Search results for: Financial decision making

Authors: Mingook Lee, Sungjoo Lee

Abstract:

As technology-based service industries grow drastically worldwide; companies are recognizing the importance of market preoccupancy and have made an effort to capture a large market to gain the upper hand. To this end, a focus on patents can be used to determine the properties of a technology, as well as to capture advantages in technical skills, in comparison with the firm’s competitors. However, technology-based services largely depend not only on their technological value but also their economic value, due to the recognized worth that is passed to a plurality of users. Thus, it is important to determine whether there are any competitors in the target areas and what services they provide in any field. Despite this importance, little effort has been made to systematically benchmark competitors in order to identify business opportunities. Thus, this study aims to not only identify each position of technology-centered service companies in complex market dynamics, but also to discover new business opportunities. For this, we try to consider both technology and market environments simultaneously by utilizing patent data as a representative proxy for technology and trademark dates as an index for a firm’s target goods and services. Theoretically, this is one of the earliest attempts to combine patent data and trademark data to analyze corporate strategies. In practice, the research results are expected to be used as a decision criterion to diagnose the economic value that companies can obtain by entering the market, as well as the technological value to be passed onto their customers. Thus, the proposed approach can be useful to support effective technology and business strategies in a firm.

Keywords: Business opportunity, patent, Portfolio analysis, trademark.

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Authors: Robin Becker, Maike Eilers, Nane Roetmann, Manfred Helmus

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The shortage of junior staff in the construction industry is a problem that will be further exacerbated in the coming years by the retirement of the baby-boom generations (1955-1969) from employment. In addition, the current working conditions in the field of construction management are not attractive for young professionals. A survey of students as part of the paper revealed a desire for an increase in flexibility and an improved work-life balance in everyday working life. Students of civil engineering and architecture are basically interested in a career in construction management but have reservations due to the image of the profession and the current working conditions. A survey among experts from the construction industry, also as a part of the paper, shows that the profession can become more attractive. This report provides recommendations for action in the form of working modules to improve the working conditions of employees. If these are taken into account, graduates can be attracted to the profession of construction management, and existing staff can be retained more effectively. The aim of this report is to show incentives for employers to respond to the wishes and needs of their current and future employees to the extent that can be implemented.

Keywords: Modern construction management, construction industry, work modules, shortage of junior staff, sustainable personnel management, making construction management more attractive, working time model.

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Authors: Cathy T. Mpanga Kowet, Aghaegbuna Obinna U. Ozumba

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This study examined and identified the inconsistencies, relationships, gaps and recurring themes in literature regarding the relationship between procurement strategies employed in the construction projects for sustainable buildings and realization of sustainability goals. A systematic literature review of studies on the relationship between various procurement strategies and attainment of sustainability outcomes was conducted. Using specific terms, papers published between 2002 and 2018 were identified and screened according to an inclusion and exclusion criteria. Current findings reveal that, although the attainment of sustainability goals is achievable with both traditional and contemporary procurement strategies, only projects delivered using modern procurement strategies are capable of meeting and exceeding targeted sustainability objectives. However, traditional procurement strategy remains the preferred method for most green building construction projects. The results suggest implications for decision makers in considering the impact of selected procurement strategies on targeted sustainability goals, in the early stages of sustainable building construction projects. The study shows that there is a gap between the reported appropriate procurement strategies and what is being practiced currently. Theoretically, the study expands on the literature on adoption and diffusion of contemporary procurement strategies, by consolidating existing studies to highlight the current gaps. While the study is at the literature review stage, deductions will serve as basis for field work involving empirical data.

Keywords: Green building, green construction, procurement method, procurement strategy, sustainability objectives, sustainability outcomes.

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Authors: Adeniyi G. Adeogun, Bolaji F. Sule, Adebayo W. Salami, Michael O. Daramola

Abstract:

Estimation of water yield and water balance in a river catchment is critical to the sustainable management of water resources at watershed level in any country. Therefore, in the present study, Soil and Water Assessment Tool (SWAT) interfaced with Geographical Information System (GIS) was applied as a tool to predict water balance and water yield of a catchment area in Nigeria. The catchment area, which was 12,992km², is located upstream Jebba hydropower dam in North central part of Nigeria. In this study, data on the observed flow were collected and compared with simulated flow using SWAT. The correlation between the two data sets was evaluated using statistical measures, such as, Nasch-Sucliffe Efficiency (NSE) and coefficient of determination (R²). The model output shows a good agreement between the observed flow and simulated flow as indicated by NSE and R², which were greater than 0.7 for both calibration and validation period. A total of 42,733 mm of water was predicted by the calibrated model as the water yield potential of the basin for a simulation period between 1985 to 2010. This interesting performance obtained with SWAT model suggests that SWAT model could be a promising tool to predict water balance and water yield in sustainable management of water resources. In addition, SWAT could be applied to other water resources in other basins in Nigeria as a decision support tool for sustainable water management in Nigeria.

Keywords: GIS, Modeling, Sensitivity Analysis, SWAT, Water Yield, Watershed level.

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Authors: A. Mohamed Mydeen, Pallapa Venkataram

Abstract:

The learning process needs to be so pervasive to impart the quality in acquiring the knowledge about a subject by making use of the advancement in the field of information and communication systems. However, pervasive learning paradigms designed so far are system automation types and they lack in factual pervasive realm. Providing factual pervasive realm requires subtle ways of teaching and learning with system intelligence. Augmentation of intelligence with pervasive learning necessitates the most efficient way of representing knowledge for the system in order to give the right learning material to the learner. This paper presents a method of representing knowledge for Pervasive Toolroom Maintenance System (PTMS) in which a learner acquires sublime knowledge about the various kinds of tools kept in the toolroom and also helps for effective maintenance of the toolroom. First, we explicate the generic model of knowledge representation for PTMS. Second, we expound the knowledge representation for specific cases of toolkits in PTMS. We have also presented the conceptual view of knowledge representation using ontology for both generic and specific cases. Third, we have devised the relations for pervasive knowledge in PTMS. Finally, events are identified in PTMS which are then linked with pervasive data of toolkits based on relation formulated. The experimental environment and case studies show the accuracy and efficient knowledge representation of toolkits in PTMS.

Keywords: Generic knowledge representation, toolkit, toolroom, pervasive computing.

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Authors: Iftikhar Ahmad, Youssef Elkezza

Abstract:

Sarir oilfield is in North Africa. It has facilities of oil and gas production. The assets of the Sarir oilfield can be divided into five following categories, namely: (i) Well bore and wellheads; (ii) Vessels such as separators, desalters, and gas processing facilities; (iii) Pipelines including all flow lines, trunk lines, and shipping lines; (iv) storage tanks; (v) Other assets such as turbines and compressors, etc. The nature of the petroleum industry recognizes the potential human, environmental and financial consequences that can result from failing to maintain the integrity of wellheads, vessels, tanks, pipelines, and other assets. The importance of effective asset integrity management increases as the industry infrastructure continues to age. The primary objective of assets integrity management (AIM) is to maintain assets in a fit-for-service condition while extending their remaining life in the most reliable, safe, and cost-effective manner. Corrosion management is one of the important aspects of successful asset integrity management. It covers corrosion mitigation, monitoring, inspection, and risk evaluation. External corrosion on pipelines, well bores, buried assets, and bottoms of tanks is controlled with a combination of coatings by cathodic protection, while the external corrosion on surface equipment, wellheads, and storage tanks is controlled by coatings. The periodic cleaning of the pipeline by pigging helps in the prevention of internal corrosion. Further, internal corrosion of pipelines is prevented by chemical treatment and controlled operations. This paper describes the integrity management system used in the Sarir oil field for its oil and gas production facilities based on standard practices of corrosion mitigation and inspection.

Keywords: Assets integrity management, corrosion prevention in oilfield assets, corrosion management in oilfield, corrosion prevention and inspection activities.

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Authors: D. I. De Souza, G. P. Azevedo, R. Rocha

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In this paper we will develop a sequential life test approach applied to a modified low alloy-high strength steel part used in highway overpasses in Brazil.We will consider two possible underlying sampling distributions: the Normal and theInverse Weibull models. The minimum life will be considered equal to zero. We will use the two underlying models to analyze a fatigue life test situation, comparing the results obtained from both.Since a major chemical component of this low alloy-high strength steel part has been changed, there is little information available about the possible values that the parameters of the corresponding Normal and Inverse Weibull underlying sampling distributions could have. To estimate the shape and the scale parameters of these two sampling models we will use a maximum likelihood approach for censored failure data. We will also develop a truncation mechanism for the Inverse Weibull and Normal models. We will provide rules to truncate a sequential life testing situation making one of the two possible decisions at the moment of truncation; that is, accept or reject the null hypothesis H0. An example will develop the proposed truncated sequential life testing approach for the Inverse Weibull and Normal models.

Keywords: Sequential life testing, normal and inverse Weibull models, maximum likelihood approach, truncation mechanism.

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Authors: S. M. Amin Hosseini, Oriol Pons, Albert de la Fuente

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After natural disasters, displaced people (DP) require important numbers of housing units, which have to be erected quickly due to emergency pressures. These tight timeframes can cause the multiplication of the environmental construction impacts. These negative impacts worsen the already high energy consumption and pollution caused by the building sector. Indeed, post-disaster housing, which is often carried out without pre-planning, usually causes high negative environmental impacts, besides other economic and social impacts. Therefore, it is necessary to establish a suitable strategy to deal with this problem which also takes into account the instability of its causes, like changing ratio between rural and urban population. To this end, this study aims to present a model that assists decision-makers to choose the most suitable building technology for post-disaster housing units. This model focuses on the alternatives sustainability and fulfillment of the stakeholders’ satisfactions. Four building technologies have been analyzed to determine the most sustainability technology and to validate the presented model. In 2003, Bam earthquake DP had their temporary housing units (THUs) built using these four technologies: autoclaved aerated concrete blocks (AAC), concrete masonry unit (CMU), pressed reeds panel (PR), and 3D sandwich panel (3D). The results of this analysis confirm that PR and CMU obtain the highest sustainability indexes. However, the second life scenario of THUs could have considerable impacts on the results.

Keywords: Sustainability, post-disaster temporary housing, integrated value model for sustainability assessment (MIVES), life cycle assessment (LCA).

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Authors: Federico Pérez González, Iñaki Goirizelaia Ordorika, Pedro Iriondo Bengoa

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In this work a new method for low complexity image coding is presented, that permits different settings and great scalability in the generation of the final bit stream. This coding presents a continuous-tone still image compression system that groups loss and lossless compression making use of finite arithmetic reversible transforms. Both transformation in the space of color and wavelet transformation are reversible. The transformed coefficients are coded by means of a coding system in depending on a subdivision into smaller components (CFDS) similar to the bit importance codification. The subcomponents so obtained are reordered by means of a highly configure alignment system depending on the application that makes possible the re-configure of the elements of the image and obtaining different importance levels from which the bit stream will be generated. The subcomponents of each importance level are coded using a variable length entropy coding system (VBLm) that permits the generation of an embedded bit stream. This bit stream supposes itself a bit stream that codes a compressed still image. However, the use of a packing system on the bit stream after the VBLm allows the realization of a final highly scalable bit stream from a basic image level and one or several improvement levels.

Keywords: Image compression, wavelet transform, highly scalable, reversible transform, embedded, subcomponents.

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Authors: T. C. Manjunath, B. Bandyopadhyay

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This paper deals with the design of a periodic output feedback controller for a flexible beam structure modeled with Timoshenko beam theory, Finite Element Method, State space methods and embedded piezoelectrics concept. The first 3 modes are considered in modeling the beam. The main objective of this work is to control the vibrations of the beam when subjected to an external force. Shear piezoelectric sensors and actuators are embedded into the top and bottom layers of a flexible aluminum beam structure, thus making it intelligent and self-adaptive. The composite beam is divided into 5 finite elements and the control actuator is placed at finite element position 1, whereas the sensor is varied from position 2 to 5, i.e., from the nearby fixed end to the free end. 4 state space SISO models are thus developed. Periodic Output Feedback (POF) Controllers are designed for the 4 SISO models of the same plant to control the flexural vibrations. The effect of placing the sensor at different locations on the beam is observed and the performance of the controller is evaluated for vibration control. Conclusions are finally drawn.

Keywords: Smart structure, Timoshenko beam theory, Periodic output feedback control, Finite Element Method, State space model, SISO, Embedded sensors and actuators, Vibration control.

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Authors: Jing Yu Pan, Dahai Liu

Abstract:

Airlines in the US have taken protective measures to battle the COVID-19 pandemic, with a mask mandate being the most important one, especially in the aircraft cabin. As the airline industry is recovering from the pandemic, mask-wearing will eventually become a personal choice during a flight. Nevertheless, COVID-19 will continue to create uncertainty for a long time into the future, making it necessary to understand the attitude and voluntary use of masks by air travelers on airplanes even after masks are no longer mandatory. This study aimed to understand the relationship between demographic characteristics and mask-wearing intention in the US. For age, gender, income, educational, and ethnicity groups, this study examined three factors – subjective norms, risk avoidance, and information seeking and their influence on the mask-wearing intention onboard airplanes during COVID-19, and whether or not attitude toward masks was an important mediator. The results show that all demographic factors except gender could help to explain the group variations in factor impact and the mediating effect in mask-wearing intentions. In particular, Asian travelers had mask-wearing intentions that were not affected by attitude either directly or indirectly. These findings provide useful implications to enhance the health safety of air travelers, especially in the US where opposing views toward mask-wearing still widely exist.

Keywords: COVID-19, passenger demographics, aircraft cabin, mask-wearing intention, attitude as mediator.

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Authors: Ahmed A. D. Sarhan, M. Sayuti, M. Hamdi

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Nowadays, the demand for high product quality focuses extensive attention to the quality of machined surface. The (CNC) milling machine facilities provides a wide variety of parameters set-up, making the machining process on the glass excellent in manufacturing complicated special products compared to other machining processes. However, the application of grinding process on the CNC milling machine could be an ideal solution to improve the product quality, but adopting the right machining parameters is required. In glass milling operation, several machining parameters are considered to be significant in affecting surface roughness. These parameters include the lubrication pressure, spindle speed, feed rate and depth of cut. In this research work, a fuzzy logic model is offered to predict the surface roughness of a machined surface in glass milling operation using CBN grinding tool. Four membership functions are allocated to be connected with each input of the model. The predicted results achieved via fuzzy logic model are compared to the experimental result. The result demonstrated settlement between the fuzzy model and experimental results with the 93.103% accuracy.

Keywords: CNC-machine, Glass milling, Grinding, Surface roughness, Cutting force, Fuzzy logic model.

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Authors: Moiz S. Vohra, Nagalingam Arun Prasanth, Wei L. Tan, S. H. Yeo

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The understanding of generation and collapse of acoustic cavitation bubbles are prerequisites for application of cavitation erosion. Microbubbles generated due to rapid fluctuation of pressure induced by propagation of ultrasonic wave lead to formation of high velocity microjets and or shock waves upon collapse. Due to vast application of ultrasonic, it is important to characterize and understand cavitation collapse pressure under the radiating surface at different conditions. A comparative investigation is carried out to determine impact load and dynamic pressure distribution exerted upon bubble collapse using thin film pressure sensors. Measurements were recorded at different input conditions such as amplitude, stand-off distance, insertion depth of the horn inside the liquid and pulse on-off time of acoustic vibrations. Impact force of 2.97 N is recorded at amplitude of 108 μm and stand-off distance of 1 mm from the sensor film, whereas impulsive force as low as 0.4 N is recorded at amplitude of 12 μm and stand-off distance of 5 mm from the sensor film. The results drawn from the investigation indicated that variety of impact loads can be achieved by controlling generation and collapse of bubbles, making it suitable to use for numerous application.

Keywords: Ultrasonic cavitation, bubble collapse, pressure mapping sensor, impact load.

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Authors: Shuen-Tai Wang, Fang-An Kuo, Chau-Yi Chou, Yu-Bin Fang

Abstract:

2016 has become the year of the Artificial Intelligence explosion. AI technologies are getting more and more matured that most world well-known tech giants are making large investment to increase the capabilities in AI. Machine learning is the science of getting computers to act without being explicitly programmed, and deep learning is a subset of machine learning that uses deep neural network to train a machine to learn  features directly from data. Deep learning realizes many machine learning applications which expand the field of AI. At the present time, deep learning frameworks have been widely deployed on servers for deep learning applications in both academia and industry. In training deep neural networks, there are many standard processes or algorithms, but the performance of different frameworks might be different. In this paper we evaluate the running performance of two state-of-the-art distributed deep learning frameworks that are running training calculation in parallel over multi GPU and multi nodes in our cloud environment. We evaluate the training performance of the frameworks with ResNet-50 convolutional neural network, and we analyze what factors that result in the performance among both distributed frameworks as well. Through the experimental analysis, we identify the overheads which could be further optimized. The main contribution is that the evaluation results provide further optimization directions in both performance tuning and algorithmic design.

Keywords: Artificial Intelligence, machine learning, deep learning, convolutional neural networks.

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Authors: A. M. Pashayev, D. D. Askerov, C. Ardil, R. A. Sadiqov, P. S. Abdullayev

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Researches show that probability-statistical methods application, especially at the early stage of the aviation Gas Turbine Engine (GTE) technical condition diagnosing, when the flight information has property of the fuzzy, limitation and uncertainty is unfounded. Hence the efficiency of application of new technology Soft Computing at these diagnosing stages with the using of the Fuzzy Logic and Neural Networks methods is considered. According to the purpose of this problem training with high accuracy of fuzzy multiple linear and non-linear models (fuzzy regression equations) which received on the statistical fuzzy data basis is made. For GTE technical condition more adequate model making dynamics of skewness and kurtosis coefficients- changes are analysed. Researches of skewness and kurtosis coefficients values- changes show that, distributions of GTE workand output parameters of the multiple linear and non-linear generalised models at presence of noise measured (the new recursive Least Squares Method (LSM)). The developed GTE condition monitoring system provides stage-by-stage estimation of engine technical conditions. As application of the given technique the estimation of the new operating aviation engine technical condition was made.

Keywords: aviation gas turbine engine, technical condition, fuzzy logic, neural networks, fuzzy statistics

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Authors: Nishant Rodrigues, Nicole Spanedda, Chilukuri K. Mohan, Arindam Chakraborty

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A crucial objective in quantum chemistry is the computation of the energy levels of chemical systems. This task requires electron-repulsion integrals as inputs and the steep computational cost of evaluating these integrals poses a major numerical challenge in efficient implementation of quantum chemical software. This work presents a moment-based machine learning approach for the efficient evaluation of electron-repulsion integrals. These integrals were approximated using linear combinations of a small number of moments. Machine learning algorithms were applied to estimate the coefficients in the linear combination. A random forest approach was used to identify promising features using a recursive feature elimination approach, which performed best for learning the sign of each coefficient, but not the magnitude. A neural network with two hidden layers was then used to learn the coefficient magnitudes, along with an iterative feature masking approach to perform input vector compression, identifying a small subset of orbitals whose coefficients are sufficient for the quantum state energy computation. Finally, a small ensemble of neural networks (with a median rule for decision fusion) was shown to improve results when compared to a single network.

Keywords: Quantum energy calculations, atomic orbitals, electron-repulsion integrals, ensemble machine learning, random forests, neural networks, feature extraction.

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Authors: Viviane Nascimento, Dania Mehmod

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Introduction: The Coronavirus (COVID-19) disease was firstly reported in Asia at the end of 2019 and became a pandemic at the beginning of 2020. It resulted in a significant impact over the global economy and the health care systems around the world. The immediate measure adopted worldwide to contain the virus was mainly the lockdown and curfews. This certainly had an important impact on expats workers due to the financial insecurity, culture barrier and distance from the family. Saudi Arabia has one of the largest flows of foreign workers in the world and expats are the majority of the workforce. The aim of this essay was assessing the psychological impact of COVID-19 in non-health care expats living in Saudi Arabia. Methods: The study was conducted in a construction company in Riyadh with non-health care employees. The cross-sectional study protocol was approved by the company's executive management. Employees who verbally agreed to participate in the study were asked to anonymously answer a questionnaire validated for behavioral research (DASS-21). In addition, a second questionnaire was created to assess feelings and emotions. Results: More than a third of participants screened positive for one or more psychological symptoms (depression, anxiety and stress) on the DASS-21 scale. Moreover, it was observed an increase on negative feelings on the additional questionnaire. Conclusion: This study reveals an increase on negative feelings and psychological symptoms among non-health care migrant workers during the COVID-19 pandemic. In light of this, it is crucial to understand the emotional effects caused by the pandemic on migrant workers in order to create supportive and informative strategies minimizing the emotional impact on this vulnerable group.

Keywords: COVID-19 pandemic, Saudi Arabia, psychological effects, migrant workers.

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Authors: Sanjay Saraf

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The oral cavity can be the site for early manifestations of mucocutaneous disorders (MD) or the only site for occurrence of these disorders. It can also exhibit oral lesions with simultaneous associated skin lesions. The MD involving the oral mucosa commonly presents with signs such as ulcers, vesicles and bullae. The unique environment of the oral cavity may modify these signs of the disease, thereby making the clinical diagnosis an arduous task. In addition to the unique environment of oral cavity, the overlapping of the signs of various mucocutaneous disorders, also makes the clinical diagnosis more intricate. The aim of this review is to present the oral signs of dermatological disorders having common oral involvement and emphasize their   importance in   early detection of the systemic disorders. The aim is also to highlight the necessity of oral examination by a dermatologist while examining the skin lesions. Prior to the oral examination, it must be imperative for the dermatologists and the dental clinicians to have the knowledge of oral anatomy. It is also important to know the impact of various diseases on oral mucosa, and the characteristic features of various oral mucocutaneous lesions. An initial clinical oral examination is may help in the early diagnosis of the MD. Failure to identify the oral manifestations may reduce the likelihood of early treatment and lead to more serious problems. This paper reviews the oral manifestations of immune mediated dermatological disorders with common oral manifestations.

Keywords: Vesiculobullous lesions, Desquamative gingivitis, Nikolsky’s sign, Erythema.

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Authors: Ayad Al-Mahturi, Herman Wahid

Abstract:

This paper presents an optimal state feedback controller based on Linear Quadratic Regulator (LQR) for a two-rotor aero-dynamical system (TRAS). TRAS is a highly nonlinear multi-input multi-output (MIMO) system with two degrees of freedom and cross coupling. There are two parameters that define the behavior of LQR controller: state weighting matrix and control weighting matrix. The two parameters influence the performance of LQR. Particle Swarm Optimization (PSO) is proposed to optimally tune weighting matrices of LQR. The major concern of using LQR controller is to stabilize the TRAS by making the beam move quickly and accurately for tracking a trajectory or to reach a desired altitude. The simulation results were carried out in MATLAB/Simulink. The system is decoupled into two single-input single-output (SISO) systems. Comparing the performance of the optimized proportional, integral and derivative (PID) controller provided by INTECO, results depict that LQR controller gives a better performance in terms of both transient and steady state responses when PSO is performed.

Keywords: Linear quadratic regulator, LQR controller, optimal control, particle swarm optimization, PSO, two-rotor aero-dynamical system, TRAS.

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Authors: Taha Bensiradj, Samira Moussaoui

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Hybrid Sensors and Vehicular Networks (HSVN), represent a hybrid network, which uses several generations of Ad-Hoc networks. It is used especially in Intelligent Transport Systems (ITS). The HSVN allows making collaboration between the Wireless Sensors Network (WSN) deployed on the border of the road and the Vehicular Network (VANET). This collaboration is defined by messages exchanged between the two networks for the purpose to inform the drivers about the state of the road, provide road safety information and more information about traffic on the road. Moreover, this collaboration created by HSVN, also allows the use of a network and the advantage of improving another network. For example, the dissemination of information between the sensors quickly decreases its energy, and therefore, we can use vehicles that do not have energy constraint to disseminate the information between sensors. On the other hand, to solve the disconnection problem in VANET, the sensors can be used as gateways that allow sending the messages received by one vehicle to another. However, because of the short communication range of the sensor and its low capacity of storage and processing of data, it is difficult to ensure the exchange of road messages between it and the vehicle, which can be moving at high speed at the time of exchange. This represents the time where the vehicle is in communication range with the sensor. This work is the proposition of a communication protocol between the sensors and the vehicle used in HSVN. The latter has as the purpose to ensure the exchange of road messages in the available time of exchange.

Keywords: HSVN, ITS, VANET, WSN.

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Authors: Ryota Sugino, Satoshi Mizoguchi, Koji Kimita, Keiichi Muramatsu, Tatsunori Matsui, Yoshiki Shimomura

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Improving added value and productivity of services entails improving both value-in-exchange and value-in-use. Value-in-use is realized by value co-creation, where providers and receivers create value together. In higher education services, value-in-use comes from learners achieving learning outcomes (e.g., knowledge and skills) that are consistent with their learning goals. To enhance the learning outcomes of a learner, it is necessary to enhance and utilize the abilities of the teacher along with the abilities of the learner. To do this, however, the learner and the teacher need to build a consensus about their respective roles. Teachers need to provide effective learning content; learners need to choose the appropriate learning strategies by using the learning content through consensus building. This makes consensus building an important factor in value co-creation. However, methods to build a consensus about their respective roles may not be clearly established, making such consensus difficult. In this paper, we propose some strategies for consensus building between a teacher and a learner in value co-creation. We focus on a teacher and learner co-design and propose an analysis method to clarify a collaborative design process to realize value co-creation. We then analyze some counseling data obtained from a university class. This counseling aimed to build a consensus for value-in-use, learning outcomes, and learning strategies between the teacher and the learner.

Keywords: Consensus building, value co-creation, higher education, learning service.

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Authors: Maruto Masserie Sardadi, Mohd Shafry bin Mohd Rahim, Zahabidin Jupri, Daut bin Daman

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The latest Geographic Information System (GIS) technology makes it possible to administer the spatial components of daily “business object," in the corporate database, and apply suitable geographic analysis efficiently in a desktop-focused application. We can use wireless internet technology for transfer process in spatial data from server to client or vice versa. However, the problem in wireless Internet is system bottlenecks that can make the process of transferring data not efficient. The reason is large amount of spatial data. Optimization in the process of transferring and retrieving data, however, is an essential issue that must be considered. Appropriate decision to choose between R-tree and Quadtree spatial data indexing method can optimize the process. With the rapid proliferation of these databases in the past decade, extensive research has been conducted on the design of efficient data structures to enable fast spatial searching. Commercial database vendors like Oracle have also started implementing these spatial indexing to cater to the large and diverse GIS. This paper focuses on the decisions to choose R-tree and quadtree spatial indexing using Oracle spatial database in mobile GIS application. From our research condition, the result of using Quadtree and R-tree spatial data indexing method in one single spatial database can save the time until 42.5%.

Keywords: Indexing, Mobile GIS, MapViewer, Oracle SpatialDatabase.

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Authors: S. Z. Sayed Hassen

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The load frequency control problem of power systems has attracted a lot of attention from engineers and researchers over the years. Increasing and quickly changing load demand, coupled with the inclusion of more generators with high variability (solar and wind power generators) on the network are making power systems more difficult to regulate. Frequency changes are unavoidable but regulatory authorities require that these changes remain within a certain bound. Engineers are required to perform the tricky task of adjusting the control system to maintain the frequency within tolerated bounds. It is well known that to minimize frequency variations, a large proportional feedback gain (speed regulation constant) is desirable. However, this improvement in performance using proportional feedback comes about at the expense of a reduced stability margin and also allows some steady-state error. A conventional PI controller is then included as a secondary control loop to drive the steadystate error to zero. In this paper, we propose a robust controller to replace the conventional PI controller which guarantees performance and stability of the power system over the range of variation of the speed regulation constant. Simulation results are shown to validate the superiority of the proposed approach on a simple single-area power system model.

Keywords: Robust control, power system, integral action, minimax LQG control.

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Authors: Hui Zhang, Ye Tian, Fang Ye, Ziming Guo

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Communication signal modulation recognition technology is one of the key technologies in the field of modern information warfare. At present, communication signal automatic modulation recognition methods are mainly divided into two major categories. One is the maximum likelihood hypothesis testing method based on decision theory, the other is a statistical pattern recognition method based on feature extraction. Now, the most commonly used is a statistical pattern recognition method, which includes feature extraction and classifier design. With the increasingly complex electromagnetic environment of communications, how to effectively extract the features of various signals at low signal-to-noise ratio (SNR) is a hot topic for scholars in various countries. To solve this problem, this paper proposes a feature extraction algorithm for the communication signal based on the improved Holder cloud feature. And the extreme learning machine (ELM) is used which aims at the problem of the real-time in the modern warfare to classify the extracted features. The algorithm extracts the digital features of the improved cloud model without deterministic information in a low SNR environment, and uses the improved cloud model to obtain more stable Holder cloud features and the performance of the algorithm is improved. This algorithm addresses the problem that a simple feature extraction algorithm based on Holder coefficient feature is difficult to recognize at low SNR, and it also has a better recognition accuracy. The results of simulations show that the approach in this paper still has a good classification result at low SNR, even when the SNR is -15dB, the recognition accuracy still reaches 76%.

Keywords: Communication signal, feature extraction, holder coefficient, improved cloud model.

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Authors: Hesham Abd Elkhalek, Sherif M. Hafez, Yasser M. El Fahham

Abstract:

Allocating limited budget to maintain bridge networks and selecting effective maintenance strategies for each bridge represent challenging tasks for maintenance managers and decision makers. In Egypt, bridges are continuously deteriorating. In many cases, maintenance works are performed due to user complaints. The objective of this paper is to develop a practical and reliable framework to manage the maintenance, repair, and rehabilitation (MR&R) activities of Bridges network considering performance and budget limits. The model solves an optimization problem that maximizes the average condition of the entire network given the limited available budget using Genetic Algorithm (GA). The framework contains bridge inventory, condition assessment, repair cost calculation, deterioration prediction, and maintenance optimization. The developed model takes into account multiple parameters including serviceability requirements, budget allocation, element importance on structural safety and serviceability, bridge impact on network, and traffic. A questionnaire is conducted to complete the research scope. The proposed model is implemented in software, which provides a friendly user interface. The framework provides a multi-year maintenance plan for the entire network for up to five years. A case study of ten bridges is presented to validate and test the proposed model with data collected from Transportation Authorities in Egypt. Different scenarios are presented. The results are reasonable, feasible and within acceptable domain.

Keywords: Bridge Management Systems (BMS), cost optimization condition assessment, fund allocation, Markov chain.

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Authors: Masoud Mahdavi

Abstract:

The structure is made using different members and combining them with each other. These members are basically based on technical and engineering principles and are combined in different ways and have their own unique effects on the building. Trusses are one of the most common and important members of the structure, accounting for a large percentage of the power transmission structure in the building. Different types of trusses are based on structural needs and evaluating and making complete comparisons between them is one of the most important engineering analyses. In the present study, four types of trusses have been studied; 1) Hawe truss, 2) Pratt truss, 3) k truss, and 4) warren truss, under cyclic loading for 80 seconds. The trusses are modeled in 3d using st₃₇ steel. The results showed that Hawe trusses had higher values ​​than all other trusses (k, Pratt and Warren) in all the studied indicators. Indicators examined in the study include; 1) von Mises stresses, 2) displacement, 3) support force, 4) velocity, 5) acceleration, 6) capacity (hysteresis curve) and 7) energy diagram. Pratt truss in indicators; Mises stress, displacement, energy have the least amount compared to other trusses. K truss in indicators; support force, speed and acceleration are the lowest compared to other trusses.

Keywords: Hawe truss, Pratt truss, K truss, Warren truss, cyclic loading, finite element method.

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Authors: Tulika Bhattacharyya, Suhita C. Chatterjee

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West Bengal, one of the most rapidly ageing states in India, has inadequate structure for elder care. Therefore, there is an urgent need to improve elder care which involves focusing on different care settings where the elderly exists, like - Homes, Hospitals and Long-Term Care facilities (e.g. - Old Age Homes, Hospices). The study explores various elder care settings, with the intention to develop an understanding about them, and thereby generate comprehensive information about the entire spectrum of elder care in Kolkata. Empirical data are collected from the elderly and their caregivers in different settings. The tools for data collection are narratives, in-depth interviews and focus group discussions, along with field observations. Mixed method design is adopted to analyze the complexities of elder care in different set ups. The major challenges of elder care in private Homes are: architecturally inadequate housing conditions, paucity of financial support and scarcity of skilled caregivers. While the key factors preventing the Hospital and Long-Term Care Facilities from providing elder care services are inadequate policies and set governmental standards for elder care for the hospitalized elderly in various departments of the Hospital and the elderly residing in different kinds of Long Term Care Facilities. The limitations in each care setting results in considerable neglect and abuse of the elderly. The major challenges in elder care in West Bengal are lack of continuum between different care settings/ peripheral location of private Homes within public health framework and inadequate state Palliative policy- including narcotic regulations. The study suggests remedial measures to improve the capacity to deliver elder care in different settings.

Keywords: Elder care settings, family caregiver, home care, geriatric hospital care, long term care facility.

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Authors: Luciana Steffen

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People with disabilities are often neglected in the exercise of their sexuality, facing several prejudices and discrimination in this area. For women with disabilities, the negligence is even major. Studies that relate sexual and reproductive rights with the experience of women with disabilities are rare, and in the field of Theology, practically nonexistent in Brazil. The aim of this work is to reflect on the relationship between women with disabilities, sexual and reproductive rights and Theology, according to a feminist perspective. The work is a literature review and involves the areas of Gender Studies, Disability Studies, Feminist Studies and Theology. In the article it will be addressed the relations between disability, sexual and reproductive rights, feminism, as well as the relations with the area of Theology, reflecting on these themes toward a fairer and more inclusive understanding of feminism, sexuality and women with disabilities. To reflect on sexual and reproductive rights of women with disabilities, it is important to reflect on religious concepts about the body, sexuality, reproduction and gender roles, because they are all connected. So, a critical analysis of traditional theological values taking into consideration the dimensions of sexuality and women with disability is important for a more liberating and inclusive understand about sexual and reproductive rights of women with disabilities. Theology should help the other areas in the understanding that all people have the right to live their lives with completeness, dignity and respect, so women with disabilities must have the opportunity of making their own choices on the fields of sexuality and reproduction.

Keywords: Disability, gender, sexual and reproductive rights, Theology.

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Authors: Bassam Istanbouli

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With the technology evolving every day and with the increase in global competition, industries are always under the pressure to be the best. They need to provide good quality products at competitive prices, when and how the customer wants them.  In order to achieve this level of service, products and their respective supply chain processes need to be flexible and evolvable; otherwise changes will be extremely expensive, slow and with many combinatorial effects. Those combinatorial effects impact the whole organizational structure, from a management, financial, documentation, logistics and specially the information system Enterprise Requirement Planning (ERP) perspective. By applying the normalized system concept/theory to segments of the supply chain, we believe minimal effects, especially at the time of launching an organization global software project. The purpose of this paper is to point out that if an organization wants to develop a software from scratch or implement an existing ERP software for their business needs and if their business processes are normalized and modular then most probably this will yield to a normalized and modular software system that can be easily modified when the business evolves. Another important goal of this paper is to increase the awareness regarding the design of the business processes in a software implementation project. If the blueprints created are normalized then the software developers and configurators will use those modular blueprints to map them into modular software. This paper only prepares the ground for further studies;  the above concept will be supported by going through the steps of developing, configuring and/or implementing a software system for an organization by using two methods: The Software Development Lifecycle method (SDLC) and the Accelerated SAP implementation method (ASAP). Both methods start with the customer requirements, then blue printing of its business processes and finally mapping those processes into a software system.  Since those requirements and processes are the starting point of the implementation process, then normalizing those processes will end up in a normalizing software.

Keywords: Blueprint, ERP, SDLC, Modular.

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Authors: Fernanda M. Assef, Maria Teresinha A. Steiner

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The aim of this paper is to compare and discuss better classifier algorithm options for credit risk assessment by applying different Machine Learning techniques. Using records from a Brazilian financial institution, this study uses a database of 5,432 companies that are clients of the bank, where 2,600 clients are classified as non-defaulters, 1,551 are classified as defaulters and 1,281 are temporarily defaulters, meaning that the clients are overdue on their payments for up 180 days. For each case, a total of 15 attributes was considered for a one-against-all assessment using four different techniques: Artificial Neural Networks Multilayer Perceptron (ANN-MLP), Artificial Neural Networks Radial Basis Functions (ANN-RBF), Logistic Regression (LR) and finally Support Vector Machines (SVM). For each method, different parameters were analyzed in order to obtain different results when the best of each technique was compared. Initially the data were coded in thermometer code (numerical attributes) or dummy coding (for nominal attributes). The methods were then evaluated for each parameter and the best result of each technique was compared in terms of accuracy, false positives, false negatives, true positives and true negatives. This comparison showed that the best method, in terms of accuracy, was ANN-RBF (79.20% for non-defaulter classification, 97.74% for defaulters and 75.37% for the temporarily defaulter classification). However, the best accuracy does not always represent the best technique. For instance, on the classification of temporarily defaulters, this technique, in terms of false positives, was surpassed by SVM, which had the lowest rate (0.07%) of false positive classifications. All these intrinsic details are discussed considering the results found, and an overview of what was presented is shown in the conclusion of this study.

Keywords: Artificial Neural Networks, ANNs, classifier algorithms, credit risk assessment, logistic regression, machine learning, support vector machines.

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