Search results for: type-2 fuzzy sets

Authors: Fereshteh Zarei, Mahdi Kohandel

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The aim of this study was investigate and compare plyometric and vibration exercises on muscle explosive power in non-athlete female students. For this purpose, 45 female students from non-athletes selected target then divided in to the three groups, two experimental and one control groups. From all groups were getting pre-tested. Experimental A did whole-body vibration exercises involved standing on one of machine vibration with frequency 30 Hz, amplitude 10 mm and in 5 different postures. Training for each position was 40 seconds with 60 seconds rest between it, and each season 5 seconds was added to duration of each body condition, until time up to 2 minutes for each postures. Exercises were done three times a week for 2 month. Experimental group B did plyometric exercises that include jumping, such as horizontal, vertical, and skipping .They included 10 times repeat for 5 set in each season. Intensity with increasing repetitions and sets were added. At this time, asked from control group that keep a daily activity and avoided strength training, explosive power and. after do exercises by groups we measured factors again. One-way analysis of variance and paired t statistical methods were used to analyze the data. There was significant difference in the amount of explosive power between the control and vibration groups (p=0/048) there was significant difference between the control and plyometric groups (019/0 = p). But between vibration and plyometric groups didn't observe significant difference in the amount of explosive power.

Keywords: vibration, plyometric, exercises, explosive power, non-athlete

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Authors: Surajit Bhattacharya, Daniel Veltri, Atit A. Patel, Daniel N. Cox

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miRNAs have emerged as key post-transcriptional regulators of gene expression, however identification of biologically-relevant target genes for this epigenetic regulatory mechanism remains a significant challenge. To address this knowledge gap, we have developed a novel tool in R, Intra-miR-ExploreR, that facilitates integrated discovery of miRNA targets by incorporating target databases and novel target prediction algorithms, using statistical methods including Pearson and Distance Correlation on microarray data, to arrive at high confidence intragenic miRNA target predictions. We have explored the efficacy of this tool using Drosophila melanogaster as a model organism for bioinformatics analyses and functional validation. A number of putative targets were obtained which were also validated using qRT-PCR analysis. Additional features of the tool include downloadable text files containing GO analysis from DAVID and Pubmed links of literature related to gene sets. Moreover, we are constructing interaction maps of intragenic miRNAs, using both micro array and RNA-seq data, focusing on neural tissues to uncover regulatory codes via which these molecules regulate gene expression to direct cellular development.

Keywords: miRNA, miRNA:mRNA target prediction, statistical methods, miRNA:mRNA interaction network

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Authors: Robin Lok Wang Ma

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The purpose of this paper is to investigate how engineering students’ motivation and interests are maintained in their journeys. In recent years, different pedagogies of teaching, including entrepreneurship, experiential and lifelong learning, as well as dream builder, etc., have been widely used for education purposes. University advocates hands-on practice, learning by experiencing and experimenting throughout different courses. Students are not limited to gaining knowledge via traditional lectures, laboratory demonstrations, tutorials, and so on. The capability to identify both complex problems and their corresponding solutions in daily life are one of the criteria/skill sets required for graduates to obtain their careers at professional organizations and companies. A project-based course, namely Mechatronic Design and Prototyping, was developed for students to design and build a physical prototype for solving existing problems in their daily lives, thereby encouraging them as an entrepreneur to explore further possibilities to commercialize their designed prototypes and launch them to the market. Feedbacks from students show that they are keen to propose their own ideas freely with guidance from the instructor instead of using either suggested or assigned topics. Proposed ideas of the prototypes reflect that if students’ interests are maintained, they acquire the knowledge and skills they need, including essential communication, logical thinking, and, more importantly, problem solving for their lifelong learning journey.

Keywords: problem solving, lifelong learning, entrepreneurship, engineering

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Authors: Gayathri Sadanala, Shibam Pokhrel, Owen Murphy

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In the ever-evolving landscape of education, Virtual Reality (VR) environments offer a promising avenue for enhancing student engagement and learning experiences. However, understanding and predicting student behavior within these immersive settings remain challenging tasks. This paper presents a comprehensive study on the predictive modeling of student behavior in VR using machine learning techniques. We introduce a rich data set capturing student interactions, movements, and progress within a VR orientation program. The dataset is divided into training and testing sets, allowing us to develop and evaluate predictive models for various aspects of student behavior, including engagement levels, task completion, and performance. Our machine learning approach leverages a combination of feature engineering and model selection to reveal hidden patterns in the data. We employ regression and classification models to predict student outcomes, and the results showcase promising accuracy in forecasting behavior within VR environments. Furthermore, we demonstrate the practical implications of our predictive models for personalized VR-based learning experiences and early intervention strategies. By uncovering the intricate relationship between student behavior and VR interactions, we provide valuable insights for educators, designers, and developers seeking to optimize virtual learning environments.

Keywords: interaction, machine learning, predictive modeling, virtual reality

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Authors: A. I. Khalafalla, K. A. Al-Busada, I. M. El-Sabagh

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Pox and pox-like diseases of camels are a group of exanthematous skin conditions that have become increasingly important economically. They may be caused by three distinct viruses: camelpox virus (CMPV), camel contagious ecthyma virus (CCEV) and camel papillomavirus (CAPV). These diseases are difficult to differentiate based on clinical presentation in disease outbreaks. Molecular methods such as PCR targeting species-specific genes have been developed and used to identify CMPV and CCEV, but not simultaneously in a single tube. Recently, multiplex PCR has gained reputation as a convenient diagnostic method with cost- and time–saving benefits. In the present communication, we describe the development, optimization and validation a multiplex PCR assays able to detect simultaneously the genome of the three viruses in one single test allowing for rapid and efficient molecular diagnosis. The assay was developed based on the evaluation and combination of published and new primer sets, and was applied to the detection of 110 tissue samples. The method showed high sensitivity, and the specificity was confirmed by PCR-product sequencing. In conclusion, this rapid, sensitive and specific assay is considered a useful method for identifying three important viruses in specimens from camels and as part of a molecular diagnostic regime.

Keywords: multiplex PCR, diagnosis, pox and pox-like diseases, camels

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Authors: Seyed Esmail Seyedi Bariran, Khairul Salleh Mohamed Sahari

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In robotics literature, the simultaneous localization and mapping (SLAM) is commonly associated with a priori-posteriori problem. The autonomous vehicle needs a neutral map to spontaneously track its local position, i.e., “localization” while at the same time a precise path estimation of the environment state is required for effective route planning and obstacle avoidance. On the other hand, the environmental noise factors can significantly intensify the inherent uncertainties in using odometry information and measurements obtained from the robot’s exteroceptive sensor which in return directly affect the overall performance of the corresponding SLAM. Therefore, the current work is primarily dedicated to provide a diagnostic analysis of six SLAM algorithms including FastSLAM, L-SLAM, GraphSLAM, Grid SLAM and DP-SLAM. A SLAM simulated environment consisting of two sets of landmark locations and robot waypoints was set based on modified EKF and UKF in MATLAB using two separate maps for indoor and outdoor route planning subject to natural and artificial obstacles. The simulation results are expected to provide an unbiased platform to compare the estimation performances of the five SLAM models as well as on the reliability of each SLAM model for indoor and outdoor applications.

Keywords: route planning, obstacle, estimation performance, FastSLAM, L-SLAM, GraphSLAM, Grid SLAM, DP-SLAM

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Authors: Angela Maria Fasnacht

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Detection of anomalies due to contaminants’ presence, also known as early detection systems in water treatment plants, has become a critical point that deserves an in-depth study for their improvement and adaptation to current requirements. The design of these systems requires a detailed analysis and processing of the data in real-time, so it is necessary to apply various statistical methods appropriate to the data generated, such as Spearman’s Correlation, Factor Analysis, Cross-Correlation, and k-fold Cross-validation. Statistical analysis and methods allow the evaluation of large data sets to model the behavior of variables; in this sense, statistical treatment or analysis could be considered a vital step to be able to develop advanced models focused on machine learning that allows optimized data management in real-time, applied to early detection systems in water treatment processes. These techniques facilitate the development of new technologies used in advanced sensors. In this work, these methods were applied to identify the possible correlations between the measured parameters and the presence of the glyphosate contaminant in the single-pass system. The interaction between the initial concentration of glyphosate and the location of the sensors on the reading of the reported parameters was studied.

Keywords: glyphosate, emergent contaminants, machine learning, probes, sensors, predictive

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Authors: Hui Zhang, Sherif Beskhyroun

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Structural Health monitoring systems (SHM) are commonly used to identify and assess structural damage. In terms of damage detection, SHM needs to periodically collect data from sensors placed in the structure as damage-sensitive features. This includes abnormal changes caused by the strain field and abnormal symptoms of the structure, such as damage and deterioration. Currently, deploying sensors on a large scale in a building structure is a challenge. In this study, a highly stretchable strain sensors are used in this study to collect data sets of strain generated on the surface of full-size reinforced concrete (RC) frames under extreme cyclic load application. This sensing sheet can be switched freely between the test bending strain and the axial strain to achieve two different configurations. On this basis, the deep neural network prediction model of the frame beam and frame column is established. The training results show that the method can accurately predict the strain value and has good generalization ability. The two deep neural network prediction models will also be deployed in the SHM system in the future as part of the intelligent strain sensor system.

Keywords: strain sensing sheets, deep neural networks, strain measurement, SHM system, RC frames

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Authors: Raveendranath Veeramani, Parkash Chand, H. Y. Suma, A. Umamageswari

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Background and Aims: The aim of this study was to evaluate students’ perception of Integrated Learning Program[ILP]. Settings and Design: A questionnaire was used to survey and evaluate the perceptions of 1styear MBBS students at the Department of Anatomy at our medical college in India. Materials and Methods: The first MBBS Students of Anatomy were involved in the ILP on the Liver and extra hepatic biliary apparatus integrating the Departments of Anatomy, Biochemistry and Hepato-biliary Surgery. The evaluation of the ILP was done by two sets of short questionnaire that had ten items using the Likert five-point grading scale. The data involved both the students’ responses and their grading. Results: A majority of students felt that the ILP was better in as compared to the traditional lecture method of teaching.The integrated teaching method was better at fulfilling learning objectives (128 students, 83%), enabled better understanding (students, 94%), were more interesting (140 students, 90%), ensured that they could score better in exams (115 students, 77%) and involved greater interaction (100 students, 66%), as compared to traditional teaching methods. Most of the students (142 students, 95%) opined that more such sessions should be organized in the future. Conclusions: Responses from students show that the integrated learning session should be incorporated even at first phase of MBBS for selected topics so as to create interest in the medical sciences at the entry level and to make them understand the importance of basic science.

Keywords: integrated learning, students response, vertical integration, horizontal integration

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Authors: Leah Lee

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This paper examines the correlation between deep learning models and emotions with noises to see whether or not noises mask emotions. The deep learning models used are plain convolutional neural networks (CNN), auto-encoder, long short-term memory (LSTM), and Visual Geometry Group-16 (VGG-16). Emotion datasets used are Ryerson Audio-Visual Database of Emotional Speech and Song (RAVDESS), Crowd-sourced Emotional Multimodal Actors Dataset (CREMA-D), Toronto Emotional Speech Set (TESS), and Surrey Audio-Visual Expressed Emotion (SAVEE). To make it four times bigger, audio set files, stretch, and pitch augmentations are utilized. From the augmented datasets, five different features are extracted for inputs of the models. There are eight different emotions to be classified. Noise variations are white noise, dog barking, and cough sounds. The variation in the signal-to-noise ratio (SNR) is 0, 20, and 40. In summation, per a deep learning model, nine different sets with noise and SNR variations and just augmented audio files without any noises will be used in the experiment. To compare the results of the deep learning models, the accuracy and receiver operating characteristic (ROC) are checked.

Keywords: auto-encoder, convolutional neural networks, long short-term memory, speech emotion recognition, visual geometry group-16

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Authors: K. R. Thabethe, G. A. Adefolaju, M. J. Hosie

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Cervical cancer is the third most commonly diagnosed cancer globally and it is one of three AIDS defining malignancies. Highly active antiretroviral therapy (HAART) is a combination of three or more antiretroviral drugs and has been shown to play a significant role in reducing the incidence of some AIDS defining malignancies, although its effect on cervical cancer is still unclear. The aim of this study was to investigate the relationship between cervical cancer and HAART. This was achieved by studying the expression of two signalling molecules expressed in cervical cancer; MUC1 and P65. Following the 24 hour treatment of a cervical cancer cell line, HCS-2, with drugs which are commonly used as part of HAART at their clinical plasma concentrations, real-time qPCR and immunofluorescence were used in order to study gene and protein expression. A one way ANOVA followed by a Tukey Kramer Post Hoc test was conducted using JMP 11 software on both sets of data. The drug classified as a protease inhibitor (PI) (i.e. LPV/r) reduced MUC1 and P65 gene and protein expression more than the other drug tested. PIs are known to play a significant role in cell death, therefore the cells were thought to be more susceptible to cell death following treatment with PIs. In conclusion, the drugs used, especially the PI showed some anticancer effects by facilitating cell death through decreased gene and protein expression of MUC1 and P65 and present promising agents for cancer treatment.

Keywords: cervical cancer, haart, MUC1, P65

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Authors: Azizah Ismail, Zainab Khalifah, Abbas Mardani

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Sustainable community-based tourism through homestay programme is a growing niche market that has impacted destinations in many countries including Malaysia. With demand predicted to continue increasing, the importance of the homestay product will grow in the tourism industry. This research examines the sustainability criteria for homestay programme in Malaysia covering economic, socio-cultural and environmental dimensions. This research applied a two-stage methodology for data analysis. Specifically, the researcher implements a hybrid method which combines two multi-criteria decision making approaches. In the first stage of the methodology, the Decision Making Trial and Evaluation Laboratory (DEMATEL) technique is applied. Then, Analytical Network Process (ANP) is employed for the achievement of the objective of the current research. After factors identification and problem formulation, DEMATEL is used to detect complex relationships and to build a Network Relation Map (NRM). Then ANP is used to prioritize and find the weights of the criteria and sub-criteria of the decision model. The research verifies the framework of multi-criteria for sustainable community-based tourism from the perspective of stakeholders. The result also provides a different perspective on the importance of sustainable criteria from the view of multi-stakeholders. Practically, this research gives the framework model and helps stakeholders to improve and innovate the homestay programme and also promote community-based tourism.

Keywords: community-based tourism, homestay programme, sustainable tourism criteria, sustainable tourism development

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Authors: Rayhaneh Amooaghaie, Maryam Norouzi

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In this study, the effects of nTiO₂ on seed germination and growth of six plant species (wheat, soybean, tomato, canola, cucumber, and lettuce) were evaluated in petri dish (direct exposure) and in soil in a greenhouse experiment (soil exposure). Data demonstrate that under both culture conditions, low or mild concentrations of nTiO₂ either stimulated or had no effect on seed germination, root growth and vegetative biomass while high concentrations had an inhibitory effect. However, results showed that the impacts of nTiO₂ on plant growth in soil were partially consistent with those observed in pure culture. Based on both experiment sets, among above six species, lettuce and canola were the most susceptible and the most tolerant species to nTiO₂ toxicity. However, results revealed the impacts of nTiO₂ on plant growth in soil were less than petri dish exposure probability due to dilution in soil and complexation/aggregation of nTiO₂ that would lead to lower exposure of plants. The high concentrations of nTiO₂ caused significant reductions in fresh and dry weight of aerial parts and root and chlorophyll and carotenoids contents of all species which also coincided with further accumulation of malondialdehyde (MDA). These findings suggest that decreasing growth might be the result of an nTiO₂-induced oxidative stress and disturbance of photosynthesis systems.

Keywords: chlorophyll, lipid peroxidation, nano TiO₂, seed germination

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Authors: Judith Mwakalonge, Geophrey Mbatta, Cuthbert Ruseruka, Gurcan Comert, Saidi Siuhi

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Recent advancements in technology have fostered the development of Electric Vehicles (EVs) that provides relief from transportation dependence on natural fossil fuels as sources of energy. It is estimated that more than 50% of petroleum is used for transportation, which accounts for 28% of annual energy use. Vehicles make up about 82% of all transportation energy use. It is also estimated that about 22% of global Carbon dioxide (CO2) emissions are produced by the transportation sector, therefore, it raises environmental concerns. Governments worldwide, including the United States, are investing in developing EVs to resolve the issues related to the use of natural fossil fuels, such as air pollution due to emissions. For instance, the Bipartisan Infrastructure Law (BIL) that was signed by President Biden on November 15th, 2021, sets aside about $5 billion to be apportioned to all 50 states, the District of Columbia, and Puerto Rico for the development of EV chargers. These chargers should be placed in a way that maximizes their utility. This study aims at studying the charging behaviors of Electric Vehicle (EV) users to establish factors to be considered in the selection of charging locations. The study will focus on social-economic and land use data by studying the relationship between charging time and charging locations. Local factors affecting the charging time and the chargers’ utility will be investigated.

Keywords: electric vehicles, EV charging stations, social economic factors, charging networks

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Authors: Benga Ebouele, Thomas Tengen

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

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

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Authors: Shreya Saxena, Felix Miller-Molloy, Phillipa Bowen, Greg Fellows, Elizabeth Bowen

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Background: Complex fluid balance abnormalities are well-established post-neurosurgery and traumatic brain injury (TBI). The triple-phase response requires fluid management strategies reactive to urine output and sodium homeostasis as patients shift between Diabetes Insipidus (DI) and Syndrome of Inappropriate ADH (SIADH). It was observed, at a tertiary paediatric center, a relatively high prevalence of the above complications within a cohort of paediatric post-neurosurgical and TBI patients. An audit of the clinical practice against set institutional guidelines was undertaken and analyzed to understand why this was occurring. Based on those results, new guidelines were developed with structured educational packages for the specialist teams involved. This was then reaudited, and the findings were compared. Methods: Two independent audits were conducted across two time periods, pre and post guideline change. Primary data was collected retrospectively, including both qualitative and quantitative data sets from the CQUIN neurosurgical database and electronic medical records. All paediatric patients post posterior fossa (PFT) or supratentorial surgery or with a TBI were included. A literature review of evidence-based practice, initial audit data, and stakeholder feedback was used to develop new clinical guidelines and nursing standard operation procedures. Compliance against these newly developed guidelines was re-assessed and a thematic, trend-based analysis of the two sets of results was conducted. Results: Audit-1 January2017-June2018, n=80; Audit-2 January2020-June2021, n=30 (reduced operative capacity due to COVID-19 pandemic). Overall, improvements in the monitoring of both fluid balance and electrolyte trends were demonstrated; 51% vs. 77% and 78% vs. 94%, respectively. The number of clear fluid management plans documented postoperatively also increased (odds ratio of 4), leading to earlier recognition and management of evolving fluid-balance abnormalities. The local paediatric endocrine team was involved in the care of all complex cases and notified sooner for those considered to be developing DI or SIADH (14% to 35%). However, significant Na fluctuations (>12mmol in 24 hours) remained similar – 5 vs six patients – found to be due to complex pituitary hypothalamic pathology – and the recommended adaptive fluid management strategy was still not always used. Qualitative data regarding useability and understanding of fluid-balance abnormalities and the revised guidelines were obtained from health professionals via surveys and discussion in the specialist teams providing care. The feedback highlighted the new guidelines provided a more consistent approach to the post-operative care of these patients and was a better platform for communication amongst the different specialist teams involved. The potential limitation to our study would be the small sample size on which to conduct formal analyses; however, this reflects the population that we were investigating, which we cannot control. Conclusion: The revised clinical guidelines, based on audited data, evidence-based literature review and stakeholder consultations, have demonstrated an improvement in understanding of the neuro-endocrine complications that are possible, as well as increased compliance to post-operative monitoring of fluid balance and electrolytes in this cohort of patients. Emphasis has been placed on preventative rather than treatment of DI and SIADH. Consequently, this has positively impacted patient safety for the center and highlighted the importance of educational awareness and multi-disciplinary team working.

Keywords: post-operative, fluid-balance management, neuro-endocrine complications, paediatric

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Authors: Justina I. R. Udotong, Essien U. Essien

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Diagnostic enzymes like aspartate aminotransferase (AST), alanine aminotransferase (ALT) and alkaline phosphatase (ALP) were determined as indices of heavy metal pollution in Tilapia guinensis. Three different sets of fishes treated with lead (Pb), iron (Fe) and copper (Cu) were used for the study while a fourth group with no heavy metal served as a control. Fishes in each of the groups were exposed to 2.65 mg/l of Pb, 0.85 mg/l of Fe and 0.35 mg/l of Cu in aerated aquaria for 96 hours. Tissue fractionation of the liver tissues was carried out and the three diagnostic enzymes (AST, ALT, and ALP) were estimated. Serum levels of the same diagnostic enzymes were also measured. The mean values of the serum enzyme activity for ALP in each experimental group were 19.5±1.62, 29.67±2.17 and 1.15±0.27 IU/L for Pb, Fe and Cu groups compared with 9.99±1.34 IU/L enzyme activity in the control. This result showed that Pb and Fe caused increased release of the enzyme into the blood circulation indicating increased tissue damage while Cu caused a reduction in the serum level as compared with the level in the control group. The mean values of enzyme activity obtained in the liver were 102.14±6.12, 140.17±2.06 and 168.23±3.52 IU/L for Pb, Fe and Cu groups, respectively compared to 91.20±9.42 IU/L enzyme activity for the control group. The serum and liver AST and ALT activities obtained in Pb, Fe, Cu and control groups are reported. It was generally noted that the presence of the heavy metal caused liver tissues damage and consequent increased level of the diagnostic enzymes in the serum.

Keywords: diagnostic enzymes, enzyme activity, heavy metals, tissues investigations

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Authors: Cara DiGirolamo

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In his 2017 Ph.D. thesis, script doctor David Baboulene presented a theory of fiction in which differences in the knowledge states between participants in a literary experience, including reader, author, and characters, create many story elements, among them suspense, expectations, subtext, theme, metaphor, and allegory. This theory can be adjusted and modeled by incorporating a formal pragmatic approach that understands narrative as a speech act with a conversational function. This approach requires both the Speaker and the Listener to be understood as participants in the discourse. It also uses theories of cooperativity and the QUD to identify the existence of implicit questions. This approach predicts that what an effective literary narrative must do: provide a conversational context early in the story so the reader can engage with the text as a conversational participant. In addition, this model incorporates schema theory. Schema theory is a cognitive model for learning and processing information about the world and transforming it into functional knowledge. Using this approach can extend the QUD model. Instead of describing conversation as a form of information gathering restricted to question-answer sets, the QUD can include knowledge modeling and understanding as a possible outcome of a conversation. With this model, Baboulene’s “Knowledge Gaps” can provide real insight into storytelling as a conversational move, and extend the QUD to be able to simply and effectively apply to a more diverse set of conversational interactions and also to narrative texts.

Keywords: literature, speech acts, QUD, literary theory

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Authors: S. Gvazava, N. Khidasheli, G. Gordeziani, A. DL. Batako

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The work presented in this paper studied the tribological characteristics (wear resistance, friction coefficient) of austempered ductile iron (ADI) with different combinations of structural composition (upper bainite, lower bainite, retained austenite) in dry sliding friction. A range of structural states of the metal matrix was obtained by changing the regimes of isothermal quenching of high-strength cast iron. The tribological tests were carried out using two sets of isothermal quenched cast irons. After austenitization at 900°С for 60 minutes, the specimens from the first group were isothermally quenched at the 300°С temperature and the specimens from the second set – at 400°С. The investigations showed that the isothermal quenching increases the friction coefficient of high-strength cast irons. The friction coefficient was found to be in the range from 0.4 to 0.55 for cast irons, depending on the structures of the metal matrix. The quenched cast irons having lower bainite demonstrate higher wear resistance in dry friction conditions. The dependence of wear resistance on the amount of retained austenite in isothermal quenched cast irons has a nonlinear characteristic and reaches its maximum value when the content of retained austenite is about 15-22%. The boron micro-additives allowed to reduce the friction coefficient of ADI and increase their wear resistance by 1.5-1.7 times.

Keywords: wear resistance, dry sliding, austempering, ADI, friction coefficient, retained austenite, isothermal quenching

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Authors: Farej Ahmed Emhmmed

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Fracture of duplex stainless steels (DSS) was investigated in air and in 3.5 wt % NaCl solution. Tow sets of fatigued specimens were heat treated at 475ºC for different times and pulled to failure either in air or after kept in 3.5% NaCl with polarization of -900 mV/ SCE. Fracture took place in general by ferrite cleavage and austenite ductile fracture in transgranular mode. Specimens measured stiffness (Ms) was affected by the aging time, with higher values measured for specimens aged for longer times. Microstructural features played a role in "blocking" the crack propagation process leading to lower the CTOD values specially for specimens aged for short times. Unbroken ligaments/ austenite were observed at the crack wake. These features may exerted a bridging stress, blocking effect, at the crack tip giving resistance to the crack propagation process i.e the crack mouth opening was reduced. Higher stress intensity factor Kıc values were observed with increased amounts of crack growth suggesting longer zone of unbroken ligaments in the crack wake. The bridging zone was typically several mm in length. Attempt to model the bridge stress was suggested to understand the role of ligaments/unbroken austenite in increasing the fracture toughness factor.

Keywords: stainless steels, fracture toughness, crack keying effect, ligaments

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Authors: Rabab M. Ramadan, Elaraby A. Elgallad

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With extensive application, the performance of unimodal biometrics systems has to face a diversity of problems such as signal and background noise, distortion, and environment differences. Therefore, multimodal biometric systems are proposed to solve the above stated problems. This paper introduces a bimodal biometric recognition system based on the extracted features of the human palm print and iris. Palm print biometric is fairly a new evolving technology that is used to identify people by their palm features. The iris is a strong competitor together with face and fingerprints for presence in multimodal recognition systems. In this research, we introduced an algorithm to the combination of the palm and iris-extracted features using a texture-based descriptor, the Scale Invariant Feature Transform (SIFT). Since the feature sets are non-homogeneous as features of different biometric modalities are used, these features will be concatenated to form a single feature vector. Particle swarm optimization (PSO) is used as a feature selection technique to reduce the dimensionality of the feature. The proposed algorithm will be applied to the Institute of Technology of Delhi (IITD) database and its performance will be compared with various iris recognition algorithms found in the literature.

Keywords: iris recognition, particle swarm optimization, feature extraction, feature selection, palm print, the Scale Invariant Feature Transform (SIFT)

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Authors: M. D. Dogara, C. G, Afuwai, O. O. Esther, A. M. Dawai

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For two decades, the inhabitants of Kujama Prison Farm faced problems of water for domestic and agricultural purposes, even after the drilling of three deep boreholes. The scarcity of this groundwater resource led to the geophysical investigation of the basement complex of the prison farm. Two geophysical techniques, vertical electrical sounding and seismic refraction methods were deployed to unravel the cause(s) of the non-productivity of the three boreholes. The area of investigation covered was 400,000 m2 of ten profiles with six investigative points. In all, 60 vertical electrical points were sounded, and sixty sets of seismic refraction data were collected using the forward and reverse approach. From the geoelectric sections, it is suggestive that the area is underlain by three to five geoelectric layers of varying thicknesses and resistivities. The result of the interpreted seismic data revealed two geovelocity layers, with velocities ranging between 478m/s to 1666m/s for the first layer and 1166m/s to 7141m/s for the second layer. From the combined results of the two techniques, it was suggestive that all the three unproductive boreholes were drilled at points that were neither weathered nor fractured. It was, therefore, suggested that new boreholes should be drilled at areas identified with depressed bedrock topography having geophysical evidence of intense weathering and fracturing within the fresh basement.

Keywords: groundwater, Kujama prison farm, kaduna, nigeria, seismic refraction, vertical electrical sounding

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Authors: Yeliz Karaca, Rana Karabudak

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Multifractal Denoising techniques are used in the identification of significant attributes by removing the noise of the dataset. Magnetic resonance (MR) image technique is the most sensitive method so as to identify chronic disorders of the nervous system such as Multiple Sclerosis. MRI and Expanded Disability Status Scale (EDSS) data belonging to 120 individuals who have one of the subgroups of MS (Relapsing Remitting MS (RRMS), Secondary Progressive MS (SPMS), Primary Progressive MS (PPMS)) as well as 19 healthy individuals in the control group have been used in this study. The study is comprised of the following stages: (i) L2 Norm Multifractal Denoising technique, one of the multifractal technique, has been used with the application on the MS data (MRI and EDSS). In this way, the new dataset has been obtained. (ii) The new MS dataset obtained from the MS dataset and L2 Multifractal Denoising technique has been applied to the K-Means and Fuzzy C Means clustering algorithms which are among the unsupervised methods. Thus, the clustering performances have been compared. (iii) In the identification of significant attributes in the MS dataset through the Multifractal denoising (L2 Norm) technique using K-Means and FCM algorithms on the MS subgroups and control group of healthy individuals, excellent performance outcome has been yielded. According to the clustering results based on the MS subgroups obtained in the study, successful clustering results have been obtained in the K-Means and FCM algorithms by applying the L2 norm of multifractal denoising technique for the MS dataset. Clustering performance has been more successful with the MS Dataset (L2_Norm MS Data Set) K-Means and FCM in which significant attributes are obtained by applying L2 Norm Denoising technique.

Keywords: clinical decision support, clustering algorithms, multiple sclerosis, multifractal techniques

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Authors: Xinhuang Wu, Yousef Sardahi

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A hybrid and optimal multi-loop control structure combining linear and nonlinear control algorithms are introduced in this paper to regulate the position of a quadcopter unmanned aerial vehicle (UAV) driven by four brushless DC motors. To this end, a nonlinear mathematical model of the UAV is derived and then linearized around one of its operating points. Using the nonlinear version of the model, a sliding mode control is used to derive the control laws of the motor thrust forces required to drive the UAV to a certain position. The linear model is used to design two controllers, XG-controller and YG-controller, responsible for calculating the required roll and pitch to maneuver the vehicle to the desired X and Y position. Three attitude controllers are designed to calculate the desired angular rates of rotors, assuming that the Euler angles are minimal. After that, a many-objective optimization problem involving 20 design parameters and ten objective functions is formulated and solved by HypE (Hypervolume estimation algorithm), one of the widely used many-objective optimization algorithms approaches. Both stability and performance constraints are imposed on the optimization problem. The optimization results in terms of Pareto sets and fronts are obtained and show that some of the design objectives are competing. That is, when one objective goes down, the other goes up. Also, Numerical simulations conducted on the nonlinear UAV model show that the proposed optimization method is quite effective.

Keywords: optimal control, many-objective optimization, sliding mode control, linear control, cascade controllers, UAV, drones

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Authors: Kanwal Javid, Shazia Pervaiz, Maria Mumtaz, Muhammad Ameer Nawaz Akram

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Forests and grasslands ecosystems play an important role in the global carbon cycle. Land management activities influence both ecosystems and enable them to absorb and sequester carbon dioxide (CO2). Similarly, in Pakistan, these terrestrial ecosystems are well known to mitigate carbon emissions and have a great source to supply a variety of services such as clean air and water, biodiversity, wood products, wildlife habitat, food, recreation and carbon sequestration. Carbon sequestration is the main agenda of developed and developing nations to reduce the impacts of global warming. But the amount of carbon storage within these ecosystems can be affected by many factors related to air quality such as land management, land-use change, deforestation, over grazing and natural calamities. Moreover, the long-term capacity of forests and grasslands to absorb and sequester CO2 depends on their health, productivity, resilience and ability to adapt to changing conditions. Thus, the main rationale of this study is to monitor the difference in carbon amount of forests and grasslands of Northern Pakistan using MODIS data sets and map results using Geographic Information System. Results of the study conclude that forests ecosystems are more effective in reducing the CO2 level and play a key role in improving the quality of air.

Keywords: carbon sequestration, grasslands, global warming, climate change.

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Authors: Sotiroula Liasidou, Katerina Pericleous, Zanete Garanti,

Abstract:

Traveling for religious and pilgrimage purposes consists of an early-stage motivation for the historical development of tourism. Sacred places have become important attractions for local and foreign visitors, and many countries invest in the development of religious and pilgrimage tourism. Cyprus has a rich tradition as an important place for the establishment and diffusion of the Christian Orthodox Religion (Greek). Being considered the ‘island of Saints’, Cyprus sets strong foundations to be recognised as a spiritual destination of devotion for visitors interested in discovering the roots and the spiritual essence of the Christian Orthodox Religion. The paper elucidates on bringing together the fact of whether tourism in sacred places affects spirituality and religiosity. Thus, the aim is to consider the perceptions of the main religious stakeholders, including monastery abbots, in relation to the development of religious tourism. The aim of the study is fulfilled by incorporating questionnaires targeting the responses of the involved religious key players and stakeholders. The results of the study are indicative and provide an understanding in terms of religious tourism as an important product by interpreting the stance of religious stakeholders. In general, religious leaders support tourism in religious sites and argue that spirituality and holiness can be maintained as long there is a policy that is followed both by religious and tourism policymakers. Undoubtedly, establishing Cyprus as a religious tourism destination would bring many economic and social benefits.

Keywords: religious tourism, pilgrimage, Cyprus, Christian Orthodox Religion, sacred places, monasteries

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Authors: J. S. Dhillon, K. K. Dhaliwal

Abstract:

In almost all the digital filtering applications the digital infinite impulse response (IIR) filters are preferred over finite impulse response (FIR) filters because they provide much better performance, less computational cost and have smaller memory requirements for similar magnitude specifications. However, the digital IIR filters are generally multimodal with respect to the filter coefficients and therefore, reliable methods that can provide global optimal solutions are required. The artificial bee colony (ABC) algorithm is one such recently introduced meta-heuristic optimization algorithm. But in some cases it shows insufficiency while searching the solution space resulting in a weak exchange of information and hence is not able to return better solutions. To overcome this deficiency, the opposition based learning strategy is incorporated in ABC and hence a modified version called oppositional artificial bee colony (OABC) algorithm is proposed in this paper. Duplication of members is avoided during the run which also augments the exploration ability. The developed algorithm is then applied for the design of optimal and stable digital IIR filter structure where design of low-pass (LP) and high-pass (HP) filters is carried out. Fuzzy theory is applied to achieve maximize satisfaction of minimum magnitude error and stability constraints. To check the effectiveness of OABC, the results are compared with some well established filter design techniques and it is observed that in most cases OABC returns better or atleast comparable results.

Keywords: digital infinite impulse response filter, artificial bee colony optimization, opposition based learning, digital filter design, multi-parameter optimization

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Authors: Neslihan Genckal, Reha Gursoy, Vedat Z. Dogan

Abstract:

In this study, dynamic responses of composite plates on elastic foundations subjected to impact and moving loads are investigated. The first order shear deformation (FSDT) theory is used for moderately thick plates. Pasternak-type (two-parameter) elastic foundation is assumed. Elastic foundation effects are integrated into the governing equations. It is assumed that plate is first hit by a mass as an impact type loading then the mass continues to move on the composite plate as a distributed moving loading, which resembles the aircraft landing on airport pavements. Impact and moving loadings are modeled by a mass-spring-damper system with a wheel. The wheel is assumed to be continuously in contact with the plate after impact. The governing partial differential equations of motion for displacements are converted into the ordinary differential equations in the time domain by using Galerkin’s method. Then, these sets of equations are solved by using the Runge-Kutta method. Several parameters such as vertical and horizontal velocities of the aircraft, volume fractions of the steel rebar in the reinforced concrete layer, and the different touchdown locations of the aircraft tire on the runway are considered in the numerical simulation. The results are compared with those of the ABAQUS, which is a commercial finite element code.

Keywords: elastic foundation, impact, moving load, thick plate

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Authors: M. N. Khajavi, S. Nasiri, E. Farokhi, M. R. Bavir

Abstract:

Alcohol fuels are renewable, have low pollution and have high octane number; therefore, they are important as fuel in internal combustion engines. Percentage detection of these alcoholic fuels with gasoline is a complicated, time consuming, and expensive process. Nowadays, these processes are done in equipped laboratories, based on international standards. The aim of this research is to determine percentage detection of different fuels based on vibration analysis of engine block signals. By doing, so considerable saving in time and cost can be achieved. Five different fuels consisted of pure gasoline (G) as base fuel and combination of this fuel with different percent of ethanol and methanol are prepared. For example, volumetric combination of pure gasoline with 10 percent ethanol is called E10. By this convention, we made M10 (10% methanol plus 90% pure gasoline), E30 (30% ethanol plus 70% pure gasoline), and M30 (30% Methanol plus 70% pure gasoline) were prepared. To simulate real working condition for this experiment, the vehicle was mounted on a chassis dynamometer and run under 1900 rpm and 30 KW load. To measure the engine block vibration, a three axis accelerometer was mounted between cylinder 2 and 3. After acquisition of vibration signal, eight time feature of these signals were used as inputs to an Adaptive Neuro Fuzzy Inference System (ANFIS). The designed ANFIS was trained for classifying these five different fuels. The results show suitable classification ability of the designed ANFIS network with 96.3 percent of correct classification.

Keywords: internal combustion engine, vibration signal, fuel composition, classification, ANFIS

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Authors: Khaleed Alsufyyan

Abstract:

This doctoral research has been successfully approved by a specialist upgrade panel, and it presents the proposition that the KSA policy for combating maritime terrorism is inadequate and current governance frameworks, including laws, are insufficiently developed to respond effectively and fairly to maritime terrorism. It will examine the legal system in the KSA in terms of effectiveness fairness, as well as investigate this proposition to determine what factors have contributed to such a deficiency. The main focus of this research will draw upon the policies, laws, and practices of the KSA, as well as UK and international laws and policies, to assess whether it is feasible to apply them in the context of the KSA. This thesis will recommend strategies regarding maritime terrorism to enrich the legal and policy frameworks and address the current and future dynamics of maritime terrorism adequately. To derive suitable improvements, UK policies, laws, and practices will be considered for policy transfer purposes. As for studies focused on the KSA, since the KSA is a Muslim state, it will be important to assess the impact of Islamic Law or Sharia Law subject to the doctrines of fairness and effectiveness to comprehend how the KSA’s legal system operates and determine the boundaries it sets for the response to maritime terrorism. This thesis will propose that more reforms are needed to effectively and fairly deal with maritime terrorism based on the prevailing understanding of Sharia law. The research will address the international perspectives on the problem of maritime terrorism and international cooperation of the KSA regarding maritime terrorism and consider the need for further developments.

Keywords: maritime terrorism, maritime security, combat maritime terrorism in the KSA, protecting maritime transport against terrorism

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