Search results for: adaptive random testing

Authors: Rasool Sadeghi, Sayed Mahdi Faghih Imani, Negar Najafi

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The inherent features of Named Data Networking (NDN) provides a robust solution for Internet of Thing (IoT). Therefore, NDN-IoT has emerged as a combined architecture which exploits the benefits of NDN for interconnecting of the heterogeneous objects in IoT. In NDN-IoT, caching schemes are a key role to improve the network performance. In this paper, we consider the effectiveness of cache replacement schemes in NDN-IoT scenarios. We investigate the impact of replacement schemes on average delay, average hop count, and average interest retransmission when replacement schemes are Least Frequently Used (LFU), Least Recently Used (LRU), First-In-First-Out (FIFO) and Random. The simulation results demonstrate that LFU and LRU present a stable performance when the cache size changes. Moreover, the network performance improves when the number of consumers increases.

Keywords: NDN-IoT, cache replacement, performance, ndnSIM

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Authors: H. A. Erdogan

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Historical masonry arch bridges built centuries ago have fulfilled their function until recent decades. However, from the beginning of 20th century, these bridges have remained inadequate as a result of increasing speed, size and capacity of the means of transport. Although new bridges have been built in many places, masonry bridges located within the city limits still need to be used. When the size and transportation loads of modern vehicles are taken into account, it is apparent that historical masonry arch bridges would be exposed to greater loads than their initial design loads. Because of that, many precautions taken either remain insufficient or damage these bridges. In this study, the history of Debbaglar Bridge, one of the historic bridges located in the city center of Aksaray/Turkey is presented and its existing condition is evaluated. Structural analysis of the bridge under present conditions and loads is explained. Moreover, the retrofit and restoration application prepared considering the analysis data is described.

Keywords: adaptive re-use, Aksaray debbaglar bridge, masonry bridge, reconstruction

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Authors: Gustave Semugaza, Anne Zora Gierth, Tommy Mielke, Marianela Escobar Castillo, Nat Doru C. Lupascu

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The generation of Construction and Demolition Waste (CDW) has globally increased enormously due to the enhanced need in construction, renovation, and demolition of construction structures. Several studies investigated the use of CDW materials in the production of new concrete and indicated the lower mechanical properties of the resulting concrete. Many other researchers considered the possibility of using the Hydrated Cement Powder (HCP) to replace a part of Ordinary Portland Cement (OPC), but only very few investigated the use of Recycled Concrete Powder (RCP) from CDW. The partial replacement of OPC for making new concrete intends to decrease the CO₂ emissions associated with OPC production. However, the RCP and HCP need treatment to produce the new concrete of required mechanical properties. The thermal treatment method has proven to improve HCP properties before their use. Previous research has stated that for using HCP in concrete, the optimum results are achievable by heating HCP between 400°C and 800°C. The optimum heating temperature depends on the type of cement used to make the Hydrated Cement Specimens (HCS), the crushing and heating method of HCP, and the curing method of the Rehydrated Cement Specimens (RCS). This research assessed the quality of recycled materials by using different techniques such as X-ray Diffraction (XRD), Differential Scanning Calorimetry (DSC) and thermogravimetry (TG), Scanning electron Microscopy (SEM), and X-ray Fluorescence (XRF). These recycled materials were thermally pretreated at different temperatures from 200°C to 1000°C. Additionally, the research investigated to what extent the thermally treated recycled cement could partially replace the OPC and if the new concrete produced would achieve the required mechanical properties. The mechanical properties were evaluated on the RCS, obtained by mixing the Dehydrated Cement Powder and Recycled Powder (DCP and DRP) with water (w/c = 0.6 and w/c = 0.45). The research used the compressive testing machine for compressive strength testing, and the three-point bending test was used to assess the flexural strength.

Keywords: hydrated cement powder, dehydrated cement powder, recycled concrete powder, thermal treatment, reactivation, mechanical performance

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Authors: Ditsapelo M. McFarland

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Background: Despite the availability of the Pap smear services in urban areas in Botswana, most women in such areas do not seem to screen regular for prevention of the cervical cancer disease. Reasons for non-use of the available Pap smear services are not well understood. Beliefs about cancer may influence participation in cancer screening in these women. The purpose of this study was to develop an instrument to measure beliefs about cervical cancer and Pap smear screening among Black women in Botswana, and evaluate the psychometric properties of the instrument. Significance: Instruments that are designed to measure beliefs about cervical cancer and screening among black women in Botswana, as well as in the surrounding region, are presently not available. Valid and reliable instruments are needed for exploration of the women’s beliefs about cervical cancer. Conceptual Framework: The Health Belief Model (HBM) provided a conceptual framework for the study. Methodology: The study was done in four phases: Phase 1: item generation: 15 items were generated from literature review and qualitative data for each of four conceptually defined HBM constructs: Perceived susceptibility, severity, benefits, and barriers (Version 1). Phase 2: content validity: Four experts who were advanced practice nurses of African descent and were familiar with the content and the HBM evaluated the content. Experts rated the items on a 4-point Likert scale ranging from: 1=not relevant, 2=somewhat relevant, 3=relevant and 4=very relevant. Fifty-five items were retained for instrument development: perceived susceptibility - 11, severity - 14, benefits - 15 and barriers - 15, all measuring on a 4-point Likert scale ranging from strongly disagree (1) to strongly agree (4). (Version 2). Phase 3: pilot testing: The instrument was pilot tested on a convenient sample of 30 women in Botswana and revised as needed. Phase 4: reliability: the revised instrument (Version 3) was submitted to a larger sample of women in Botswana (n=300) for reliability testing. The sample included women who were Batswana by birth and decent, were aged 30 years and above and could complete an English questionnaire. Data were collected with the assistance of trained research assistants. Major findings: confirmatory factor analysis of the 55 items found that a number of items did not adequately load in a four-factor solution. Items that exhibited reasonable reliability and had low frequency of missing values (n=36) were retained: perceived barriers (14 items), perceived benefits (8 items), perceived severity (4 items), and perceived susceptibility (10 items). confirmatory factor analysis (principle components) for a four factor solution using varimax rotation demonstrated that these four factors explained 43% of the variation in these 36 items. Conclusion: reliability analysis using Cronbach’s Alpha gave generally satisfactory results with values from 0.53 to 0.89.

Keywords: cervical cancer, factor analysis, psychometric evaluation, varimax rotation

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Authors: Ganbat Danaa, Chuluundorj Puntsag

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The steel structures of the Oyu-Tolgoi mining Concentrator plant are corroded during operation, which raises doubts about the continued use of some important structures of the plant, which is one of the problems facing the plant's regular operation. As a part of the main operation of the plant, the bottom part of the pressure vessel, which plays an important role in the reliable operation of the concentrate filter-drying unit, was heavily corroded, so it was necessary to study by engineering calculations, modeling, and simulation using modern advanced engineering programs and methods. The purpose of this research is to investigate whether the corroded part of the pressure vessel can be used normally in the future using advanced engineering software and to predetermine the remaining life of the time of the pressure vessel based on engineering calculations. When the thickness of the bottom part of the pressure vessel was thinned by 0.5mm due to corrosion detected by non-destructive testing, finite element analysis using ANSYS WorkBench software was used to determine the mechanical stress, strain and safety factor in the wall and bottom of the pressure vessel operating under 2.2 MPa working pressure, made conclusions on whether it can be used in the future. According to the recommendations, by using sand-blast cleaning and anti-corrosion paint, the normal, continuous and reliable operation of the Concentrator plant can be ensured, such as ordering new pressure vessels and reducing the installation period. By completing this research work, it will be used as a benchmark for assessing the corrosion condition of steel parts of pressure vessels and other metallic and non-metallic structures operating under severe conditions of corrosion, static and dynamic loads, and other deformed steels to make analysis of the structures and make it possible to evaluate and control the integrity and reliable operation of the structures.

Keywords: corrosion, non-destructive testing, finite element analysis, safety factor, structural reliability

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Authors: Ana L. Martins, Rita S. Saraiva, João C. Ferreira

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Much effort has been placed on the assessment of perceived service quality. Several models can be found in literature, but these are mainly focused on business-to-consumer (B2C) relationships. Literature on how to assess perceived quality in business-to-business (B2B) contexts is scarce both conceptually and in terms of its application. This research aims at filling this gap in literature by applying INDSERV to a case study situation. Under this scope, this research aims at analyzing the adequacy of the proposed assessment tool to other context besides the one where it was developed and by doing so analyzing the perceive quality of the advertisement service provided by a specific television network to its B2B customers. The INDSERV scale was adopted and applied to a sample of 33 clients, via questionnaires adapted to interviews. Data was collected in person or phone. Both quantitative and qualitative data collection was performed. Qualitative data analysis followed content analysis protocol. Quantitative analysis used hypotheses testing. Findings allowed to conclude that the perceived quality of the television service provided by television network is very positive, being the Soft Process Quality the parameter that reveals the highest perceived quality of the service as opposed to Potential Quality. To this end, some comments and suggestions were made by the clients regarding each one of these service quality parameters. Based on the hypotheses testing, it was noticed that only advertisement clients that maintain a connection to the television network from 5 to 10 years do show a significant different perception of the TV advertisement service provided by the company in what the Hard Process Quality parameter is concerned. Through the collected data content analysis, it was possible to obtain the percentage of clients which share the same opinions and suggestions for improvement. Finally, based on one of the four service quality parameter in a B2B context, managerial suggestions were developed aiming at improving the television network advertisement perceived quality service.

Keywords: B2B, case study, INDSERV, perceived service quality

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Authors: M. Bouferhane, A. Boukhebza, L. Hatab

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In this paper, the position control of linear induction motor using fuzzy sliding mode controller design is proposed. First, the indirect field oriented control LIM is derived. Then, a designed sliding mode control system with an integral-operation switching surface is investigated, in which a simple adaptive algorithm is utilized for generalised soft-switching parameter. Finally, a fuzzy sliding mode controller is derived to compensate the uncertainties which occur in the control, in which the fuzzy logic system is used to dynamically control parameter settings of the SMC control law. The effectiveness of the proposed control scheme is verified by numerical simulation. The experimental results of the proposed scheme have presented good performances compared to the conventional sliding mode controller.

Keywords: linear induction motor, vector control, backstepping, fuzzy-sliding mode control

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Authors: Onabajo Paul

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The testing and assessment of physical fitness of school-aged adolescents in Nigeria has been going on for several decades. Originally, these tests strictly focused on identifying health and physical fitness status and comparing the results of adolescents with others. There is a considerable interest in health and performance fitness of adolescents in which results attained are compared with criteria representing positive health rather than simply on score comparisons with others. Despite the fact that physical education program is being studied in secondary schools and physical activities are encouraged, it is observed that regular assessment of students’ fitness level and health status seems to be scarce or not being done in these schools. The purpose of the study was to assess the heath and performance fitness of adolescents in middle-income schools in Lagos State. A total number of 150 students were selected using the simple random sampling technique. Participants were measured on hand grip strength, sit-up, pacer 20 meter shuttle run, standing long jump, weight and height. The data collected were analyzed with descriptive statistics of means, standard deviations, and range and compared with fitness norms. It was concluded that majority 111(74.0%) of the adolescents achieved the healthy fitness zone, 33(22.0%) were very lean, and 6(4.0%) needed improvement according to the normative standard of Body Mass Index test. For muscular strength, majority 78(52.0%) were weak, 66(44.0%) were normal, and 6(4.0%) were strong according to the normative standard of hand-grip strength test. For aerobic capacity fitness, majority 93(62.0%) needed improvement and were at health risk, 36(24.0%) achieved healthy fitness zone, and 21(14.0%) needed improvement according to the normative standard of PACER test. Majority 48(32.0%) of the participants had good hip flexibility, 38(25.3%) had fair status, 27(18.0%) needed improvement, 24(16.0%) had very good hip flexibility status, and 13(8.7%) of the participants had excellent status. Majority 61(40.7%) had average muscular endurance status, 30(20.0%) had poor status, 29(18.3%) had good status, 28(18.7%) had fair muscular endurance status, and 2(1.3%) of the participants had excellent status according to the normative standard of sit-up test. Majority 52(34.7%) had low jump ability fitness, 47(31.3%) had marginal fitness, 31(20.7%) had good fitness, and 20(13.3%) had high performance fitness according to the normative standard of standing long jump test. Based on the findings, it was concluded that majority of the adolescents had better Body Mass Index status, and performed well in both hip flexibility and muscular endurance tests. Whereas majority of the adolescents performed poorly in aerobic capacity test, muscular strength and jump ability test. It was recommended that to enhance wellness, adolescents should be involved in physical activities and recreation lasting 30 minutes three times a week. Schools should engage in fitness program for students on regular basis at both senior and junior classes so as to develop good cardio-respiratory, muscular fitness and improve overall health of the students.

Keywords: adolescents, health-related fitness, performance-related fitness, physical fitness

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Authors: Hend Alrasheed

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Due to their different properties that have led to avoid several limitations of classic client/server systems, there has been a great interest in the development and the improvement of different peer to peer systems. Understanding the properties of complex peer to peer networks is essential for their future improvements. It was shown that the performances of peer to peer protocols are directly related to their underlying topologies. Therefore, multiple efforts have analyzed the topologies of different peer to peer systems. This study presents an overview of major findings of close experimental analyses to different topologies of three unstructured peer to peer systems: BitTorrent, Gnutella, and FreeNet.

Keywords: peer to peer networks, network topology, graph diameter, clustering coefficient, small-world property, random graph, degree distribution

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Authors: Michael Nicolosi, Karola Dillenburger

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The University of California at Los Angeles-Young Autism Project (UCLA-YAP) provides one of the best-known and most researched comprehensive applied behavior analysis-based intervention models for young children on the autism spectrum. This paper reports a systematic literature review of replication studies over more than 30 years. The data show that the relatively high-intensity UCLA-YAP model can be greatly beneficial for children on the autism spectrum, particularly with regard to their cognitive functioning and adaptive behavior. This review concludes that, while more research is always welcome, the impact of the UCLA-YAP model on autism interventions is justified by more than 30 years of outcome evidence.

Keywords: ABA, applied behavior analysis, autism, California at Los Angeles Young Autism project, intervention, Lovaas, UCLA-YAP

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Authors: Navid Kaboudi, Ali Shayanfar

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Introduction: Feeding infants with safe milk from the beginning of their life is an important issue. Drugs which are used by mothers can affect the composition of milk in a way that is not only unsuitable, but also toxic for infants. Consuming permeable drugs during that sensitive period by mother could lead to serious side effects to the infant. Due to the ethical restrictions of drug testing on humans, especially women, during their lactation period, computational approaches based on structural parameters could be useful. The aim of this study is to develop mechanistic models to predict the M/P ratio of drugs during breastfeeding period based on their structural descriptors. Methods: Two hundred and nine different chemicals with their M/P ratio were used in this study. All drugs were categorized into two groups based on their M/P value as Malone classification: 1: Drugs with M/P>1, which are considered as high risk 2: Drugs with M/P>1, which are considered as low risk Thirty eight chemical descriptors were calculated by ACD/labs 6.00 and Data warrior software in order to assess the penetration during breastfeeding period. Later on, four specific models based on the number of hydrogen bond acceptors, polar surface area, total surface area, and number of acidic oxygen were established for the prediction. The mentioned descriptors can predict the penetration with an acceptable accuracy. For the remaining compounds (N= 147, 158, 160, and 174 for models 1 to 4, respectively) of each model binary regression with SPSS 21 was done in order to give us a model to predict the penetration ratio of compounds. Only structural descriptors with p-value<0.1 remained in the final model. Results and discussion: Four different models based on the number of hydrogen bond acceptors, polar surface area, and total surface area were obtained in order to predict the penetration of drugs into human milk during breastfeeding period About 3-4% of milk consists of lipids, and the amount of lipid after parturition increases. Lipid soluble drugs diffuse alongside with fats from plasma to mammary glands. lipophilicity plays a vital role in predicting the penetration class of drugs during lactation period. It was shown in the logistic regression models that compounds with number of hydrogen bond acceptors, PSA and TSA above 5, 90 and 25 respectively, are less permeable to milk because they are less soluble in the amount of fats in milk. The pH of milk is acidic and due to that, basic compounds tend to be concentrated in milk than plasma while acidic compounds may consist lower concentrations in milk than plasma. Conclusion: In this study, we developed four regression-based models to predict the penetration class of drugs during the lactation period. The obtained models can lead to a higher speed in drug development process, saving energy, and costs. Milk/plasma ratio assessment of drugs requires multiple steps of animal testing, which has its own ethical issues. QSAR modeling could help scientist to reduce the amount of animal testing, and our models are also eligible to do that.

Keywords: logistic regression, breastfeeding, descriptors, penetration

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Authors: Osama Elsarrar, Marjorie Darrah, Richard Devin

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This paper discusses the idea of capturing an expert’s knowledge in the form of human understandable rules and then inserting these rules into a dynamic cell structure (DCS) neural network. The DCS is a form of self-organizing map that can be used for many purposes, including classification and prediction. This particular neural network is considered to be a topology preserving network that starts with no pre-structure, but assumes a structure once trained. The DCS has been used in mission and safety-critical applications, including adaptive flight control and health-monitoring in aerial vehicles. The approach is to insert expert knowledge into the DCS before training. Rules are translated into a pre-structure and then training data are presented. This idea has been demonstrated using the well-known Iris data set and it has been shown that inserting the pre-structure results in better accuracy with the same training.

Keywords: neural network, self-organizing map, rule extraction, rule insertion

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Authors: Chang-Hsing Lee, Cheng-Chang Lien, Chin-Chuan Han

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In this paper, an edge-strength guided multiscale retinex (EGMSR) approach will be proposed for color image contrast enhancement. In EGMSR, the pixel-dependent weight associated with each pixel in the single scale retinex output image is computed according to the edge strength around this pixel in order to prevent from over-enhancing the noises contained in the smooth dark/bright regions. Further, by fusing together the enhanced results of EGMSR and adaptive multiscale retinex (AMSR), we can get a natural fused image having high contrast and proper tonal rendition. Experimental results on several low-contrast images have shown that our proposed approach can produce natural and appealing enhanced images.

Keywords: image enhancement, multiscale retinex, image fusion, EGMSR

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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

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Authors: Mohammad Moridzadeh, Mohammad Djavid, Barry Doyle

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An assessment of the long-term stability of a cavern in Chagrin shale excavated by the sequential excavation method was performed during and after construction. During the excavation of the cavern, deformations of rock mass were measured at the surface of excavation and within the rock mass by surface and deep measurement instruments. Rock deformations were measured during construction which appeared to result from the as-built excavation sequence that had potentially disturbed the rock and its behavior. Also some additional time dependent rock deformations were observed during and post excavation. Several opinions have been expressed to explain this time dependent deformation including stress changes induced by excavation, strain softening (or creep) in the beddings with and without clay and creep of the shaley rock under compressive stresses. In order to analyze and replicate rock behavior observed during excavation, including current and post excavation elastic, plastic, and time dependent deformation, Finite Element Analysis (FEA) was performed. The analysis was also intended to estimate long term deformation of the rock mass around the excavation. Rock mass behavior including time dependent deformation was measured by means of rock surface convergence points, MPBXs, extended creep testing on the long anchors, and load history data from load cells attached to several long anchors. Direct creep testing of Chagrin Shale was performed on core samples from the wall of the Pump Room. Results of these measurements were used to calibrate the FEA of the excavation. These analyses incorporate time dependent constitutive modeling for the rock to evaluate the potential long term movement in the roof, walls, and invert of the cavern. The modeling was performed due to the concerns regarding the unanticipated behavior of the rock mass as well as the forecast of long term deformation and stability of rock around the excavation.

Keywords: Cavern, Chagrin shale, creep, finite element.

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Authors: Annapureddy Srikant Reddy, Atthanti Mahendra, Samala Chinni Krishna, N. Neelima

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In our present world, we are generating a lot of data and we, need a specific device to store all these data. Generally, we store data in pen drives, hard drives, etc. Sometimes we may loss the data due to the corruption of devices. To overcome all these issues, we implemented a cloud space for storing the data, and it provides more security to the data. We can access the data with just using the internet from anywhere in the world. We implemented all these with the java using Net beans IDE. Once user uploads the data, he does not have any rights to change the data. Users uploaded files are stored in the cloud with the file name as system time and the directory will be created with some random words. Cloud accepts the data only if the size of the file is less than 2MB.

Keywords: cloud space, AES, FTP, NetBeans IDE

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Authors: Brunno Vasques

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Due to the non-toxic nature and high performance in terms of vacuum specific impulse and density specific impulse, the combination of liquid oxygen and liquid methane have been identified as a promising option for future space vehicle systems. Applications requiring throttling capability include specific missions such as rendezvous, planetary landing and de-orbit as well as weapon systems. One key challenge in throttling liquid rocket engines is maintaining an adequate pressure drop across the injection elements, which is necessary to provide good propellant atomization and mixing as well as system stability. The potential scalability of pintle injectors, their great suitability to throttling and inherent combustion stability characteristics led to investigations using a variety of propellant combinations, including liquid oxygen and hydrogen and fluorine-oxygen and methane. Presented here are the preliminary performance and heat transfer information obtained during hot-fire testing of a pintle injector running on liquid oxygen and liquid methane propellants. The specific injector design selected for this purpose is a multi-configuration building block version with replaceable injection elements, providing flexibility to accommodate hardware modifications with minimum difficulty. On the basis of single point runs and the use of a copper/nickel segmented calorimetric combustion chamber and associated transient temperature measurement, the characteristic velocity efficiency, injector footprint and heat fluxes could be established for the first proposed pintle configuration as a function of injection velocity- and momentum-ratios. A description of the test-bench is presented as well as a discussion of irregularities encountered during testing, such as excessive heat flux into the pintle tip resulting from certain operating conditions.

Keywords: green propellants, hot-fire performance, rocket engine throttling, pintle injector

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Authors: Diego Santibañez Oyarce, Fernanda Bravo Cornejo, Camilo Cerda Sarabia, Belén Díaz Díaz, Esteban Gómez Terán, Hugo Osses Prado, Raúl Caulier-Cisterna, Jorge Vergara-Quezada, Ana Moya-Beltrán

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The advent of high-throughput sequencing technologies has revolutionized genomics, generating vast amounts of genetic data that challenge traditional bioinformatics methods. Machine learning addresses these challenges by leveraging computational power to identify patterns and extract information from large datasets. However, biological sequence data, being symbolic and non-numeric, must be converted into numerical formats for machine learning algorithms to process effectively. So far, some encoding methods, such as one-hot encoding or k-mers, have been explored. This work proposes additional approaches for encoding DNA sequences in order to compare them with existing techniques and determine if they can provide improvements or if current methods offer superior results. Data from the 16S rRNA gene, a universal marker, was used to analyze eight bacterial groups that are significant in the pulmonary environment and have clinical implications. The bacterial genes included in this analysis are Prevotella, Abiotrophia, Acidovorax, Streptococcus, Neisseria, Veillonella, Mycobacterium, and Megasphaera. These data were downloaded from the NCBI database in Genbank file format, followed by a syntactic analysis to selectively extract relevant information from each file. For data encoding, a sequence normalization process was carried out as the first step. From approximately 22,000 initial data points, a subset was generated for testing purposes. Specifically, 55 sequences from each bacterial group met the length criteria, resulting in an initial sample of approximately 440 sequences. The sequences were encoded using different methods, including one-hot encoding, k-mers, Fourier transform, and Wavelet transform. Various machine learning algorithms, such as support vector machines, random forests, and neural networks, were trained to evaluate these encoding methods. The performance of these models was assessed using multiple metrics, including the confusion matrix, ROC curve, and F1 Score, providing a comprehensive evaluation of their classification capabilities. The results show that accuracies between encoding methods vary by up to approximately 15%, with the Fourier transform obtaining the best results for the evaluated machine learning algorithms. These findings, supported by the detailed analysis using the confusion matrix, ROC curve, and F1 Score, provide valuable insights into the effectiveness of different encoding methods and machine learning algorithms for genomic data analysis, potentially improving the accuracy and efficiency of bacterial classification and related genomic studies.

Keywords: DNA encoding, machine learning, Fourier transform, Fourier transformation

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Authors: Danilo López, Edwin Rivas, Fernando Pedraza

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Currently, one of the major challenges in wireless networks is the optimal use of radio spectrum, which is managed inefficiently. One of the solutions to existing problem converges in the use of Cognitive Radio (CR), as an essential parameter so that the use of the available licensed spectrum is possible (by secondary users), well above the usage values that are currently detected; thus allowing the opportunistic use of the channel in the absence of primary users (PU). This article presents the results found when estimating or predicting the future use of a spectral transmission band (from the perspective of the PU) for a chaotic type channel arrival behavior. The time series prediction method (which the PU represents) used is ANFIS (Adaptive Neuro Fuzzy Inference System). The results obtained were compared to those delivered by the RNA (Artificial Neural Network) algorithm. The results show better performance in the characterization (modeling and prediction) with the ANFIS methodology.

Keywords: ANFIS, cognitive radio, prediction primary user, RNA

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Authors: Tazvin Ijaz, Rabia Khan

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The current study was conducted to identify the predictors of burnout among university students. The sample for the study was collected through simple random sampling. The tools to measure burnout and personality characteristics included Indigenous burnout scale and Eysenck personality inventory respectively. Results indicated that neurotic personality traits significantly predicts burnout among university students while extraversion does not lead to burnout. Results also indicated female students experience more burnout than male students. It was also found that family size and birth order did not affected the level of burnout. Results of the study are discussed to explain association between etiological factors and burnout with in Pakistani cultural context.

Keywords: burnout, students, neuroticism, extraversion

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Authors: ZhenlinJu, Christopher P. Vellano, RehanAkbani, Yiling Lu, Gordon B. Mills

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The epithelial–mesenchymal transition (EMT) is a process by which epithelial cells acquire mesenchymal characteristics, such as profound disruption of cell-cell junctions, loss of apical-basolateral polarity, and extensive reorganization of the actin cytoskeleton to induce cell motility and invasion. A hallmark of EMT is its capacity to promote metastasis, which is due in part to activation of several transcription factors and subsequent downregulation of E-cadherin. Unfortunately, current approaches have yet to uncover robust protein marker sets that can classify tumors as possessing strong EMT signatures. In this study, we utilize reverse phase protein array (RPPA) data and consensus clustering methods to successfully classify a subset of cervical squamous cell carcinoma and endocervical adenocarcinoma (CESC) tumors into an EMT protein signaling group (EMT group). The overall survival (OS) of patients in the EMT group is significantly worse than those in the other Hormone and PI3K/AKT signaling groups. In addition to a shrinkage and selection method for linear regression (LASSO), we applied training/test set and Monte Carlo resampling approaches to identify a set of protein markers that predicts the EMT status of CESC tumors. We fit a logistic model to these protein markers and developed a classifier, which was fixed in the training set and validated in the testing set. The classifier robustly predicted the EMT status of the testing set with an area under the curve (AUC) of 0.975 by Receiver Operating Characteristic (ROC) analysis. This method not only identifies a core set of proteins underlying an EMT signature in cervical cancer patients, but also provides a tool to examine protein predictors that drive molecular subtypes in other diseases.

Keywords: consensus clustering, TCGA CESC, Silhouette, Monte Carlo LASSO

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Authors: V. Saccà, A. Sarica, F. Novellino, S. Barone, T. Tallarico, E. Filippelli, A. Granata, P. Valentino, A. Quattrone

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The work aim was to evaluate how well Random Forest (RF) and Support Vector Machine (SVM) algorithms could support the early diagnosis of Multiple Sclerosis (MS) from resting-state functional connectivity data. In particular, we wanted to explore the ability in distinguishing between controls and patients of mean signals extracted from ICA components corresponding to 15 well-known networks. Eighteen patients with early-MS (mean-age 37.42±8.11, 9 females) were recruited according to McDonald and Polman, and matched for demographic variables with 19 healthy controls (mean-age 37.55±14.76, 10 females). MRI was acquired by a 3T scanner with 8-channel head coil: (a)whole-brain T1-weighted; (b)conventional T2-weighted; (c)resting-state functional MRI (rsFMRI), 200 volumes. Estimated total lesion load (ml) and number of lesions were calculated using LST-toolbox from the corrected T1 and FLAIR. All rsFMRIs were pre-processed using tools from the FMRIB's Software Library as follows: (1) discarding of the first 5 volumes to remove T1 equilibrium effects, (2) skull-stripping of images, (3) motion and slice-time correction, (4) denoising with high-pass temporal filter (128s), (5) spatial smoothing with a Gaussian kernel of FWHM 8mm. No statistical significant differences (t-test, p < 0.05) were found between the two groups in the mean Euclidian distance and the mean Euler angle. WM and CSF signal together with 6 motion parameters were regressed out from the time series. We applied an independent component analysis (ICA) with the GIFT-toolbox using the Infomax approach with number of components=21. Fifteen mean components were visually identified by two experts. The resulting z-score maps were thresholded and binarized to extract the mean signal of the 15 networks for each subject. Statistical and machine learning analysis were then conducted on this dataset composed of 37 rows (subjects) and 15 features (mean signal in the network) with R language. The dataset was randomly splitted into training (75%) and test sets and two different classifiers were trained: RF and RBF-SVM. We used the intrinsic feature selection of RF, based on the Gini index, and recursive feature elimination (rfe) for the SVM, to obtain a rank of the most predictive variables. Thus, we built two new classifiers only on the most important features and we evaluated the accuracies (with and without feature selection) on test-set. The classifiers, trained on all the features, showed very poor accuracies on training (RF:58.62%, SVM:65.52%) and test sets (RF:62.5%, SVM:50%). Interestingly, when feature selection by RF and rfe-SVM were performed, the most important variable was the sensori-motor network I in both cases. Indeed, with only this network, RF and SVM classifiers reached an accuracy of 87.5% on test-set. More interestingly, the only misclassified patient resulted to have the lowest value of lesion volume. We showed that, with two different classification algorithms and feature selection approaches, the best discriminant network between controls and early MS, was the sensori-motor I. Similar importance values were obtained for the sensori-motor II, cerebellum and working memory networks. These findings, in according to the early manifestation of motor/sensorial deficits in MS, could represent an encouraging step toward the translation to the clinical diagnosis and prognosis.

Keywords: feature selection, machine learning, multiple sclerosis, random forest, support vector machine

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Authors: Aparna Lal, Ashwin Swaminathan, Teisa Holani

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Background: Clostridium difficile infections (CDIs) have been on the rise globally. In Australia, rates of CDI in all States and Territories have increased significantly since mid-2011. Identifying risk factors for CDI in the community can help inform targeted interventions to reduce infection. Methods: We examine the role of neighbourhood socio-economic status, demography, testing practices and the number of residential aged care facilities on spatial patterns in CDI incidence in the Australian Capital Territory. Data on all tests conducted for CDI were obtained from ACT Pathology by postcode for the period 1st January 2004 through 31 December 2014. Distribution of age groups and the neighbourhood Index of Relative Socio-economic Advantage Disadvantage (IRSAD) were obtained from the Australian Bureau of Statistics 2011 National Census data. A Bayesian spatial conditional autoregressive model was fitted at the postcode level to quantify the relationship between CDI and socio-demographic factors. To identify CDI hotspots, exceedance probabilities were set at a threshold of twice the estimated relative risk. Results: CDI showed a positive spatial association with the number of tests (RR=1.01, 95% CI 1.00, 1.02) and the resident population over 65 years (RR=1.00, 95% CI 1.00, 1.01). The standardized index of relative socio-economic advantage disadvantage (IRSAD) was significantly negatively associated with CDI (RR=0.74, 95% CI 0.56, 0.94). We identified three postcodes with high probability (0.8-1.0) of excess risk. Conclusions: Here, we demonstrate geographic variations in CDI in the ACT with a positive association of CDI with socioeconomic disadvantage and identify areas with a high probability of elevated risk compared with surrounding communities. These findings highlight community-based risk factors for CDI.

Keywords: spatial, socio-demographic, infection, Clostridium difficile

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Authors: Chung Danbi, Linton Oliver, Whang Yoon-Jae

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Supermodularity, or complementarity, is a popular concept in economics which can characterize many objective functions such as utility, social welfare, and production functions. Further, supermodular dominance captures a preference for greater interdependence among inputs of those functions, and it can be applied to examine which input set would produce higher expected utility, social welfare, or production. Therefore, we propose and justify a consistent testing for a useful implication of supermodular dominance. We also conduct Monte Carlo simulations to explore the finite sample performance of our test, with critical values obtained from the recentered bootstrap method, with and without the selective recentering, and the subsampling method. Under various parameter settings, we confirmed that our test has reasonably good size and power performance. Finally, we apply our test to compare the geographic and distant knowledge spillover in terms of their effects on social welfare using the National Bureau of Economic Research (NBER) patent data. We expect localized citing to supermodularly dominate distant citing if the geographic knowledge spillover engenders greater social welfare than distant knowledge spillover. Taking subgroups based on firm and patent characteristics, we found that there is industry-wise and patent subclass-wise difference in the pattern of supermodular dominance between localized and distant citing. We also compare the results from analyzing different time periods to see if the development of Internet and communication technology has changed the pattern of the dominance. In addition, to appropriately deal with the sparse nature of the data, we apply high-dimensional methods to efficiently select relevant data.

Keywords: supermodularity, supermodular dominance, stochastic dominance, Monte Carlo simulation, bootstrap, subsampling

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Authors: Elena Jiménez Lidueña, Noelia Santos Muriel, Patricia López Resa, Noelia Pulido García, Esther Moraleda Sepúlveda

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In recent years, there has been a notable increase in the number of investigations that establish that Down Syndrome and Autism Spectrum Disorder are diagnoses that can coexist together. However, there are also many studies that consider that both diagnoses present neuropsychological, linguistic and adaptive characteristics with a totally different profiles. The objective of this research is to question whether there really can be a profile that encompasses both disorders or if they can be incompatible with each other. To this end, a review of the scientific literature of recent years has been carried out. The results indicate that the two lines collect opposite approaches. On the one hand, there is research that supports the increase in comorbidity between Down Syndrome and Autism Spectrum Disorder and, on the other hand, shows a totally different general development profile between the two. The discussion focuses on discussing both lines of work and on proposing future lines of research in this regard.

Keywords: Down Syndrome, Autism, comorbidity, linguistic

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Authors: Zijian Zhang, Yangyang Dong

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Synchronization is of great importance to ensure the multi-arm robot to complete the task. Therefore, a synchronous controller is designed to coordinate task space motion of the multi-arm in the paper. The position error, the synchronous position error, and the coupling position error are all considered in the controller. Besides, an adaptive control method is used to adjust parameters of the controller to improve the effectiveness of coordinated control performance. Simulation in the Matlab shows the effectiveness of the method. At last, a robot experiment platform with two 7-DOF (Degree of Freedom) robot arms has been established and the synchronous controller simplified to control dual-arm robot has been validated on the experimental set-up. Experiment results show the position error decreased 10% and the corresponding frequency is also greatly improved.

Keywords: synchronous control, space robot, task space control, multi-arm robot

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Authors: Othmane Touri, Sanaa El Filali, El Habib Benlahmar

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Natural Language Processing (NLP) has gained significant attention lately. It has proved its ability to analyze and extract insights from unstructured text data in various languages. It is found that one of the most popular NLP applications is sentiment analysis which aims to identify the sentiment expressed in a piece of text, such as positive, negative, or neutral, in multiple languages. While there are several multilingual NLP models available for sentiment analysis, there is a need to investigate their effectiveness in different contexts and applications. In this study, we aim to investigate the effectiveness of different multilingual NLP models for sentiment analysis on a dataset of online product reviews in multiple languages. The performance of several NLP models, including Google Cloud Natural Language API, Microsoft Azure Cognitive Services, Amazon Comprehend, Stanford CoreNLP, spaCy, and Hugging Face Transformers are being compared. The models based on several metrics, including accuracy, precision, recall, and F1 score, are being evaluated and compared to their performance across different categories of product reviews. In order to run the study, preprocessing of the dataset has been performed by cleaning and tokenizing the text data in multiple languages. Then training and testing each model has been applied using a cross-validation approach where randomly dividing the dataset into training and testing sets and repeating the process multiple times has been used. A grid search approach to optimize the hyperparameters of each model and select the best-performing model for each category of product reviews and language has been applied. The findings of this study provide insights into the effectiveness of different multilingual NLP models for Multilingual Sentiment Analysis and their suitability for different languages and applications. The strengths and limitations of each model were identified, and recommendations for selecting the most performant model based on the specific requirements of a project were provided. This study contributes to the advancement of research methods in multilingual NLP and provides a practical guide for researchers and practitioners in the field.

Keywords: NLP, multilingual, sentiment analysis, texts

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Authors: Temitope Faloye

Abstract:

In today’s changing job market economy, most employers of labor want employees who are employable and possess relevant skills. Graduates need to possess generic skills due to the continually changing nature of the job market, which requires adaptive coping strategies. Most employers of labor complain that graduates are not employable, which is one of the major factors causing a high rate of graduate unemployment in Nigeria. However, the number of unemployed females is higher than that of unemployed males; hence gender difference is linked to the employability of graduates. The human capital theory is considered an appropriate theory for this study. A qualitative approach will be used to provide answers to the research questions. Therefore, the research study aims to investigate the employers’ perspective on female graduate employability in Nigeria.

Keywords: graduate employability, generic skills, graduate unemployment, gender

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Authors: Lalit Kumar, Rashid Al Shidi

Abstract:

Phoenix dactylifera (date palm) is a major crop in many middle-eastern countries, including Oman. The Dubas bug Ommatissus lybicus is the main pest that affects date palm crops. However not all plantations are infested. It is still uncertain why some plantations get infested while others are not. This research investigated whether tree density and the system of planting (random versus systematic) had any relationship with infestation and levels of infestation. Remote Sensing and Geographic Information Systems were used to determine the density of trees (number of trees per unit area) while infestation levels were determined by manual counting of insects on 40 leaflets from two fronds on each tree, with a total of 20-60 trees in each village. The infestation was recorded as the average number of insects per leaflet. For tree density estimation, WorldView-3 scenes, with eight bands and 2m spatial resolution, were used. The Local maxima method, which depends on locating of the pixel of highest brightness inside a certain exploration window, was used to identify the trees in the image and delineating individual trees. This information was then used to determine whether the plantation was random or systematic. The ordinary least square regression (OLS) was used to test the global correlation between tree density and infestation level and the Geographic Weight Regression (GWR) was used to find the local spatial relationship. The accuracy of detecting trees varied from 83–99% in agricultural lands with systematic planting patterns to 50–70% in natural forest areas. Results revealed that the density of the trees in most of the villages was higher than the recommended planting number (120–125 trees/hectare). For infestation correlations, the GWR model showed a good positive significant relationship between infestation and tree density in the spring season with R² = 0.60 and medium positive significant relationship in the autumn season, with R² = 0.30. In contrast, the OLS model results showed a weaker positive significant relationship in the spring season with R² = 0.02, p < 0.05 and insignificant relationship in the autumn season with R² = 0.01, p > 0.05. The results showed a positive correlation between infestation and tree density, which suggests the infestation severity increased as the density of date palm trees increased. The correlation result showed that the density alone was responsible for about 60% of the increase in the infestation. This information can be used by the relevant authorities to better control infestations as well as to manage their pesticide spraying programs.

Keywords: dubas bug, date palm, tree density, infestation levels

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Authors: Madaki Umar Yusuf, Mohd Rizam B. Abubakar

Abstract:

The survival censored data with incomplete covariate data is a common occurrence in many studies in which the outcome is survival time. With model when the missing covariates are categorical, a useful technique for obtaining parameter estimates is the EM by the method of weights. The survival outcome for the class of generalized linear model is applied and this method requires the estimation of the parameters of the distribution of the covariates. In this paper, we propose some clinical trials with ve covariates, four of which have some missing values which clearly show that they were fully censored data.

Keywords: EM algorithm, incomplete categorical covariates, ignorable missing data, missing at random (MAR), Weibull Distribution

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