Farah Fazlinda Binti Mohamad, Siti Hikmah Binti Musthar, Ahmad Saifizul Bin Abdullah, Jamilah Mohamad, Mohamed Rehan Karim

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Road safety is intolerable issue. It affects and impinges on everyone's life as the roads shared by everyone. The most vulnerable victims were the road users who cater the roads every day. It is an appalling when World Health Organization reported that Malaysian road users were ranked worst in Asian countries with 23 deaths for every 100,000 of population over the span of 12 years (World Health Organization, 2009). From this report, it is found that speeding has contributed to 60% of all accidents in the country. Therefore, this study aims to elucidate on speeding matter that occur among road users in selected roads of Peninsular Malaysia. This study on the other hand, provides an insight understanding on the factors affecting behaviour of road users to speeding in selected roads of Peninsular Malaysia. To answer the study aims, 500 sets of questionnaires were distributed among 500 respondents in selected roads of Peninsular Malaysia to obtain their opinions on the matter. The respondents were from different demographics backgrounds to have fair explanation on the issue. The answers have been analysed using descriptive analysis. The results indicated psychological factors of road users appeared to be prominent in explaining road users’ behaviour to speeding. Male road users were also found dominant in speeding compared to female. Thus, this has increased their vulnerability to road injuries and deaths. These findings are very useful in order for us to improve our driving behaviour. Relevant authorities should also revise the existing countermeasures as well as designing the new countermeasures for the road users. It is nevertheless important to comprehend this speeding issue and factors associating it. This matter should be taken seriously and responsibly by each road users as road safety is a responsible of all.

Keywords: road safety, speeding, countermeasures, accidents

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Authors: David C. Ni

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Contemporary models for N-body systems are based on temporal, two-body, and mass point representation of Newtonian mechanics. Other mainstream models include 2D and 3D Ising models based on local neighborhood the lattice structures. In Quantum mechanics, the theories of collective modes are for superconductivity and for the long-range quantum entanglement. However, these models are still mainly for the specific phenomena with a set of designated parameters. We are therefore motivated to develop a new construction directly from the complex-variable N-body systems based on the extended Blaschke functions (EBF), which represent a non-temporal and nonlinear extension of Lorentz transformation on the complex plane – the normalized momentum spaces. A point on the complex plane represents a normalized state of particle momentums observed from a reference frame in the theory of special relativity. There are only two key parameters, normalized momentum and nonlinearity for modelling. An algorithm similar to Jenkins-Traub method is adopted for solving EBF iteratively. Through iteration, the solution sets show a form of σ + i [-t, t], where σ and t are the real numbers, and the [-t, t] shows various distributions, such as 1-peak, 2-peak, and 3-peak etc. distributions and some of them are analog to the canonical distributions. The results of the numerical analysis demonstrate continuum-to-discreteness transitions, evolutional invariance of distributions, phase transitions with conjugate symmetry, etc., which manifest the construction as a potential candidate for the unification of statistics. We hereby classify the observed distributions on the finite convergent domains. Continuous and discrete distributions both exist and are predictable for given partitions in different regions of parameter-pair. We further compare these distributions with canonical distributions and address the impacts on the existing applications.

Keywords: blaschke, lorentz transformation, complex variables, continuous, discrete, canonical, classification

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Authors: Zouhair Haddi, Stephane Delliaux, Jean-Francois Pons, Ismail Kechaf, Jean-Claude De Haro, Mustapha Ouladsine

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Atrial fibrillation (AF) has been considered as the most common cardiac arrhythmia, and a major public health burden associated with significant morbidity and mortality. Nowadays, telemedical approaches targeting cardiac outpatients situate AF among the most challenged medical issues. The automatic, early, and fast AF detection is still a major concern for the healthcare professional. Several algorithms based on univariate analysis have been developed to detect atrial fibrillation. However, the published results do not show satisfactory classification accuracy. This work was aimed at resolving this shortcoming by proposing multivariate data analysis methods for automatic AF detection. Four publicly-accessible sets of clinical data (AF Termination Challenge Database, MIT-BIH AF, Normal Sinus Rhythm RR Interval Database, and MIT-BIH Normal Sinus Rhythm Databases) were used for assessment. All time series were segmented in 1 min RR intervals window and then four specific features were calculated. Two pattern recognition methods, i.e., Principal Component Analysis (PCA) and Learning Vector Quantization (LVQ) neural network were used to develop classification models. PCA, as a feature reduction method, was employed to find important features to discriminate between AF and Normal Sinus Rhythm. Despite its very simple structure, the results show that the LVQ model performs better on the analyzed databases than do existing algorithms, with high sensitivity and specificity (99.19% and 99.39%, respectively). The proposed AF detection holds several interesting properties, and can be implemented with just a few arithmetical operations which make it a suitable choice for telecare applications.

Keywords: atrial fibrillation, multivariate data analysis, automatic detection, telemedicine

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Authors: Sima Majnooni

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This paper discusses the Green Climate Fund's environmental and social management framework (GCF). The environmental and social management framework ensures the accredited entity considers the GCF's accreditation standards and effectively implements each of the GCF-funded projects. The GCF requires all accredited entities to meet basic transparency and accountability standards as well as environmental and social safeguards (ESMS). In doing so, the accredited entity sets up different independent units. One of these units is called the Grievance Mechanism. When allegations of environmental and social harms are raised in association with GCF-funded activities, affected parties can contact the entity’s grievance unit. One of the most challenging things about the accredited entity's grievance unit is the lack of available information and resources on the entities' websites. Many AEs have anti-corruption or anti-money laundering unit, but they do not have the environmental and social unit for affected people. This paper will argue the effectiveness of environmental and social grievance mechanisms of AEs by using a qualitative approach to indicate how many of AEs have a poor or an effective GRM. Some ESMSs seem highly effective. On the other hand, other mechanisms lack basic requirements such as a clear, transparent, uniform procedure and a definitive timetable. We have looked at each AE mechanism not only in light of how the website goes into detail regarding the process of grievance mechanism but also in light of their risk category. Many mechanisms appear inadequate for the lower level risk category entities (C) and, even surprisingly, for many higher-risk categories (A). We found; in most cases, the grievance mechanism of AEs seems vague.

Keywords: grievance mechanism, vague environmental and social policies, green climate fund, international climate finance, lower and higher risk category

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Authors: Serges Mendomo Meye, Li Guowei, Shen Zhenzhong

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Earthquake is one of the main loads threatening dam safety. Once the dam is damaged, it will bring huge losses of life and property to the country and people. Therefore, it is very important to research the seismic safety of the dam. Due to the complex foundation conditions, high fortification intensity, and high scientific and technological content, it is necessary to adopt reasonable methods to evaluate the seismic safety performance of concrete arch dams built and under construction in strong earthquake areas. Structural seismic vulnerability analysis can predict the probability of structural failure at all levels under different intensity earthquakes, which can provide a scientific basis for reasonable seismic safety evaluation and decision-making. In this paper, the response surface method (RSM) is applied to the seismic vulnerability analysis of arch dams, which improves the efficiency of vulnerability analysis. Based on the central composite test design method, the material-seismic intensity samples are established. The response surface model (RSM) with arch crown displacement as performance index is obtained by finite element (FE) calculation of the samples, and then the accuracy of the response surface model (RSM) is verified. To obtain the seismic vulnerability curves, the seismic intensity measure ??(?1) is chosen to be 0.1~1.2g, with an interval of 0.1g and a total of 12 intensity levels. For each seismic intensity level, the arch crown displacement corresponding to 100 sets of different material samples can be calculated by algebraic operation of the response surface model (RSM), which avoids 1200 times of nonlinear dynamic calculation of arch dam; thus, the efficiency of vulnerability analysis is improved greatly.

Keywords: high concrete arch dam, performance index, response surface method, seismic vulnerability analysis, vector-valued intensity measure

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Authors: Bryce R. Jolley, Michael Uchic

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This study presents initial results toward the correlative characterization of inherent defects of Ti-6Al-4V additive manufacture (AM). X-Ray Computed Tomography (CT) defect data are compared and correlated with microscopic photographs obtained via automated serial sectioning. The metal AM specimen was manufactured out of Ti-6Al-4V virgin powder to specified dimensions. A post-contour was applied during the fabrication process with a speed of 1050 mm/s, power of 260 W, and a width of 140 µm. The specimen was stress relief heat-treated at 16°F for 3 hours. Microfocus CT imaging was accomplished on the specimen within a predetermined region of the build. Microfocus CT imaging was conducted with parameters optimized for Ti-6Al-4V additive manufacture. After CT imaging, a modified RoboMet. 3D version 2 was employed for serial sectioning and optical microscopy characterization of the same predetermined region. Automated montage capture with sub-micron resolution, bright-field reflection, 12-bit monochrome optical images were performed in an automated fashion. These optical images were post-processed to produce 2D and 3D data sets. This processing included thresholding and segmentation to improve visualization of defect features. The defects observed from optical imaging were compared and correlated with the defects observed from CT imaging over the same predetermined region of the specimen. Quantitative results of area fraction and equivalent pore diameters obtained via each method are presented for this correlation. It is shown that Microfocus CT imaging does not capture all inherent defects within this Ti-6Al-4V AM sample. Best practices for this correlative effort are also presented as well as the future direction of research resultant from this current study.

Keywords: additive manufacture, automated serial sectioning, computed tomography, nondestructive evaluation

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Authors: Chao-Ton Su, Li-Fei Chen

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The parameter design is crucial to improving the uniformity of a product or process. In the product design stage, parameter design aims to determine the optimal settings for the parameters of each element in the system, thereby minimizing the functional deviations of the product. In the process design stage, parameter design aims to determine the operating settings of the manufacturing processes so that non-uniformity in manufacturing processes can be minimized. The parameter design, trying to minimize the influence of noise on the manufacturing system, plays an important role in the high-tech companies. Taiwan has many well-known high-tech companies, which show key roles in the global economy. Quality remains the most important factor that enables these companies to sustain their competitive advantage. In Taiwan however, many high-tech companies face various quality problems. A common challenge is related to root causes and defect patterns. In the R&D stage, root causes are often unknown, and defect patterns are difficult to classify. Additionally, data collection is not easy. Even when high-volume data can be collected, data interpretation is difficult. To overcome these challenges, high-tech companies in Taiwan use more advanced quality improvement tools. In addition to traditional statistical methods and quality tools, the new trend is the application of powerful tools, such as neural network, fuzzy theory, data mining, industrial engineering, operations research, and innovation skills. In this study, several examples of optimizing the parameter settings for the manufacturing process in Taiwan’s tech companies will be presented to illustrate proposed approach’s effectiveness. Finally, a discussion of using traditional experimental design versus the proposed approach for process optimization will be made.

Keywords: quality engineering, parameter design, neural network, genetic algorithm, experimental design

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Authors: Pia Moschall, Kathrin Sackmann

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Considering a purchasing volume of around two trillion Euros per year, which equals about 14% of the European Union’s gross domestic product, European public authorities have significant market power. Making use of this market power by prioritizing the procurement of green products and services offers a great potential to contribute to the Green New Deal. The market demand that is created by Green Public Procurement (GPP) sets incentives for European producers to design and develop Green Products and Eco-Innovations. However, most procurement still does not consider environmental criteria. The goal of the work is to encourage the adaptation of GPP in the European Union. To this end, the drivers for the adaptation were investigated over different case studies. The paper analyzes good-practice cases from European authorities from 2010 to 2020 that were provided by the European Commission. This analysis was guided by Philipp Mayring’s method of qualitative content analysis, whereby the inductively formed categories led to the identification of nine major drivers. The most important ones are ‘use of official guidelines and standards, ‘political support and requirements as well as ‘market research and involvement.’ Further, the paper discusses mutual dependencies between several drivers and how to exploit them. A supporting infrastructure was identified as a crucial factor for the successful adaption of green public procurement. In the next step, the work aims to examine on which administrative level the single drivers can be implemented most effectively. Practical implications of this research are recommendations on how to create a supporting structure on a municipal, federal and national level, including training for the responsible staff, support tools, as well as guidelines and standards for involved stakeholders.

Keywords: content analysis, green public procurement, public authorities, sustainable procurement

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Authors: Pius Marthin, Nihal Ata Tutkun

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Survival analysis has become one of the paramount procedures in the modeling of time-to-event data. When we encounter complex survival problems, the traditional approach remains limited in accounting for the complex correlational structure between the covariates and the outcome due to the strong assumptions that limit the inference and prediction ability of the resulting models. Several studies exist on the deep learning approach to survival modeling; moreover, the application for the case of complex survival problems still needs to be improved. In addition, the existing models need to address the data structure's complexity fully and are subject to noise and redundant information. In this study, we design a deep learning technique (CmpXRnnSurv_AE) that obliterates the limitations imposed by traditional approaches and addresses the above issues to jointly predict the risk-specific probabilities and survival function for recurrent events with competing risks. We introduce the component termed Risks Information Weights (RIW) as an attention mechanism to compute the weighted cumulative incidence function (WCIF) and an external auto-encoder (ExternalAE) as a feature selector to extract complex characteristics among the set of covariates responsible for the cause-specific events. We train our model using synthetic and real data sets and employ the appropriate metrics for complex survival models for evaluation. As benchmarks, we selected both traditional and machine learning models and our model demonstrates better performance across all datasets.

Keywords: cumulative incidence function (CIF), risk information weight (RIW), autoencoders (AE), survival analysis, recurrent events with competing risks, recurrent neural networks (RNN), long short-term memory (LSTM), self-attention, multilayers perceptrons (MLPs)

Procedia PDF Downloads 91