Search results for: opposition based learning

Authors: Sofiane Bououden, Ilyes Boulkaibet

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In this paper, a robust fault-tolerant predictive control (FTPC) strategy is proposed for systems with linear parameter varying (LPV) models and input constraints subject to sensor faults. Generally, virtual observers are used for improving the observation precision and reduce the impacts of sensor faults and uncertainties in the system. However, this type of observer lacks certain system measurements which substantially reduce its accuracy. To deal with this issue, a real observer is then designed based on the virtual observer, and consequently a real observer-based robust predictive control is designed for polytopic LPV systems. Moreover, the proposed observer can entirely assure that all system states and sensor faults are estimated. As a result, and based on both observers, a robust fault-tolerant predictive control is then established via the Lyapunov method where sufficient conditions are proposed, for stability analysis and control purposes, in linear matrix inequalities (LMIs) form. Finally, simulation results are given to show the effectiveness of the proposed approach.

Keywords: linear parameter varying systems, fault-tolerant predictive control, observer-based control, sensor faults, input constraints, linear matrix inequalities

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Authors: Sadeq Al Yaari, Muhammad Alkhunayn, Adham Al Yaari, Ayah Al Yaari, Ayman Al Yaari, Montaha Al Yaari, Sajedah Al Yaari, Fatehi Eissa

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The recent rise of smart educational aids and the move away from traditional listening aids are leading to a fundamental shift in the way in which individuals with attention and listening problems (ALP) manipulate listening inputs and/or act appropriately to the spoken information presented to them. A total sample of twenty-six ALP pupils (m=20 and f=6) between 7-12 years old was selected from different strata based on gender, region and school. In the sample size, thirteen (10 males and 3 females) received the treatment in terms of smart classes provided with smart educational aids in a listening course that lasted for four months, while others did not (they studied the same course by the same instructor but in ordinary class). A pretest was administered to assess participants’ levels, and a posttest was given to evaluate their attention and listening comprehension performance, namely in phonetic and phonological tests with sociolinguistic themes that have been designed for this purpose. Test results were analyzed both psychoneurolinguistically and statistically. Results reveal a remarkable change in pupils’ behavioral listening where scores witnessed a significant difference in the performance of the experimental ALP group in the pretest compared to the posttest (Pupils performed better at the pretest-posttest on phonetics than at the two tests on phonology). It is concluded that smart educational aids designed for listening skills help not only increase the listening command of pupils with ALP to understand what they listen to but also develop their interactive listening capability and, at the same rate, are responsible for increasing concentrated and in-depth listening capacity. Plus, ALP pupils become able to grasp the audio content of text recordings, including educational audio recordings, news, oral stories and tales, views, spiritual/religious text and general knowledge. However, the pupils have not experienced individual smart audio-visual aids that connect listening to other language receptive and productive skills, which could be the future area of research.

Keywords: smart educational aids, listening attention, pupils, problems

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Authors: Sara Hasani

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This research focuses on natural sudden onset disasters characterised as ‘occurring with little or no warning and often cause excessive injuries far surpassing the national response capacities’. Based on the panel analysis of the historic record of 4,252 natural onset disasters between 1980 to 2015, a predictive method was developed to predict the human impact of the disaster (fatality, injured, homeless) with less than 3% of errors. The geographical dispersion of the disasters includes every country where the data were available and cross-examined from various humanitarian sources. The records were then filtered into 4252 records of the disasters where the five predictive variables (disaster type, HDI, DRI, population, and population density) were clearly stated. The procedure was designed based on a combination of pattern recognition techniques and rule-based clustering for prediction and discrimination analysis to validate the results further. The result indicates that there is a relationship between the disaster human impact and the five socio-economic characteristics of the affected country mentioned above. As a result, a framework was put forward, which could predict the disaster’s human impact based on their severity rank in the early hours of disaster strike. The predictions in this model were outlined in two worst and best-case scenarios, which respectively inform the lower range and higher range of the prediction. A necessity to develop the predictive framework can be highlighted by noticing that despite the existing research in literature, a framework for predicting the human impact and estimating the needs at the time of the disaster is yet to be developed. This can further be used to allocate the resources at the response phase of the disaster where the data is scarce.

Keywords: disaster management, natural disaster, pattern recognition, prediction

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Authors: Hamidreza Saberkari, Mousa Shamsi, Hossein Ahmadi, Saeed Vaali, , MohammadHossein Sedaaghi

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In the recent years, using signal processing tools for accurate identification of the protein coding regions has become a challenge in bioinformatics. Most of the genomic signal processing methods is based on the period-3 characteristics of the nucleoids in DNA strands and consequently, spectral analysis is applied to the numerical sequences of DNA to find the location of periodical components. In this paper, a novel ensemble algorithm for gene selection in DNA sequences has been presented which is based on the combination of Goertzel algorithm and anti-notch filter (ANF). The proposed algorithm has many advantages when compared to other conventional methods. Firstly, it leads to identify the coding protein regions more accurate due to using the Goertzel algorithm which is tuned at the desired frequency. Secondly, faster detection time is achieved. The proposed algorithm is applied on several genes, including genes available in databases BG570 and HMR195 and their results are compared to other methods based on the nucleotide level evaluation criteria. Implementation results show the excellent performance of the proposed algorithm in identifying protein coding regions, specifically in identification of small-scale gene areas.

Keywords: protein coding regions, period-3, anti-notch filter, Goertzel algorithm

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Authors: Alam Ali, Ashok Kumar Pathak

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Path analysis is a statistical technique used to evaluate the strength of the direct and indirect effects of variables. One or more structural regression equations are used to estimate a series of parameters in order to find the better fit of data. Sometimes, exogenous variables do not show a significant strength of their direct and indirect effect when the assumption of classical regression (ordinary least squares (OLS)) are violated by the nature of the data. The main motive of this article is to investigate the efficacy of the copula-based regression approach over the classical regression approach and calculate the direct and indirect effects of variables when data violates the OLS assumption and variables are linked through an elliptical copula. We perform this study using a well-organized numerical scheme. Finally, a real data application is also presented to demonstrate the performance of the superiority of the copula approach.

Keywords: path analysis, copula-based regression models, direct and indirect effects, k-fold cross validation technique

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Authors: Bipasha Sen, Aditya Agarwal

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Multilingual automatic speech recognition (ASR) system is a single entity capable of transcribing multiple languages sharing a common phone space. Performance of such a system is highly dependent on the compatibility of the languages. State of the art speech recognition systems are built using sequential architectures based on recurrent neural networks (RNN) limiting the computational parallelization in training. This poses a significant challenge in terms of time taken to bootstrap and validate the compatibility of multiple languages for building a robust multilingual system. Complex architectural choices based on self-attention networks are made to improve the parallelization thereby reducing the training time. In this work, we propose Reed, a simple system based on 1D convolutions which uses very short context to improve the training time. To improve the performance of our system, we use raw time-domain speech signals directly as input. This enables the convolutional layers to learn feature representations rather than relying on handcrafted features such as MFCC. We report improvement on training and inference times by atleast a factor of 4x and 7.4x respectively with comparable WERs against standard RNN based baseline systems on SpeechOcean's multilingual low resource dataset.

Keywords: convolutional neural networks, language compatibility, low resource languages, multilingual automatic speech recognition

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Authors: Mohammad A. Alia, Abdelfatah Aref Tamimi, Omaima N. A. Al-Allaf

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This paper reviews a comparison study on the most common used authentication methods. Some of these methods are actually based on cryptography. In this study, we show the main cryptographic services. Also, this study presents a specific discussion about authentication service, since the authentication service is classified into several categorizes according to their methods. However, this study gives more about the real life example for each of the authentication methods. It talks about the simplest authentication methods as well about the available biometric authentication methods such as voice, iris, fingerprint, and face authentication.

Keywords: information security, cryptography, system access control, authentication, network security

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Authors: Shan Jia, Jinbao Chen, Jinhua Zhou, Jiacheng Qian

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Lunar exploration is a necessary foundation for deep-space exploration. For the functional limitations of the fixed landers which are widely used currently and are to expand the detection range by the use of wheeled rovers with unavoidable path-repeatability, a movable lunar soft-landing device based on cantilever type buffer mechanism and leg-foot type walking mechanism is presented. Firstly, a 20 DoFs quadruped configuration based on pushrod is proposed. The configuration is of the bionic characteristics such as hip, knee and ankle joints, and can make the kinematics of the whole mechanism unchanged before and after buffering. Secondly, the multi-function main/auxiliary buffers based on crumple-energy absorption and screw-nut mechanism, as well as the telescopic device which could be used to protect the plantar force sensors during the buffer process are designed. Finally, the kinematic model of the whole mechanism is established, and the configuration optimization of the whole mechanism is completed based on the performance requirements of slope adaptation and obstacle crossing. This research can provide a technical solution integrating soft-landing, large-scale inspection and material-transfer for future lunar exploration and even mars exploration, and can also serve as the technical basis for developing the reusable landers.

Keywords: configuration design, lunar soft-landing device, movable, optimization

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Authors: Ł. Kaczmarek, T. Wejrzanowski, M. Maksimczuk

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The study presents the use of the numerical calculations based on high-resolution computed microtomography in analysis of fluid flow through Miocene sandstones. Therefore, the permeability studies of rocks were performed. Miocene samples were taken from well S-3, located in the eastern part of the Carpathian Foredeep. For aforementioned analysis, two series of X-ray irradiation were performed. The first set of samples was selected to obtain the spatial distribution of grains and pores. At this stage of the study length of voxel side amounted 27 microns. The next set of X-ray irradation enabled recognition of microstructural components as well as petrophysical features. The length of voxel side in this stage was up to 2 µm. Based on this study, the samples were broken down into two distinct groups. The first one represents conventional reservoir deposits, in opposite to second one - unconventional type. Appropriate identification of petrophysical parameters such as porosity and permeability of the formation is a key element for optimization of the reservoir development.

Keywords: grains, permeability, pores, pressure distribution

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Authors: Juneseok You, Chanho Park, Kuehwan Jang, Sungsoo Na

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Sensing of nanotoxic materials is strongly important, as their engineering applications are growing recently and results in that nanotoxic material can harmfully influence human health and environment. In current study we report the quartz crystal microbalance (QCM)-based, in situ and real-time sensing of nanotoxic-material by frequency shift. We propose the in situ detection of nanotoxic material of zinc oxice by using QCM functionalized with a taget-specific DNA. Since the mass of a target material is comparable to that of an atom, the mass change caused by target binding to DNA on the quartz electrode is so small that it is practically difficult to detect the ions at low concentrations. In our study, we have demonstrated the in-situ and fast detection of zinc oxide using the quartz crystal microbalance (QCM). The detection was derived from the DNA hybridization between the DNA on the quartz electrode. The results suggest that QCM-based detection opens a new avenue for the development of a practical water-testing sensor.

Keywords: nanotoxic material, qcm, frequency, in situ sensing

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Authors: Lokman Gozcu, Sule Deniz Gozcu

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This study aims to analyze the skills of speaking in the acquisition of Turkish as a foreign language. One of the most important things for the individual who learns a foreign language is to be successful in the oral communication (speaking) skills and to interact in an understandable way. Speech skill requires much more time and effort than other language skills. In this direction, it is necessary to make an analysis of these oral communication skills, which is important in Turkish language acquisition as a foreign language and to draw out a road map according to the result. The aim of this study is to determine the competence and attitudes of speaking competence according to the individuals who learn Turkish as a foreign language and to be considered as speaking skill elements; Grammar, emphasis, intonation, body language, speed, ranking, accuracy, fluency, pronunciation, etc. and the results and suggestions based on these determinations. A mixed method has been chosen for data collection and analysis. A Likert scale (for competence and attitude) was applied to 190 individuals who were interviewed face-to-face (for speech skills) with a semi-structured interview form about 22 participants randomly selected. In addition, the observation form related to the 22 participants interviewed were completed by the researcher during the interview, and after the completion of the collection of all the voice recordings, analyses of voice recordings with the speech skills evaluation scale was made. The results of the research revealed that the speech skills of the individuals who learned Turkish as a foreign language have various perspectives. According to the results, the most inadequate aspects of the participants' ability to speak in Turkish include vocabulary, using humorous elements while speaking Turkish, being able to include items such as idioms and proverbs while speaking Turkish, Turkish fluency respectively. In addition, the participants were found not to feel comfortable while speaking Turkish, to feel ridiculous and to be nervous while speaking in formal settings. There are conclusions and suggestions for the situations that arise after the have been analyses made.

Keywords: learning Turkish as a foreign language, proficiency criteria, phonetic (modalities), speaking skills

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Authors: Mary Anne Roa

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Congestion in network occurs due to exceed in aggregate demand as compared to the accessible capacity of the resources. Network congestion will increase as network speed increases and new effective congestion control methods are needed, especially for today’s very high speed networks. To address this undeniably global issue, the study focuses on the development of a fuzzy-based congestion control model concerned with allocating the resources of a computer network such that the system can operate at an adequate performance level when the demand exceeds or is near the capacity of the resources. Fuzzy logic based models have proven capable of accurately representing a wide variety of processes. The model built is based on bandwidth, the aggregate incoming traffic and the waiting time. The theoretical analysis and simulation results show that the proposed algorithm provides not only good utilization but also low packet loss.

Keywords: congestion control, queue management, computer networks, fuzzy logic

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Authors: L. Bikulciene, E. Venskaityte, G. Jarusevicius

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Non-stationary and nonlinear signals generated by living complex systems defy traditional mechanistic approaches, which are based on homeostasis. Previous our studies have shown that the evaluation of the interactions of physiological signals by using special analysis methods is suitable for observation of physiological processes. It is demonstrated the possibility of using deep physiological model, based interpretation of the changes of the human body’s functional states combined with an application of the analytical method based on matrix theory for the physiological signals analysis, which was applied on high risk cardiac patients. It is shown that evaluation of cardiac signals interactions show peculiar for each individual functional changes at the onset of hemodynamic restoration procedure. Therefore we suggest that the alterations of functional state of the body, after patients overcome surgery can be complemented by the data received from the suggested approach of the evaluation of functional variables interactions.

Keywords: cardiac diseases, complex systems theory, ECG analysis, matrix analysis

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Authors: Rinda Kurnia

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Despite the differences and additional needs in the learning process, every individual has the right to receive educational services in order to enhance her/his abilities and potentials. This notion underlies the principle of inclusive education system, something many countries in the world are striving for since the UNESCO Salamanca Statement in 1994. This paper will consider different views that many theorists have published of the term inclusive, the issues, challenges, and dilemmas encountered during the practice, as well as some possible ways forward. It is being described, criticized and analyzed using the standpoint of a shadow teacher in an inclusive secondary school in Jakarta, Indonesia.

Keywords: inclusive education, inclusive education challenges, inclusive education dilemmas, inclusive education future plans, inclusive education issues

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Authors: Dylan Santos De Pinho, Nabil Ouerhani

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Energy consumption of machine-tools is becoming critical for machine-tool builders and end-users because of economic, ecological and legislation-related reasons. Many machine-tool builders are seeking for solutions that allow the reduction of energy consumption of machine-tools while preserving the same productivity rate and the same quality of machined parts. In this paper, we present the first results of a project conducted jointly by academic and industrial partners to reduce the energy consumption of a Swiss-Type lathe. We employ genetic algorithms to find optimal machining parameters – the set of parameters that lead to the best trade-off between energy consumption, part quality and tool lifetime. Three main machining process parameters are considered in our optimization technique, namely depth of cut, spindle rotation speed and material feed rate. These machining process parameters have been identified as the most influential ones in the configuration of the Swiss-type machining process. A state-of-the-art multi-objective genetic algorithm has been used. The algorithm combines three fitness functions, which are objective functions that permit to evaluate a set of parameters against the three objectives: energy consumption, quality of the machined parts, and tool lifetime. In this paper, we focus on the investigation of the fitness function related to energy consumption. Four different energy consumption related fitness functions have been investigated and compared. The first fitness function refers to the Kienzle cutting force model. The second fitness function uses the Material Removal Rate (RMM) as an indicator of energy consumption. The two other fitness functions are non-deterministic, learning-based functions. One fitness function uses a simple Neural Network to learn the relation between the process parameters and the energy consumption from experimental data. Another fitness function uses Lasso regression to determine the same relation. The goal is, then, to find out which fitness functions predict best the energy consumption of a Swiss-Type machining process for the given set of machining process parameters. Once determined, these functions may be used for optimization purposes – determine the optimal machining process parameters leading to minimum energy consumption. The performance of the four fitness functions has been evaluated. The Tornos DT13 Swiss-Type Lathe has been used to carry out the experiments. A mechanical part including various Swiss-Type machining operations has been selected for the experiments. The evaluation process starts with generating a set of CNC (Computer Numerical Control) programs for machining the part at hand. Each CNC program considers a different set of machining process parameters. During the machining process, the power consumption of the spindle is measured. All collected data are assigned to the appropriate CNC program and thus to the set of machining process parameters. The evaluation approach consists in calculating the correlation between the normalized measured power consumption and the normalized power consumption prediction for each of the four fitness functions. The evaluation shows that the Lasso and Neural Network fitness functions have the highest correlation coefficient with 97%. The fitness function “Material Removal Rate” (MRR) has a correlation coefficient of 90%, whereas the Kienzle-based fitness function has a correlation coefficient of 80%.

Keywords: adaptive machining, genetic algorithms, smart manufacturing, parameters optimization

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Authors: Smriti Pahwa, Karishma Vats, Aditi Macwan, Jija Dutt, Sumukhi Vaid

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Age approporiate complementary feeding has been stressed upon for sound young child nutrition and appropriate growth. National Infant and Young Child Feeding guidelines, policies and programs indicate cognizance of the issue taken by the country’s government, policy makers and technical experts. However, it is important that ordinary people, the caregivers of young children too understand the importance of appropriate feeding. For this, an interface might be required where ordinary people could participate in assessing the gaps in IYCF as a first step to take subsequent action. In this context an attempt was made to extrapolate a citizen led learning level survey that has been involving around 25000 ordinary citizens to reach out to 600,000 children annually for over a decade in India. Based on this philosophy of involving ordinary people in simple assessments to produce understandable actionable evidence, a rapid diet assessment tool was developed and collected from caregivers of 90 < 3year children from two urban clusters in Ahmedabad and Baroda, Gujarat. Target sample for pilot was selected after cluster census. Around half the mothers reported that they had not yet introduced water or other fluids to their < 6 month babies. However, about a third were already feeding them food other than mother’s milk. Although complementary feeding was initiated in almost all (95%) children more than 6 months old, frequency was suboptimal in 60%; in 80% cases no measure was taken to either improve energy or nutrient density; only 33% were fed protective foods; Green Leafy Vegetables consumption was negligible (1.4%). Anganwadi food was not consumed. By engaging ordinary people to generate evidence and understand the gaps, such assessments have the potential to be used to generate useful evidence for action at scale as well as locally.

Keywords: citizen led, grass root engagement, IYCF (Infant and Young Child Feeding), rapid diet assessment, under nutrition

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Authors: Won-Ho Kim

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In this paper, a design of H.263 based wireless video transceiver is presented for wireless camera system. It uses standard WIFI transceiver and the covering area is up to 100m. Furthermore the standard H.263 video encoding technique is used for video compression since wireless video transmitter is unable to transmit high capacity raw data in real time and the implemented system is capable of streaming at speed of less than 1Mbps using NTSC 720x480 video.

Keywords: wireless video transceiver, video surveillance camera, H.263 video encoding digital signal processing

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Authors: Khen Cohen, Daniel Yankelevich, David Mendlovic, Dan Raviv

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We present a 3D stereo system for high-resolution sensing in both the spatial and the temporal domains by combining a frame-based camera and an event-based camera. We establish a method to merge both devices into one unite system and introduce a calibration process, followed by a correspondence technique and interpolation algorithm for 3D reconstruction. We further provide quantitative analysis about our system in terms of depth resolution and additional parameter analysis. We show experimentally how our system performs temporal super-resolution up to effectively 1ms and can detect fast-moving objects and human micro-movements that can be used for micro-expression analysis. We also demonstrate how our method can extract colored events for an event-based camera without any degradation in the spatial resolution, compared to a colored filter array.

Keywords: DVS-CIS stereo vision, micro-movements, temporal super-resolution, 3D reconstruction

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Authors: Aydin Teymourifar, Gurkan Ozturk, Ozan Bahadir

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NSGA-II is one of the most well-known and most widely used evolutionary algorithms. In addition to its new versions, such as NSGA-III, there are several modified types of this algorithm in the literature. In this paper, a hybrid NSGA-II algorithm has been suggested for solving the multi-objective flexible job shop scheduling problem. For a better search, new neighborhood-based crossover and mutation operators are defined. To create new generations, the neighbors of the selected individuals by the tournament selection are constructed. Also, at the end of each iteration, before sorting, neighbors of a certain number of good solutions are derived, except for solutions protected by elitism. The neighbors are generated using a constraint-based neural network that uses various constructs. The non-dominated sorting and crowding distance operators are same as the classic NSGA-II. A comparison based on some multi-objective benchmarks from the literature shows the efficiency of the algorithm.

Keywords: flexible job shop scheduling problem, multi-objective optimization, NSGA-II algorithm, neighborhood structures

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Authors: Tanzeel Ur Rehman Khan, Franz Josef Behr, Phillip Davis

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Open source geospatial certifications are needed in geospatial technology education and industry sector. In parallel with proprietary software, free and open source software solutions become important in geospatial technology research and play an important role for the growth of the geospatial industry. ESRI, GISCI (GIS Certification Institute), ASPRS (American Society of Photogrammetry and remote sensing), and Meta spatial are offering certifications on proprietary and open source software. These are portfolio and competency based certifications depending on GIS Body of Knowledge (Bok). The analysis of these certification approaches might lead to the discovery of some gaps in them and will open a new way to develop certifications related to the geospatial open source (OS). This new certification will investigate the different geospatial competencies according to open source tools that help to identify geospatial professionals and strengthen the geospatial academic content. The goal of this research is to introduce a geospatial certification model based on geospatial technology competency model (GTCM).The developed certification will not only incorporate the importance of geospatial education and production of the geospatial competency-based workforce in universities and companies (private or public) as well as describe open source solutions with tools and technology. Job analysis, market analysis, survey analysis of this certification opens a new horizon for business as well.

Keywords: geospatial certification, open source, geospatial technology competency model, geoscience

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Authors: Guorong Sui, Xingwei Jia, Chenhui Yin, Xiumin Gao

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In the processing of camera-based high precision and non-contact measurement, the geometric-optical aberration is always inevitably disturbing the measuring system. Moreover, the aberration is different with the different focal length, which will increase the difficulties of the system’s calibration. Therefore, to understand the relationship between the focal length as a function of aberration properties is a very important issue to the calibration of the measuring systems. In this study, we propose a new mathematics model, which is based on the plane calibration method by Zhang Zhengyou, and establish a relationship between the focal length and aberration coefficient. By using the mathematics model and carefully modified compensation templates, the calibration precision of the system can be dramatically improved. The experiment results show that the relative error is less than 1%. It is important for optoelectronic imaging systems that apply to measure, track and position by changing the camera’s focal length.

Keywords: camera calibration, aberration coefficient, vision measurement, focal length, mathematics model

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Authors: M. Gulam Kibria, Shourav Ahmed, Kais Zaman

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In system analysis, the information on the uncertain input variables cause uncertainty in the system responses. Different probabilistic approaches for uncertainty representation and propagation in such cases exist in the literature. Different uncertainty representation approaches result in different outputs. Some of the approaches might result in a better estimation of system response than the other approaches. The NASA Langley Multidisciplinary Uncertainty Quantification Challenge (MUQC) has posed challenges about uncertainty quantification. Subproblem A, the uncertainty characterization subproblem, of the challenge posed is addressed in this study. In this subproblem, the challenge is to gather knowledge about unknown model inputs which have inherent aleatory and epistemic uncertainties in them with responses (output) of the given computational model. We use two different methodologies to approach the problem. In the first methodology we use sampling-based uncertainty propagation with first order error analysis. In the other approach we place emphasis on the use of Percentile-Based Optimization (PBO). The NASA Langley MUQC’s subproblem A is developed in such a way that both aleatory and epistemic uncertainties need to be managed. The challenge problem classifies each uncertain parameter as belonging to one the following three types: (i) An aleatory uncertainty modeled as a random variable. It has a fixed functional form and known coefficients. This uncertainty cannot be reduced. (ii) An epistemic uncertainty modeled as a fixed but poorly known physical quantity that lies within a given interval. This uncertainty is reducible. (iii) A parameter might be aleatory but sufficient data might not be available to adequately model it as a single random variable. For example, the parameters of a normal variable, e.g., the mean and standard deviation, might not be precisely known but could be assumed to lie within some intervals. It results in a distributional p-box having the physical parameter with an aleatory uncertainty, but the parameters prescribing its mathematical model are subjected to epistemic uncertainties. Each of the parameters of the random variable is an unknown element of a known interval. This uncertainty is reducible. From the study, it is observed that due to practical limitations or computational expense, the sampling is not exhaustive in sampling-based methodology. That is why the sampling-based methodology has high probability of underestimating the output bounds. Therefore, an optimization-based strategy to convert uncertainty described by interval data into a probabilistic framework is necessary. This is achieved in this study by using PBO.

Keywords: aleatory uncertainty, epistemic uncertainty, first order error analysis, uncertainty quantification, percentile-based optimization

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Authors: Gopinath Ganapathy, Chellammal Surianarayanan

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Services are growing rapidly and generally they are aggregated into a composite service to accomplish complex business processes. There may be several services that offer the same required function of a particular task in a composite service. Hence a choice has to be made for selecting suitable services from alternative functionally similar services. Quality of Service (QoS)plays as a discriminating factor in selecting which component services should be selected to satisfy the quality requirements of a user during service composition. There are two categories of approaches for QoS based service selection, namely global and local approaches. Global approaches are known to be Non-Polynomial (NP) hard in time and offer poor scalability in large scale composition. As an alternative to global methods, local selection methods which reduce the search space by breaking up the large/complex problem of selecting services for the workflow into independent sub problems of selecting services for individual tasks are coming up. In this paper, distributed architecture for selecting services based on QoS using local selection is presented with an overview of local selection methodology. The architecture describes the core components, namely, selection manager and QoS manager needed to implement the local approach and their functions. Selection manager consists of two components namely constraint decomposer which decomposes the given global or workflow level constraints in local or task level constraints and service selector which selects appropriate service for each task with maximum utility, satisfying the corresponding local constraints. QoS manager manages the QoS information at two levels namely, service class level and individual service level. The architecture serves as an implementation model for local selection.

Keywords: architecture of service selection, local method for service selection, QoS based service selection, approaches for QoS based service selection

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Authors: J. E. Cobbinah

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One of the most challenging issues confronting schools is good leadership succession planning. Experts argue that, although the idea of leadership succession planning is one of the strategies or practices that can help sustain improvement and promote continuity of good leadership, seem to have been neglected in many schools over the years. Appointment of head teachers in senior high schools is based on long service or one’s ability to demonstrate his/her competence in a leadership selection interview. There is no clear and well-structured leadership succession plan, before leadership position is filled, while school leadership succession planning seem to be an issue that nobody talks about. In faith-based schools the issue is even worse, because religious groups impose whoever they consider strong in the faith on schools as leaders, irrespective of the individual competence, ability to take up challenges associated with individuals’ preparedness to take up leadership position. Therefore, the present study examined the nature (including type) of leadership succession plans in faith-based senior high schools and its associated challenges. Convergent mixed method design was employed to effectively achieve the objectives of the study. The data collection strategies involved the use of interviews, questionnaires, and reviews of secondary data. The data was gathered from students, school leaders (head teachers, deputy heads, and head of departments), selected parents teachers associated members, school management committee members and members from school governors. The results show that governors of faith-based schools are making efforts to enhance education quality, by making school leadership accountable, the absence and the neglect of clear, and well-structured leadership succession plan has some negative outcomes. Unsustainable students’ academic performance, lack of support from existing staffs and senior leaders and lack of support in the implementation of school improvement plan. It would be concluded that, faith-based schools should focus on leadership competence and abilities in the selection process of potential school leaders to achieve a good succession plan rather than appointing leaders who are affiliates of one’s faith.

Keywords: school leadership, succession planning, faith-based schools, school governors

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Authors: Elcin Timur Cakmak, Ayse Oguzlar

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This study presents the results of the tweets sent by Twitter users on social media about the Russia-Ukraine war by bigram and trigram methods. On February 24, 2022, Russian President Vladimir Putin declared a military operation against Ukraine, and all eyes were turned to this war. Many people living in Russia and Ukraine reacted to this war and protested and also expressed their deep concern about this war as they felt the safety of their families and their futures were at stake. Most people, especially those living in Russia and Ukraine, express their views on the war in different ways. The most popular way to do this is through social media. Many people prefer to convey their feelings using Twitter, one of the most frequently used social media tools. Since the beginning of the war, it is seen that there have been thousands of tweets about the war from many countries of the world on Twitter. These tweets accumulated in data sources are extracted using various codes for analysis through Twitter API and analysed by Python programming language. The aim of the study is to find the word sequences in these tweets by the n-gram method, which is known for its widespread use in computational linguistics and natural language processing. The tweet language used in the study is English. The data set consists of the data obtained from Twitter between February 24, 2022, and April 24, 2022. The tweets obtained from Twitter using the #ukraine, #russia, #war, #putin, #zelensky hashtags together were captured as raw data, and the remaining tweets were included in the analysis stage after they were cleaned through the preprocessing stage. In the data analysis part, the sentiments are found to present what people send as a message about the war on Twitter. Regarding this, negative messages make up the majority of all the tweets as a ratio of %63,6. Furthermore, the most frequently used bigram and trigram word groups are found. Regarding the results, the most frequently used word groups are “he, is”, “I, do”, “I, am” for bigrams. Also, the most frequently used word groups are “I, do, not”, “I, am, not”, “I, can, not” for trigrams. In the machine learning phase, the accuracy of classifications is measured by Classification and Regression Trees (CART) and Naïve Bayes (NB) algorithms. The algorithms are used separately for bigrams and trigrams. We gained the highest accuracy and F-measure values by the NB algorithm and the highest precision and recall values by the CART algorithm for bigrams. On the other hand, the highest values for accuracy, precision, and F-measure values are achieved by the CART algorithm, and the highest value for the recall is gained by NB for trigrams.

Keywords: classification algorithms, machine learning, sentiment analysis, Twitter

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Authors: JingWei Yu, Hong Yang Yu

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At present, the increasingly serious air pollution in various cities of China has made people pay more attention to the air quality index(hereinafter referred to as AQI) of their living areas. To face this situation, it is of great significance to predict air pollution in heavily polluted areas. In this paper, based on the time series model of LSTM, a spatiotemporal prediction model of PM2.5 concentration in Mianyang, Sichuan Province, is established. The model fully considers the temporal variability and spatial distribution characteristics of PM2.5 concentration. The spatial correlation of air quality at different locations is based on the Air quality status of other nearby monitoring stations, including AQI and meteorological data to predict the air quality of a monitoring station. The experimental results show that the method has good prediction accuracy that the fitting degree with the actual measured data reaches more than 0.7, which can be applied to the modeling and prediction of the spatial and temporal distribution of regional PM2.5 concentration.

Keywords: LSTM, PM2.5, neural networks, spatio-temporal prediction

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Authors: Fahad Al Shidi, Reginald Victor

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This study was conducted in Northern Oman to assess the physical and chemical characteristics of 40 thermal springs distributed in Al Hajar Mountains in northern Oman. Physical measurements of water samples were carried out in two main seasons in Oman (winter and summer 2019). Studied springs were classified into three groups based on water temperature, four groups based on water pH values and two groups based on conductivity. Ten thermal alkaline springs that originated in Ophiolite (Samail Napp) were dominated by high pH (> 11), elevated concentration of Cl- and Na+ ions, relatively low temperature and discharge ratio. Other springs in the Hajar Super Group massif recorded high concentrations of Ca²+ and SO^2-₄ ions controlled by rock dominance, geochemistry processes, and mineralization. There was only one spring which has brackish water with very high conductivity (5500 µs/cm) and Total Dissolved Solids and it is not suitable for irrigation purposes because of the high abundance of Na+, Cl−, and Ca²+ ions.

Keywords: alkaline springs, geothermal, HSG, ophiolite

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Authors: Yousuf N. Al Khamisi, Eduardo M. Hernandez, Khurshid M. Khan

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Purpose: This paper presents a model of a Knowledge-Based (KB) using Lean Six Sigma (L6σ) principles to enhance the performance of healthcare leadership. Design/methodology/approach: Using L6σ principles to enhance healthcare leaders’ performance needs a pre-assessment of the healthcare organisation’s capabilities. The model will be developed using a rule-based approach of KB system. Thus, KB system embeds Gauging Absence of Pre-requisite (GAP) for benchmarking and Analytical Hierarchy Process (AHP) for prioritization. A comprehensive literature review will be covered for the main contents of the model with a typical output of GAP analysis and AHP. Findings: The proposed KB system benchmarks the current position of healthcare leadership with the ideal benchmark one (resulting from extensive evaluation by the KB/GAP/AHP system of international leadership concepts in healthcare environments). Research limitations/implications: Future work includes validating the implementation model in healthcare environments around the world. Originality/value: This paper presents a novel application of a hybrid KB combines of GAP and AHP methodology. It implements L6σ principles to enhance healthcare performance. This approach assists healthcare leaders’ decision making to reach performance improvement against a best practice benchmark.

Keywords: Lean Six Sigma (L6σ), Knowledge-Based System (KBS), healthcare leadership, Gauge Absence Prerequisites (GAP), Analytical Hierarchy Process (AHP)

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Authors: L. Hoade, O. O. Onafowokan, K. Anderson, G. E. Bartlett, E. D. Fern, M. R. Norton, R. G. Middleton

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Aims: Femoroacetabular impingement (FAI) was first recognised as a potential driver for hip pain at the turn of the last millennium. While there is an increasing trend towards surgical management of FAI by arthroscopic means, open surgical hip dislocation and debridement (SHD) remains the Gold Standard of care in terms of reported outcome measures. (1) Long-term functional and survivorship outcomes of SHD as a treatment for FAI are yet to be sufficiently reported in the literature. This study sets out to help address this imbalance. Methods: We undertook a retrospective review of our institutional database for all patients who underwent SHD for FAI between January 2003 and December 2008. A total of 223 patients (241 hips) were identified and underwent a ten year review with a standardised radiograph and patient-reported outcome measures questionnaire. The primary outcome measure of interest was survivorship, defined as progression to total hip arthroplasty (THA). Negative predictive factors were analysed. Secondary outcome measures of interest were survivorship to further (non-arthroplasty) surgery, functional outcomes as reflected by patient reported outcome measure scores (PROMS) scores, and whether a learning curve could be identified. Results: The final cohort consisted of 131 females and 110 males, with a mean age of 34 years. There was an overall native hip joint survival rate of 85.4% at ten years. Those who underwent a THA were significantly older at initial surgery, had radiographic evidence of preoperative osteoarthritis and pre- and post-operative acetabular undercoverage. In those whom had not progressed to THA, the average Non-arthritic Hip Score and Oxford Hip Score at ten year follow-up were 72.3% and 36/48, respectively, and 84% still deemed their surgery worthwhile. A learning curve was found to exist that was predicated on case selection rather than surgical technique. Conclusion: This is only the second study to evaluate the long-term outcomes (beyond ten years) of SHD for FAI and the first outside the originating centre. Our results suggest that, with correct patient selection, this remains an operation with worthwhile outcomes at ten years. How the results of open surgery compared to those of arthroscopy remains to be answered. While these results precede the advent of collison software modelling tools, this data helps set a benchmark for future comparison of other techniques effectiveness at the ten year mark.

Keywords: femoroacetabular impingement, hip pain, surgical hip dislocation, hip debridement

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Authors: Nina N. Serdar, Jelena R. Pejović

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This paper presents seismic risk assessment of two bridge structure, based on the probabilistic performance-based seismic assessment methodology. Both investigated bridges are tree span continuous RC curved bridges with the difference in column shapes. First bridge (type A) has a wall-type pier and second (type B) has a two-column bent with circular columns. Bridges are designed according to European standards: EN 1991-2, EN1992-1-1 and EN 1998-2. Aim of the performed analysis is to compare seismic behavior of these two structures and to detect the influence of column shapes on the seismic response. Seismic risk assessment is carried out by obtaining demand fragility curves. Non-linear model was constructed and time-history analysis was performed using thirty five pairs of horizontal ground motions selected to match site specific hazard. In performance based analysis, peak column drift ratio (CDR) was selected as engineering demand parameter (EDP). For seismic intensity measure (IM) spectral displacement was selected. Demand fragility curves that give probability of exceedance of certain value for chosen EDP were constructed and based on them conclusions were made.

Keywords: RC curved bridge, demand fragility curve, wall type column, nonlinear time-history analysis, circular column

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