Search results for: pneumatic artificial muscles

Authors: Tatiana A. Dolenko, Sergey A. Burikov, Alexander O. Efitorov, Sergey A. Dolenko

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In this study, a comparative analysis of the approaches associated with the use of neural network algorithms for effective solution of a complex inverse problem – the problem of identifying and determining the individual concentrations of inorganic salts in multicomponent aqueous solutions by the spectra of Raman scattering of light – is performed. It is shown that application of artificial neural networks provides the average accuracy of determination of concentration of each salt no worse than 0.025 M. The results of comparative analysis of input data compression methods are presented. It is demonstrated that use of uniform aggregation of input features allows decreasing the error of determination of individual concentrations of components by 16-18% on the average.

Keywords: inverse problems, multi-component solutions, neural networks, Raman spectroscopy

Procedia PDF Downloads 528

Authors: Zeinab Jafari A., Ali Sharifnezhad B., Mohammad Razi C., Mohammad Haghpanahi D., Arash Maghsoudi

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Background and Objective: Sports-related rupture of the anterior cruciate ligament (ACL) and following injuries have been associated with various disorders, such as long-lasting changes in muscle activation patterns in athletes, which might last after ACL reconstruction (ACLR). The rupture of the ACL might result in abnormal patterns of movement execution, extending the treatment period and delaying athletes’ return to sports (RTS). As ACL injury is especially prevalent among athletes, the lengthy treatment process and athletes’ absence from sports are of great concern to athletes and coaches. Thus, estimating safe time of RTS is of crucial importance. Therefore, using a deep neural network (DNN) to classify the health levels of ACL in injured athletes, this study aimed to estimate the safe time for athletes to return to competitions. Methods: Ten athletes with ACLR and fourteen healthy controls participated in this study. Three health levels of ACL were defined: healthy, six-month post-ACLR surgery and nine-month post-ACLR surgery. Athletes with ACLR were tested six and nine months after the ACLR surgery. During the course of this study, surface electromyography (sEMG) signals were recorded from five knee muscles, namely Rectus Femoris (RF), Vastus Lateralis (VL), Vastus Medialis (VM), Biceps Femoris (BF), Semitendinosus (ST), during single-leg drop landing (SLDL) and forward hopping (SLFH) tasks. The Pseudo-Wigner-Ville distribution (PWVD) was used to produce three-dimensional (3-D) images of the energy distribution patterns of sEMG signals. Then, these 3-D images were converted to two-dimensional (2-D) images implementing the heat mapping technique, which were then fed to a deep convolutional neural network (DCNN). Results: In this study, we estimated the safe time of RTS by designing a DCNN classifier with an accuracy of 90 %, which could classify ACL into three health levels. Discussion: The findings of this study demonstrate the potential of the DCNN classification technique using sEMG signals in estimating RTS time, which will assist in evaluating the recovery process of ACLR in athletes.

Keywords: anterior cruciate ligament reconstruction, return to sports, surface electromyography, deep convolutional neural network

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Authors: Rauno Pääkkönen, Fabriziomaria Gobba, Leena Korpinen

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The objective of the study is to assess the implementation of LED lighting into forest machine work in the dark. In addition, the paper includes a wide variety of important and relevant safety and health parameters. In modern, computerized work in the cab of forest machines, artificial illumination is a demanding task when performing duties, such as the visual inspections of wood and computer calculations. We interviewed entrepreneurs and gathered the following as the most pertinent themes: (1) safety, (2) practical problems, and (3) work with LED lighting. The most important comments were in regards to the practical problems of LED lighting. We found indications of technical problems in implementing LED lighting, like snow and dirt on the surfaces of lamps that dim the emission of light. Moreover, service work in the dark forest is dangerous and increases the risks of on-site accidents. We also concluded that the amount of blue light to the eyes should be assessed, especially, when the drivers are working in a semi-dark cab.

Keywords: forest machines, health, LED, safety

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Authors: Ayanda P. Deliwe, Storm B. Watson

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The COVID-19 pandemic has disrupted the established practices of higher education institutions (HEIs). Most higher education institutions worldwide had to shift from traditional face-to-face to online learning. The online environment and new online tools are disrupting the way in which higher education is presented. Furthermore, the structures of higher education institutions have been impacted by rapid advancements in information and communication technologies. Emerging technologies should not be viewed in a negative light because, as opposed to the traditional curriculum that worked to create productive and efficient researchers, emerging technologies encourage creativity and innovation. Therefore, using technology together with traditional means will enhance teaching and learning. Emerging technologies in higher education not only change the experience of students, lecturers, and the content, but it is also influencing the attraction and retention of students. Higher education institutions are under immense pressure because not only are they competing locally and nationally, but emerging technologies also expand the competition internationally. Emerging technologies have eliminated border barriers, allowing students to study in the country of their choice regardless of where they are in the world. Higher education institutions are becoming indifferent as technology is finding its way into the lecture room day by day. Academics need to utilise technology at their disposal if they want to get through to their students. Academics are now competing for students' attention with social media platforms such as WhatsApp, Snapchat, Instagram, Facebook, TikTok, and others. This is posing a significant challenge to higher education institutions. It is, therefore, critical to pay attention to emerging technologies in order to see how they can be incorporated into the classroom in order to improve educational quality while remaining relevant in the work industry. This study aims to understand how emerging technologies have been utilised at Nelson Mandela University in presenting teaching and learning activities since April 2020. The primary objective of this study is to analyse how academics are incorporating emerging technologies in their teaching and learning activities. This primary objective was achieved by conducting a literature review on clarifying and conceptualising the emerging technologies being utilised by higher education institutions, reviewing and analysing the use of emerging technologies, and will further be investigated through an empirical analysis of the use of emerging technologies at Nelson Mandela University. Findings from the literature review revealed that emerging technology is impacting several key areas in higher education institutions, such as the attraction and retention of students, enhancement of teaching and learning, increase in global competition, elimination of border barriers, and highlighting the digital divide. The literature review further identified that learning management systems, open educational resources, learning analytics, and artificial intelligence are the most prevalent emerging technologies being used in higher education institutions. The identified emerging technologies will be further analysed through an empirical analysis to identify how they are being utilised at Nelson Mandela University.

Keywords: artificial intelligence, emerging technologies, learning analytics, learner management systems, open educational resources

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Authors: Jekaterina Novikova, Leon Watts

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In the emerging world of human-robot interaction, people and robots will interact socially in real-world situations. This paper presents the results of an experimental study probing the interaction between situational context and emotional body language in robots. 34 people rated video clips of robots performing expressive behaviours in different situational contexts both for emotional expressivity on Valence-Arousal-Dominance dimensions and by selecting a specific emotional term from a list of suggestions. Results showed that a contextual information enhanced a recognition of emotional body language of a robot, although it did not override emotional signals provided by robot expressions. Results are discussed in terms of design guidelines on how an emotional body language of a robot can be used by roboticists developing social robots.

Keywords: social robotics, non-verbal communication, situational context, artificial emotions, body language

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Authors: Sezer Kefeli, Sertaç Arslan

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Supercavitation is a pressure dependent process which gives opportunity to eliminate the wetted surface effects on the underwater vehicle due to the differences of viscosity and velocity effects between liquid (freestream) and gas phase. Cavitation process occurs depending on rapid pressure drop or temperature rising in liquid phase. In this paper, pressure based cavitation is investigated due to the fact that is encountered in the underwater world, generally. Basically, this vapor-filled pressure based cavities are unstable and harmful for any underwater vehicle because these cavities (bubbles or voids) lead to intense shock waves while collapsing. On the other hand, supercavitation is a desired and stabilized phenomena than general pressure based cavitation. Supercavitation phenomena offers the idea of minimizing form drag, and thus supercavitating vehicles are revived. When proper circumstances are set up, which are either increasing the operating speed of the underwater vehicle or decreasing the pressure difference between free stream and artificial pressure, the continuity of the supercavitation is obtainable. There are 2 types of supercavitation to obtain stable and continuous supercavitation, and these are called as natural and artificial supercavitation. In order to generate natural supercavitation, various mechanical structures are discovered, which are called as cavitators. In literature, a lot of cavitator types are studied either experimentally or numerically on a CFD platforms with intent to observe natural supercavitation since the 1900s. In this paper, firstly, experimental results are obtained, and trend lines are generated based on supercavitation parameters in terms of cavitation number (), form drag coefficientC_D, dimensionless cavity diameter (d_m/d_c), and length (L_c/d_c). After that, natural cavitation verification studies are carried out for disk and cone shape cavitators. In addition, supercavitation parameters are numerically analyzed at different operating conditions, and CFD results are fitted into trend lines of experimental results. The aims of this paper are to generate one generally accepted drag coefficient equation for disk and cone cavitators at different cavitator half angle and investigation of the supercavitation parameters with respect to cavitation number. Moreover, 165 CFD analysis are performed at different cavitation numbers on FLUENT version 21R2. Five different cavitator types are modeled on SCDM with respect tocavitator’s half angles. After that, CFD database is generated depending on numerical results, and new trend lines are generated based on supercavitation parameters. These trend lines are compared with experimental results. Finally, the generally accepted drag coefficient equation and equations of supercavitation parameters are generated.

Keywords: cavity envelope, CFD, high speed underwater vehicles, supercavitation, supercavitating flows, supercavitation parameters, drag reduction, viscous force elimination, natural cavitation verification

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Authors: G. F. Okunade, M. O. Lawal, R. E. Uwadiae, D. Portnoy

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The diversity, physico-chemical, biochemical and genomics assessment of Macrofauna species of Ologe and Badagry Lagoons were carried out between August 2016 and July 2018. The concentrations of Fe, Zn, Mn, Cd, Cr, and Pb in water were determined by Atomic Absorption Spectrophotometer (AAS). Particle size distribution was determined with wet-sieving and sedimentation using hydrometer method. Genomics analyses were carried using 25 P. fusca (quadriseriata) and 25 P.fusca from each lagoon due to abundance in both lagoons all through the two years of collection. DNA was isolated from each sample using the Mag-Bind Blood and Tissue DNA HD 96 kit; a method designed to isolate high quality. The biochemical characteristics were analysed in the dominanat species (P.aurita and T. fuscatus) using ELISA kits. Physico-chemical parameters such as pH, total dissolved solids, dissolved oxygen, conductivity and TDS were analysed using APHA standard protocols. The Physico-chemical parameters of the water quality recorded with mean values of 32.46 ± 0.66mg/L and 41.93 ± 0.65 for COD, 27.28 ± 0.97 and 34.82 ± 0.1 mg/L for BOD, 0.04 ± 4.71 mg/L for DO, 6.65 and 6.58 for pH in Ologe and Badagry lagoons with significant variations (p ≤ 0.05) across seasons. The mean and standard deviation of salinity for Ologe and Badagry Lagoons ranged from 0.43 ± 0.30 to 0.27 ± 0.09. A total of 4210 species belonging to a phylum, two classes, four families and a total of 2008 species in Ologe lagoon while a phylum, two classes, 5 families and a total of 2202 species in Badagry lagoon. The percentage composition of the classes at Ologe lagoon had 99% gastropod and 1% bivalve, while Gastropod contributed 98.91% and bivalve 1.09% in Badagry lagoon. Particle size was distributed in 0.002mm to 2.00mm, particle size distribution in Ologe lagoon recorded 0.83% gravels, 97.83% sand, and 1.33% silt particles while Badagry lagoon recorded 7.43% sand, 24.71% silt, and 67.86% clay particles hence, the excessive dredging activities going on in the lagoon. Maximum percentage of sand (100%) was seen in station 6 in Ologe lagoon while the minimum (96%) was found in station 1. P. aurita (Ologe Lagoon) and T. fuscastus (Badagry Lagoon) were the most abundant benthic species in which both contributed 61.05% and 64.35%, respectively. The enzymatic activities of P. aurita observed with mean values of 21.03 mg/dl for AST, 10.33 mg/dl for ALP, 82.16 mg/dl for ALT and 73.06 mg/dl for CHO in Ologe Lagoon While T. fuscatus observed mean values of Badagry Lagoon) recorded mean values 29.76 mg/dl, ALP with 11.69mg/L, ALT with 140.58 mg/dl and CHO with 45.98 mg/dl. There were significant variations (P < 0.05) in AST and CHO levels of activities in the muscles of the species.

Keywords: benthos, biochemical responses, genomics, metals, particle size

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Authors: Huy Q. Tran, Bac V. Nguyen, Choonghyun Kang, Jungwon Huh

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Jumbo container crane is an important part of port structures that needs to be designed properly, even when the port locates in low seismicity zone such as in Korea. In this paper, 30 artificial ground motions derived from the elastic response spectra of Korean Building Code (2005) are used for time history analysis. It is found that the uplift might not occur in this analysis when the crane locates in the low seismic zone. Therefore, a selection of a pinned or a gap element for base supporting has not much effect on the determination of the total base shear. The relationships between the total base shear and peak ground acceleration (PGA) and the relationships between the portal drift and the PGA are proposed in this study.

Keywords: jumbo container crane, portal drift, time history analysis, total base shear

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Authors: Noura Al-Ajmi, Mohammed A. Almulla

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With the increased utilization of technology devices around the world, healthcare and medical diagnosis are critical issues that people worry about these days. Doctors are doing their best to avoid any medical errors while diagnosing diseases and prescribing the wrong medication. Subsequently, artificial intelligence applications that can be installed on mobile devices such as rule-based expert systems facilitate the task of assisting doctors in several ways. Due to their many advantages, the usage of expert systems has increased recently in health sciences. This work presents a backward rule-based expert system that can be used for a headache diagnosis and medication recommendation system. The structure of the system consists of three main modules, namely the input unit, the processing unit, and the output unit.

Keywords: headache diagnosis system, prescription recommender system, expert system, backward rule-based system

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Authors: Saad Al-Baddai, Karema Al-Subari, Elmar Lang, Bernd Ludwig

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Empirical mode decomposition (EMD), a new data-driven of time-series decomposition, has the advantage of supposing that a time series is non-linear or non-stationary, as is implicitly achieved in Fourier decomposition. However, the EMD suffers of mode mixing problem in some cases. The aim of this paper is to present a solution for a common type of signals causing of EMD mode mixing problem, in case a signal suffers of an intermittency. By an artificial example, the solution shows superior performance in terms of cope EMD mode mixing problem comparing with the conventional EMD and Ensemble Empirical Mode decomposition (EEMD). Furthermore, the over-sifting problem is also completely avoided; and computation load is reduced roughly six times compared with EEMD, an ensemble number of 50.

Keywords: empirical mode decomposition (EMD), mode mixing, sifting process, over-sifting

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Authors: N. Belas, O. Belaribi, S. Aggoun, K. Bendani, N. Bouhamou, A. Mebrouki

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This experimental work is a part of a long research on the valorization of the dam dredged sediments issued from Fergoug Dam (Mascara-West Algeria). These sediments have to be subjected to thermal treatment to become reactive with the cement and thus to obtain an artificial pozzolana. It is therefore a question of developing the calcined mud as substitutable material in part to the cement used in the composition of self compacting concrete. The objective of the present work is to highlight its influence on the behavior of self compacting concrete compared to that of the natural pozzolana and this, in fresh and hardened states. The study is being conducted on three SCC, the first using 20% in volume of natural pozzolana, the second with 20 % of calcined mud and the third for the sake of comparison is made with cement only. The first results showed the possibility of obtaining SCC with calcined mud complying with the AFGC recommendations having a good mechanical behavior which makes interesting its development as construction materials.

Keywords: dam, fresh state, hardened state mud, sediments, self compacting concrete, valorization

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Authors: Maria Lucia G. Lourenco, Keylla H. N. P. Pereira, Viviane Y. Hibaru, Fabiana F. Souza, Joao C. P. Ferreira, Simone B. Chiacchio, Luiz H. A. Machado

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Congenital malformations are organ defects due to genetic or teratogenic causes, which can lead to high mortality in dog litters. This study assessed and described the congenital malformations in newborn dogs. The study included litters attend in the São Paulo State University (UNESP) Veterinary Hospital, Botucatu, Sao Paulo, Brazil. One hundred seventy-eight litters and 803 newborns were evaluated. The occurrence of litters with malformations was 24.7%, and of newborns was 6.7%. Twenty-seven different malformations were registered: anasarca, anal atresia, cleft lip, cleft palate, duplicated right ribcage, equinovarus, exencephaly, gastroschisis, hydrocephaly, lissencephaly, macroglossia, microphthalmia, mitral valve dysplasia, omphalocele, eyelid agenesis, persistent urachus, polydactyly, pulmonary hypoplasia, pulmonary valve stenosis, rectovaginal fistula, agenesis of abdominal muscles, rib hypoplasia, scoliosis, segmental aplasia of the intestines, tricuspid valve dysplasia, unilateral kidney agenesis, and vaginal atresia. 68.7% of newborns died as a result of malformations. The pure breeds with the highest chances of manifesting malformations in contrast with mixed breeds were French Bulldog, Pug, English Bulldog, Rottweiler, German Spitz, Pinscher, Pitbull, Yorkshire Terrier, and Shih-Tzu. Significant values (P<0.05) occurred in races French Bulldogs and Pugs. The causes of congenital disabilities are possibly related to hereditary genetic factors considering that the highest incidence of malformations was observed among purebreds. There as one case of exposure to a teratogenic agent, but no other mothers were exposed to such agents during pregnancy. Two cases of consanguineal breeding between siblings were reported. The mortality rate was high. Genetic breeding programs for reproduction, avoiding consanguineous mating, care in choosing parents, and avoiding maternal exposure to teratogenic agents are of utmost importance in reducing dog malformations and consequent mortality.

Keywords: congenital defects, teratogenesis, canine neonatology, newborn puppy

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Authors: Ismene Ithai Bras-Ruiz

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Learning Analytics (LA) is employed by universities not only as a tool but as a specialized ground to enhance students and professors. However, not all the academic programs apply LA with the same goal and use the same tools. In fact, LA is formed by five main fields of study (academic analytics, action research, educational data mining, recommender systems, and personalized systems). These fields can help not just to inform academic authorities about the situation of the program, but also can detect risk students, professors with needs, or general problems. The highest level applies Artificial Intelligence techniques to support learning practices. LA has adopted different techniques: statistics, ethnography, data visualization, machine learning, natural language process, and data mining. Is expected that any academic program decided what field wants to utilize on the basis of his academic interest but also his capacities related to professors, administrators, systems, logistics, data analyst, and the academic goals. The Open and Distance University System (SUAYED in Spanish) of the University National Autonomous of Mexico (UNAM), has been working for forty years as an alternative to traditional programs; one of their main supports has been the employ of new information and communications technologies (ICT). Today, UNAM has one of the largest network higher education programs, twenty-six academic programs in different faculties. This situation means that every faculty works with heterogeneous populations and academic problems. In this sense, every program has developed its own Learning Analytic techniques to improve academic issues. In this context, an investigation was carried out to know the situation of the application of LA in all the academic programs in the different faculties. The premise of the study it was that not all the faculties have utilized advanced LA techniques and it is probable that they do not know what field of study is closer to their program goals. In consequence, not all the programs know about LA but, this does not mean they do not work with LA in a veiled or, less clear sense. It is very important to know the grade of knowledge about LA for two reasons: 1) This allows to appreciate the work of the administration to improve the quality of the teaching and, 2) if it is possible to improve others LA techniques. For this purpose, it was designed three instruments to determinate the experience and knowledge in LA. These were applied to ten faculty coordinators and his personnel; thirty members were consulted (academic secretary, systems manager, or data analyst, and coordinator of the program). The final report allowed to understand that almost all the programs work with basic statistics tools and techniques, this helps the administration only to know what is happening inside de academic program, but they are not ready to move up to the next level, this means applying Artificial Intelligence or Recommender Systems to reach a personalized learning system. This situation is not related to the knowledge of LA, but the clarity of the long-term goals.

Keywords: academic improvements, analytical techniques, learning analytics, personnel expertise

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Authors: Sara Fayez Fawzy Mikhael

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Inclusive education services for students with autism are still developing in Thailand. Although many more children with intellectual disabilities have been attending school since the Thai government enacted the Education for Persons with Disabilities Act in 2008, facilities for students with disabilities and their families are generally inadequate. This comprehensive study used the Attitudes and Preparedness for Teaching Students with Autism Scale (APTSAS) to examine the attitudes and preparedness of 110, elementary teachers in teaching students with autism in the general education setting. Descriptive statistical analyzes showed that the most important factor in the formation of a negative image of teachers with autism is student attitudes. Most teachers also stated that their pre-service training did not prepare them to meet the needs of children with special needs who cannot speak. The study is important and provides directions for improving non-formal teacher education in Thailand.

Keywords: attitude, autism, teachers, thailandsports activates, movement skills, motor skills

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Authors: Ahmed I. Alutabi, Naghmeh Dezhabad, Sudhakar Ganti

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Data centers have been growing in size and dema nd continuously in the last two decades. Planning for the deployment of resources has been shallow and always resorted to over-provisioning. Data center operators try to maximize the availability of their services by allocating multiple of the needed resources. One resource that has been wasted, with little thought, has been energy. In recent years, programmable resource allocation has paved the way to allow for more efficient and robust data centers. In this work, we examine the predictability of resource usage in a data center environment. We use a number of models that cover a wide spectrum of machine learning categories. Then we establish a framework to guarantee the client service level agreement (SLA). Our results show that using prediction can cut energy loss by up to 55%.

Keywords: machine learning, artificial intelligence, prediction, data center, resource allocation, green computing

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Authors: Tahar Abid, Haoues Ghouss, Abdelhamid Boubertakh

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This present work is focused on the hardening precipitation in two AlMgSi(Cu) automotive body sheets. The effect of pre-aging, aging treatment and 0.10 wt % copper addition on the hardening response was investigated using scanning calorimetry (DSC), transmission electron microscopy (TEM), and Vickers microhardness measurements (Hv). The results reveal the apparition of α-AlFeSi, α-AlFe(Mn)Si type precipitates frequently present and witch remain stable at high temperature in Al-Mg-Si alloys. Indeed, the hardening response in both sheets is certainly due to the predominance of very fine typical phases β' and β'' as rods and needles developed during aging with and without pre-aging. The effect of pre ageing just after homogenization and quenching is to correct the undesirable effect of aging at ambient temperature by making faster alloy hardening during artificial aging.The addition of 0.10 wt % copper has allowed to refine and to enhance the precipitation hardening after quenching.

Keywords: AlMgSi alloys, precipitation, hardening, activation energy

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Authors: Claes Gunnarsson, Karin Fröding, Nina Hasche

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Innovation is a necessity for the evolution of societies and it is also a driving force in human life that leverages value creation among cross-sector participants in various network arrangements. Social innovations can be characterized as the creation and implementation of a new solution to a social problem, which is more effective and sustainable than existing solutions in terms of improvement of society’s conditions and in particular social inclusion processes. However, barriers exist which may restrict the potential of social innovations to live up to its promise as a societal welfare promoting driving force. The literature points at difficulties in tackling social problems primarily related to problem complexity, access to networks, and lack of financial muscles. Further research is warranted at detailed at detail clarification of these barriers, also connected to recognition of the interplay between institutional logics on the development of cross-sector collaborations in networks and the organizing processes to achieve innovation barrier break-through. There is also a need to further elaborate how obstacles that spur a difference between the actual and desired state of innovative value creating service systems can be overcome. The purpose of this paper is to illustrate barriers to social innovations, based on qualitative content analysis of 36 dialogue-based seminars (i.e. research circles) with nine Swedish focus groups including more than 90 individuals representing civil society organizations, private business, municipal offices, and politicians; and analyze patterns that reveal constituents of barriers to social innovations. The paper draws on central aspects of innovation barriers as discussed in the literature and analyze barriers basically related to internal/external and tangible/intangible characteristics. The findings of this study are that existing institutional structures highly influence the transformative potential of social innovations, as well as networking conditions in terms of building a competence-propelled strategy, which serves as an offspring for overcoming barriers of competence extension. Both theoretical and practical knowledge will contribute to how policy-makers and SI-practitioners can facilitate and support social innovation processes to be contextually adapted and implemented across areas and sectors.

Keywords: barriers, research circles, social innovation, service systems

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Authors: André Smith, Amr Abdel-Dayem

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The stereo matching problem, while having been present for several decades, continues to be an active area of research. The goal of this research is to find correspondences between elements found in a set of stereoscopic images. With these pairings, it is possible to infer the distance of objects within a scene, relative to the observer. Advancements in this field have led to experimentations with various techniques, from graph-cut energy minimization to artificial neural networks. At the basis of these techniques is a cost function, which is used to evaluate the likelihood of a particular match between points in each image. While at its core, the cost is based on comparing the image pixel data; there is a general lack of consistency as to what image data representation to use. This paper presents an experimental analysis to compare the effectiveness of more common image data representations. The goal is to determine the effectiveness of these data representations to reduce the cost for the correct correspondence relative to other possible matches.

Keywords: colour data, local stereo matching, stereo correspondence, disparity map

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Authors: Vijay Kr. Yadav, Nilam Rathi

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Accurate prediction of glucose level for diabetes mellitus is required to avoid affecting the functioning of major organs of human body. This study describes the fundamental assumptions and two different methodologies of the Blood glucose prediction. First is based on the back-propagation algorithm of Artificial Neural Network (ANN), and second is based on the Neuro-Fuzzy technique, called Fuzzy Inference System (FIS). Errors between proposed methods further discussed through various statistical methods such as mean square error (MSE), normalised mean absolute error (NMAE). The main objective of present study is to develop mathematical model for blood glucose prediction before 12 hours advanced using data set of three patients for 60 days. The comparative studies of the accuracy with other existing models are also made with same data set.

Keywords: back-propagation, diabetes mellitus, fuzzy inference system, neuro-fuzzy

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Authors: Saeid Jalilzadeh

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PID controllers are widely used to control the industrial plants because of their robustness and simple structures. Tuning of the controller's parameters to get a desired response is difficult and time consuming. With the development of computer technology and artificial intelligence in automatic control field, all kinds of parameters tuning methods of PID controller have emerged in endlessly, which bring much energy for the study of PID controller, but many advanced tuning methods behave not so perfect as to be expected. Honey Bee algorithm (HBA) and genetic algorithm (GA) are extensively used for real parameter optimization in diverse fields of study. This paper describes an application of HBA and GA to the problem of designing a PID controller whose parameters comprise proportionality constant, integral constant and derivative constant. Presence of three parameters to optimize makes the task of designing a PID controller more challenging than conventional P, PI, and PD controllers design. The suitability of the proposed approach has been demonstrated through computer simulation using MATLAB/SIMULINK.

Keywords: controller, GA, optimization, PID, PSO

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Authors: Muhammad Farooq, Ahtasham Gul

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To meet the desired standard, it is important to monitor and analyze different engineering processes to get desired output. The bivariate distributions got a lot of attention in recent years to describe the randomness of natural as well as artificial mechanisms. In this article, a bivariate model is constructed using two independent models developed by the nesting approach to study the effect of each component on reliability for better understanding. Further, the Bayes analysis of system availability is studied by considering prior parametric variations in the failure time and repair time distributions. Basic statistical characteristics of marginal distribution, like mean median and quantile function, are discussed. We use inverse Gamma prior to study its frequentist properties by conducting Monte Carlo Markov Chain (MCMC) sampling scheme.

Keywords: reliability, system availability Weibull, inverse Lomax, Monte Carlo Markov Chain, Bayesian

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Authors: Tarek Aboueldahab, Hanan Farag

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Cost minimization of Multiple Vehicle Routing Problem becomes a critical issue in the field of transportation because it is NP-hard optimization problem and the search space is complex. Many researches use the hybridization of artificial intelligence (AI) models to solve this problem; however, it can not guarantee to reach the best solution due to the difficulty of searching the whole search space. To overcome this problem, we introduce the hybrid model of Discrete Particle Swarm Optimization (DPSO) with a passive congregation which enable searching the whole search space to compromise between both local and global search. The practical experiment shows that our model obviously outperforms other hybrid models in cost minimization.

Keywords: cost minimization, multi-vehicle routing problem, passive congregation, discrete swarm, passive congregation

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Authors: Masoud Madadelahi, Amir Shamloo, Seyedeh Sara Salehi

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Previously, some materials like solid metals and their alloys have been used as implants in human’s body. In order to amend fixation of these artificial hard human tissues, some porous structures have been introduced. In this way, tissues in vicinity of the porous structure can be attached more easily to the inserted implant. In particular, the porous bone scaffolds are useful since they can deliver important biomolecules like growth factors and proteins. This study focuses on the properties of the degradable porous hard tissues using a three-dimensional numerical Finite Element Method (FEM). The most important studied properties of these structures are diffusivity flux and concentration of different species like glucose, oxygen, and lactate. The process of cells migration into the scaffold is considered as a diffusion process, and related parameters are studied for different values of production/consumption rates.

Keywords: bone scaffolds, diffusivity, numerical simulation, tissue engineering

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Authors: Mariam Bibi, Rubab Mehboob, Mehreen Sirshar

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Satisfying the customer requirements is the ultimate goal of producing or developing any product. The quality of the product is decided on the bases of the level of customer satisfaction. There are different techniques which have been reported during the survey which enhance the quality of the product through software defect prediction and by locating the missing software requirements. Some mining techniques were proposed to assess the individual performance indicators in collaborative environment to reduce errors at individual level. The basic intention is to produce a product with zero or few defects thereby producing a best product quality wise. In the analysis of survey the techniques like Genetic algorithm, artificial neural network, classification and clustering techniques and decision tree are studied. After analysis it has been discovered that these techniques contributed much to the improvement and enhancement of the quality of the product.

Keywords: data mining, defect prediction, missing requirements, software quality

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Authors: Abdelghani Qadem, Zohair Qadem, Mohamed Lasri

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At the level of the Moroccan Sais Plateau, groundwater constitutes strategic natural resources for agricultural, industrial, and domestic use. Today, due to climate change and population growth, the pressure on groundwater has increased considerably. This contribution aims to delineate and map potential areas for groundwater storage in the area in question using GIS and remote sensing. The methodology adopted is based on the identification of the thematic layers used to assess the potential recharge of the aquifer. The mapping of potential areas for groundwater storage is developed through the method of modeling and weighted overlay using the spatial analysis tool on the Geographic Information System. The results obtained can be used for the planning of future artificial recharge projects in the study area in order to ensure the good sustainable use of this underground gift.

Keywords: Morocco, climate change, groundwater, mapping, recharge

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Authors: Hassan Masoumi, Ahad Salimi, Nazanin Barhemmat, Babak Gholami

Abstract:

Artificial neural networks have gained a lot of interest as empirical models for their powerful representational capacity, multi input and output mapping characteristics. In fact, most feed-forward networks with nonlinear nodal functions have been proved to be universal approximates. In this paper, we propose a new supervised method for color image classification based on self organizing feature maps (SOFM). This algorithm is based on competitive learning. The method partitions the input space using self-organizing feature maps to introduce the concept of local neighborhoods. Our image classification system entered into RGB image. Experiments with simulated data showed that separability of classes increased when increasing training time. In additional, the result shows proposed algorithms are effective for color image classification.

Keywords: classification, SOFM algorithm, neural network, neighborhood, RGB image

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Authors: Michael Saad Thabet Azrek

Abstract:

Experiential advertising method is an unforgettable revel that remains deeply anchored within the customer's memory. Furthermore, client pleasure is defined as the emotional reaction to the stories provided that relate to precise products or services bought. Consequently, experiential advertising sports can influence the extent of consumer pleasure and loyalty. In this context, they have a look at pursuits to observe the connection between experiential advertising, purchaser satisfaction and loyalty to splendor merchandise in Konya. The outcomes of this examination confirmed that experiential marketing is an important indicator of consumer pride and loyalty, and that experiential advertising and marketing have a large positive impact on patron satisfaction and loyalty.

Keywords: sponsorship, marketing communication theories, marketing communication tools internet, marketing, tourism, tourism management corporate responsibility, employee organizational performance, internal marketing, internal customer experiential marketing, customer satisfaction, customer loyalty, social sciences.

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Authors: Shenouda Farag Aziz Ibrahim

Abstract:

The relationship between terrorism and human rights has become an important issue in the fight against terrorism worldwide. This is based on the fact that terrorism and human rights are closely linked, so that when the former begins, the latter suffers. This direct link was recognized in the Vienna Declaration and Program of Action adopted by the International Conference on Human Rights held in Vienna on 25 June 1993, which recognized that terrorist acts aim to violate human rights in all their forms and manifestations. . Therefore, terrorism represents an attack on fundamental human rights. For this purpose, the first part of this article focuses on the relationship between terrorism and human rights and aims to show the relationship between these two concepts. In the second part, the concept of cyber threat and its manifestations are discussed. An analysis of the fight against terrorism in the context of human rights was also made..

Keywords: sustainable development, human rights, the right to development, the human rights-based approach to development, environmental rights, economic development, social sustainability human rights protection, human rights violations, workers’ rights, justice, security.

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Authors: Boudemagh Naime

Abstract:

Automatic radar detection requires some methods of adapting to variations in the background clutter in order to control their false alarm rate. The problem becomes more complicated in non-Gaussian environment. In fact, the conventional approach in real time applications requires a complex statistical modeling and much computational operations. To overcome these constraints, we propose another approach based on artificial neural network (ANN-CMLD-CFAR) using a Back Propagation (BP) training algorithm. The considered environment follows a log-normal distribution in the presence of multiple Rayleigh-targets. To evaluate the performances of the considered detector, several situations, such as scale parameter and the number of interferes targets, have been investigated. The simulation results show that the ANN-CMLD-CFAR processor outperforms the conventional statistical one.

Keywords: radat detection, ANN-CMLD-CFAR, log-normal clutter, statistical modelling

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Authors: Ei Ei Hlaing, Narissara Lailerd, Sittiruk Roytrakul, Pichapat Piamrojanaphat

Abstract:

Type 2 diabetes is one of the most common metabolic diseases all over the world. The pathogenesis of type 2 diabetes is not the only dysfunction of pancreatic beta cells but also insulin resistance in muscle, liver and adipose tissue. High levels of circulating free fatty acids, an increased lipid content of muscle cells, impaired insulin-mediated glucose uptake and diminished mitochondrial functioning are pathophysiological hallmarks of diabetic skeletal muscles. Purple rice (Oryza sativa L. indica) has been shown to have antidiabetic effects. However, the underlying mechanism(s) of antidiabetic activity of purple rice is still unraveled. In this research, to explore in-depth cellular mechanism(s), proteomic profile of purple rice supplemented type 2 diabetic rats’ skeletal muscle were analyzed contract with non-supplemented rats. Diabetic rats were induced high-fat diet combined with streptozotocin injection. By using one- dimensional gel electrophoresis (1-DE) and LC-MS/MS quantitative proteomic method, we analyzed proteomic profiles in skeletal muscle of normal rats, normal rats with purple rice supplementation, type 2 diabetic rats, and type 2 diabetic rats with purple rice supplementation. Total 2676 polypeptide expressions were identified. Among them, 24 peptides were only expressed in type 2 diabetic rats, and 24 peptides were unique peptides in type 2 diabetic rats with purple rice supplementation. Acetyl CoA carboxylase 1 (ACACA) found as unique protein in type 2 diabetic rats which is the major enzyme in lipid synthesis and metabolism. Interestingly, DNA damage response protein, heterogeneous nuclear ribonucleoprotein K [Mus musculus] (Hnrnpk), was upregulated in type 2 diabetic rats’ skeletal muscle. Meanwhile, unique proteins of type 2 diabetic rats with purple rice supplementation (bone morphogenetic 7 protein preproprotein, BMP7; and forkhead box protein NX4, Foxn4) involved with muscle cells growth through the regulation of TGF-β/Smad signaling network. Moreover, BMP7 may effect on insulin signaling through the downstream signaling of protein kinase B (Akt) which acts in protein synthesis, glucose uptake, and glycogen synthesis. In conclusion, our study supports that type 2 diabetes impairs muscular lipid metabolism. In addition, purple rice might recover the muscle cells growth and insulin signaling.

Keywords: proteomics, purple rice bran, skeletal muscle, type 2 diabetic rats

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