Search results for: statistical techniques

Authors: Ho-Won Jung, Donghun Lee, Hyung-Jin Kim

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Predictive analytics (data analysis) uses a subset of measurements (the features, predictor, or independent variable) to predict another measurement (the outcome, target, or dependent variable) on a single person or unit. It applies empirical methods in statistics, operations research, and machine learning to predict the future, or otherwise unknown events or outcome on a single or person or unit, based on patterns in data. Most analyses of metabolic syndrome are not predictive analytics but statistical explanatory studies that build a proposed model (theory building) and then validate metabolic syndrome predictors hypothesized (theory testing). A proposed theoretical model forms with causal hypotheses that specify how and why certain empirical phenomena occur. Predictive analytics and explanatory modeling have their own territories in analysis. However, predictive analytics can perform vital roles in explanatory studies, i.e., scientific activities such as theory building, theory testing, and relevance assessment. In the context, this study is to demonstrate how to use our predictive analytics to support theory building (i.e., hypothesis generation). For the purpose, this study utilized a big data predictive analytics platform TM based on a co-occurrence graph. The co-occurrence graph is depicted with nodes (e.g., items in a basket) and arcs (direct connections between two nodes), where items in a basket are fully connected. A cluster is a collection of fully connected items, where the specific group of items has co-occurred in several rows in a data set. Clusters can be ranked using importance metrics, such as node size (number of items), frequency, surprise (observed frequency vs. expected), among others. The size of a graph can be represented by the numbers of nodes and arcs. Since the size of a co-occurrence graph does not depend directly on the number of observations (transactions), huge amounts of transactions can be represented and processed efficiently. For a demonstration, a total of 13,254 metabolic syndrome training data is plugged into the analytics platform to generate rules (potential hypotheses). Each observation includes 31 predictors, for example, associated with sociodemographic, habits, and activities. Some are intentionally included to get predictive analytics insights on variable selection such as cancer examination, house type, and vaccination. The platform automatically generates plausible hypotheses (rules) without statistical modeling. Then the rules are validated with an external testing dataset including 4,090 observations. Results as a kind of inductive reasoning show potential hypotheses extracted as a set of association rules. Most statistical models generate just one estimated equation. On the other hand, a set of rules (many estimated equations from a statistical perspective) in this study may imply heterogeneity in a population (i.e., different subpopulations with unique features are aggregated). Next step of theory development, i.e., theory testing, statistically tests whether a proposed theoretical model is a plausible explanation of a phenomenon interested in. If hypotheses generated are tested statistically with several thousand observations, most of the variables will become significant as the p-values approach zero. Thus, theory validation needs statistical methods utilizing a part of observations such as bootstrap resampling with an appropriate sample size.

Keywords: explanatory modeling, metabolic syndrome, predictive analytics, theory building

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Authors: Fina O. Faithpraise, Effiong O. Obisung, Azele E. Peter, Chris R. Chatwin

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This study investigates and identifies consumer attitude as a major influence that results in non-technical losses in the Nigerian electricity supply sector. This discovery is revealed by the combination of quantitative and qualitative research to complete a survey. The dataset employed is a simple random sampling of households using electricity (public power supply), and the number of units chosen is based on statistical power analysis. The units were subdivided into two categories (household with and without electrical meters). The hypothesis formulated was tested and analyzed using a chi-square statistical method. The results obtained shows that the critical value for the household with electrical prepared meter (EPM) was (9.488 < 427.4) and those without electrical prepared meter (EPMn) was (9.488 < 436.1) with a p-value of 0.01%. The analysis demonstrated so far established the real-time position, which shows that the wrong attitude towards handling the electricity supplied (not turning off light bulbs and electrical appliances when not in use within the rooms and outdoors within 12 hours of the day) characterized the non-technical losses in the power sector. Therefore the adoption of efficient lighting attitudes in individual households as recommended by the researcher is greatly encouraged. The results from this study should serve as a model for energy efficiency and use for the improvement of electricity consumption as well as a stable economy.

Keywords: attitudinal change, household, non-technical losses, prepared meter

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Authors: Huseyin Kavas

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Oleic acid (OA) capped superparamagnetic iron oxide nanoparticles (SPION) were synthesized by a thermal decomposition method. The composition of nanoparticles was confirmed by X-ray powder diffraction, and the morphology of particles was investigated by Atomic Force Microscopy (AFM), Scanning Electron Microscopy (SEM), and Transmission electron microscopy (TEM). The crystalline and particle size distribution of SPIONS capped with OA were investigated with a mean size of 6.99 nm and 8.9 nm, respectively. It was found that SPIONS have superparamagnetic characteristics with a saturation magnetization value of 64 emu/g. The thin film form of self-assembled SPIONS was fabricated by coating techniques of spin coating and dip coating. SQUID-VSM magnetometer and FMR techniques were performed in order to evaluate the magnetic properties of thin films, especially the existence of magnetic anisotropy. The thin films with magnetic anisotropy were obtained by self-assembled monolayers of SPION.

Keywords: magnetic materials, nanostructures, self-assembly, FMR

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Authors: Mehdi Modabberifar, Milad Roodi, Ehsan Souri

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In recent years, honeycomb fiber reinforced plastic (FRP) sandwich panels have been increasingly used in various industries. Low weight, low price, and high mechanical strength are the benefits of these structures. However, their mechanical properties and behavior have not been fully explored. The objective of this study is to conduct a combined numerical-statistical investigation of honeycomb FRP sandwich beams subject to torsion load. In this paper, the effect of geometric parameters of the sandwich panel on the maximum shear strain in both face and core and angle of torsion in a honeycomb FRP sandwich structures in torsion is investigated. The effect of Parameters including core thickness, face skin thickness, cell shape, cell size, and cell thickness on mechanical behavior of the structure were numerically investigated. Main effects of factors were considered in this paper and regression equations were derived. Taguchi method was employed as experimental design and an optimum parameter combination for the maximum structure stiffness has been obtained. The results showed that cell size and face skin thickness have the most significant impacts on torsion angle, maximum shear strain in face and core.

Keywords: finite element, honeycomb FRP sandwich panel, torsion, civil engineering

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Authors: Jannika Dombrowski, Sabine Ambros, Ulrich Kulozik

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Several drying techniques are used to preserve sensitive substances such as probiotic lactic acid bacteria. With the aim to better understand differences between these processes, this work gives new insights into structural variations resulting from different preservation methods and their impact on product quality and storage stability. Industrially established methods (freeze drying, spray drying) were compared to upcoming vacuum, microwave-freeze, and microwave-vacuum drying. For freeze and microwave-freeze dried samples, survival and activity maintained 100%, whereas vacuum and microwave-vacuum dried cultures achieved 30-40% survival. Spray drying yielded in lowest viability. The results are directly related to temperature and oxygen content during drying. Interestingly, most storage stable products resulted from vacuum and microwave-vacuum drying due to denser product structures as determined by helium pycnometry and SEM images. Further, lower water adsorption velocities were responsible for lower inactivation rates. Concluding, resulting product structures as well as survival rates and storage stability mainly depend on the type of water removal instead of energy input. Microwave energy compared to conductive heating did not lead to significant differences regarding the examined factors. Correlations could be proven for three investigated microbial strains. The presentation will be completed by an overview on the energy efficiency of the presented methods.

Keywords: drying techniques, energy efficiency, lactic acid bacteria, probiotics, survival rates, structure characterization

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Authors: Yinan Cao, Francine Herrmann

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Adversary emulation is a very effective concrete way to evaluate the defense of an information system or network. It is about building an emulator, which depending on the vulnerability of a target system, will allow to detect and execute a set of identified attacks. However, emulating an adversary is very costly in terms of time and resources. Verifying the information of each technique and building up the countermeasures in the middle of the test is also needed to be accomplished manually. In this article, a synthesis of previous MITRE research on the creation of the ATT&CK matrix will be as the knowledge base of the known techniques and a well-designed adversary emulation software CALDERA based on ATT&CK Matrix will be used as our platform. Inspired and guided by the previous study, a plugin in CALDERA called Tinker will be implemented, which is aiming to help the tester to get more information and also the mitigation of each technique used in the previous operation. Furthermore, the optional countermeasures for some techniques are also implemented and preset in Tinker in order to facilitate and fasten the process of the defense improvement of the tested system.

Keywords: automation, adversary emulation, CALDERA, countermeasures, MITRE ATT&CK

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Authors: Sara T. K. Li, Belinda H. J. Chung, Jeffery C. N. Yip, Janet H. Hsiao

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Music notation and text reading both involve statistical learning of music or linguistic structures. However, it remains unclear how music reading expertise influences text reading behavior. The present study examined this issue through an eye-tracking study. Chinese-English bilingual musicians and non-musicians read English sentences, Chinese sentences, musical phrases, and sentences in Tibetan, a language novel to the participants, with their eye movement recorded. Each set of stimuli consisted of two conditions in terms of structural regularity: syntactically correct and syntactically incorrect musical phrases/sentences. They then completed a sentence comprehension (for syntactically correct sentences) or a musical segment/word recognition task afterwards to test their comprehension/recognition abilities. The results showed that in reading musical phrases, as compared with non-musicians, musicians had a higher accuracy in the recognition task, and had shorter reading time, fewer fixations, and shorter fixation duration when reading syntactically correct (i.e., in diatonic key) than incorrect (i.e., in non-diatonic key/atonal) musical phrases. This result reflects their expertise in music reading. Interestingly, in reading Tibetan sentences, which was novel to both participant groups, while non-musicians did not show any behavior differences between reading syntactically correct or incorrect Tibetan sentences, musicians showed a shorter reading time and had marginally fewer fixations when reading syntactically correct sentences than syntactically incorrect ones. However, none of the musicians reported discovering any structural regularities in the Tibetan stimuli after the experiment when being asked explicitly, suggesting that they may have implicitly acquired the structural regularities in Tibetan sentences. This group difference was not observed when they read English or Chinese sentences. This result suggests that music reading expertise facilities reading texts in a novel language (i.e., Tibetan), but not in languages that the readers are already familiar with (i.e., English and Chinese). This phenomenon may be due to the similarities between reading music notations and reading texts in a novel language, as in both cases the stimuli follow particular statistical structures but do not involve semantic or lexical processing. Thus, musicians may transfer their statistical learning skills stemmed from music notation reading experience to implicitly discover structures of sentences in a novel language. This speculation is consistent with a recent finding showing that music reading expertise modulates the processing of English nonwords (i.e., words that do not follow morphological or orthographic rules) but not pseudo- or real words. These results suggest that the modulation of music reading expertise on language processing depends on the similarities in the cognitive processes involved. It also has important implications for the benefits of music education on language and cognitive development.

Keywords: eye movement behavior, eye-tracking, music reading expertise, sentence reading, structural regularity, visual processing

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Authors: Sunny Arora, Meghal A. Desai

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An impetus towards green consumerism and implementation of sustainable techniques, consumption of natural products and utilization of environment friendly techniques have gained accelerated acceptance. Butein, a natural colorant, has many medicinal properties apart from its use in dyeing industries. Extraction of butein from the flowers of flame of forest was carried out using ultrasonication bath. Solid loading (2-6 g), extraction time (30-50 min), volume of solvent (30-50 mL) and types of solvent (methanol, ethanol and water) have been studied to maximize the yield of butein using the Taguchi method. The highest yield of butein 4.67% (w/w) was obtained using 4 g of plant material, 40 min of extraction time and 30 mL volume of methanol as a solvent. The present method provided a greater reduction in extraction time compared to the conventional method of extraction. Hence, the outcome of the present investigation could further be utilized to develop the method at a higher scale.

Keywords: butein, flowers of Flame of the Forest, Taguchi method, ultrasonic bath

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Authors: Kai Warsoenke, Maik Mackiewicz

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To achieve a high quality of assembled car body structures, tolerancing is used to ensure a geometric accuracy of the single car body parts. There are two main techniques to determine the required tolerances. The first is tolerance analysis which describes the influence of individually tolerated input values on a required target value. Second is tolerance synthesis to determine the location of individual tolerances to achieve a target value. Both techniques are based on classical statistical methods, which assume certain probability distributions. To ensure competitiveness in both saturated and dynamic markets, production processes in vehicle manufacturing must be flexible and efficient. The dimensional specifications selected for the individual body components and the resulting assemblies have a major influence of the quality of the process. For example, in the manufacturing of forming tools as operating equipment or in the higher level of car body assembly. As part of the metrological process monitoring, manufactured individual parts and assemblies are recorded and the measurement results are stored in databases. They serve as information for the temporary adjustment of the production processes and are interpreted by experts in order to derive suitable adjustments measures. In the production of forming tools, this means that time-consuming and costly changes of the tool surface have to be made, while in the body shop, uncertainties that are difficult to control result in cost-intensive rework. The stored measurement results are not used to intelligently design tolerances in future processes or to support temporary decisions based on real-world geometric data. They offer potential to extend the tolerancing methods through data analysis and machine learning models. The purpose of this paper is to examine real-world measurement data from individual car body components, as well as assemblies, in order to develop an approach for using the data in short-term actions and future projects. For this reason, the measurement data will be analyzed descriptively in the first step in order to characterize their behavior and to determine possible correlations. In the following, a database is created that is suitable for developing machine learning models. The objective is to create an intelligent way to determine the position and number of measurement points as well as the local tolerance range. For this a number of different model types are compared and evaluated. The models with the best result are used to optimize equally distributed measuring points on unknown car body part geometries and to assign tolerance ranges to them. The current results of this investigation are still in progress. However, there are areas of the car body parts which behave more sensitively compared to the overall part and indicate that intelligent tolerancing is useful here in order to design and control preceding and succeeding processes more efficiently.

Keywords: automotive production, machine learning, process optimization, smart tolerancing

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Authors: Si Chen, Le Zhang, Xian He

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The research explored the influences of public open space system on housing price through hedonic models, in order to support better open space plans and economic policies. We have three initial hypotheses: 1) public open space system has an overall positive influence on surrounding housing prices. 2) Different public open space types have different levels of influence on motivating surrounding housing prices. 3) Walking and driving accessibilities from property to public open spaces have different statistical relation with housing prices. Cook County, Illinois, was chosen to be a study area since data availability, sufficient open space types, and long-term open space preservation strategies. We considered the housing attributes, driving and walking accessibility scores from houses to nearby public open spaces, and driving accessibility scores to hospitals as influential features and used real housing sales price in 2010 as a dependent variable in the built hedonic model. Through ordinary least squares (OLS) regression analysis, General Moran’s I analysis and geographically weighted regression analysis, we observed the statistical relations between public open spaces and housing sale prices in the three built hedonic models and confirmed all three hypotheses.

Keywords: hedonic model, public open space, housing sale price, regression analysis, accessibility score

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Authors: Ranjitkumar Patil

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Age estimation has its importance in forensic dentistry. Dental age estimation has emerged as an alternative to skeletal age determination. The methods based on stages of tooth formation, as appreciated on radiographs, seems to be more appropriate in the assessment of age than those based on skeletal development. The study was done to evaluate dental age in north Indian population using Cameriere’s method. Aims/Objectives: The study was conducted to assess the dental age of North Indian children using Cameriere’smethodand to compare the chronological age and dental age for validation of the Cameriere’smethod in the north Indian population. A comparative study of 02 year duration on the OPG (using PLANMECA Promax 3D) data of 497 individuals with age ranging from 5 to 15 years was done based on simple random technique ethical approval obtained from the institutional ethical committee. The data was obtained based on inclusion and exclusion criteria was analyzed by a software for dental age estimation. Statistical analysis: Student’s t test was used to compare the morphological variables of males with those of females and to compare observed age with estimated age. Regression formula was also calculated. Results: Present study was a comparative study of 497 subjects with a distribution between male and female, with their dental age assessed by using Panoramic radiograph, following the method described by Cameriere, which is widely accepted. Statistical analysis in our study indicated that gender does not have a significant influence on age estimation. (R2= 0.787). Conclusion: This infers that cameriere’s method can be effectively applied in north Indianpopulation.

Keywords: Forensic, Chronological Age, Dental Age, Skeletal Age

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Authors: Bouthiba Amina

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The estimation of the statistics bound to the precipitation represents a vast domain, which puts numerous challenges to meteorologists and hydrologists. Sometimes, it is necessary, to approach in value the extreme events for sites where there is little, or no datum, as well as their periods of return. The search for a model of the frequency of the heights of daily rains dresses a big importance in operational hydrology: It establishes a basis for predicting the frequency and intensity of floods by estimating the amount of precipitation in past years. The most known and the most common approach is the statistical approach, It consists in looking for a law of probability that fits best the values observed by the random variable " daily maximal rain " after a comparison of various laws of probability and methods of estimation by means of tests of adequacy. Therefore, a frequent analysis of the annual series of daily maximal rains was realized on the data of 54 pluviometric stations of the pond of high and average. This choice was concerned with five laws usually applied to the study and the analysis of frequent maximal daily rains. The chosen period is from 1970 to 2013. It was of use to the forecast of quantiles. The used laws are the law generalized by extremes to three components, those of the extreme values to two components (Gumbel and log-normal) in two parameters, the law Pearson typifies III and Log-Pearson III in three parameters. In Algeria, Gumbel's law has been used for a long time to estimate the quantiles of maximum flows. However, and we will check and choose the most reliable law.

Keywords: return period, extreme flow, statistics laws, Gumbel, estimation

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Authors: Jeries J. Abou-Hanna

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Several techniques exist for determining stress-intensity factors in linear elastic fracture mechanics analysis. These techniques are based on analytical, numerical, and empirical approaches that have been well documented in literature and engineering handbooks. However, not all techniques share the same merit. In addition to overly-conservative results, the numerical methods that require extensive computational effort, and those requiring copious user parameters hinder practicing engineers from efficiently evaluating stress-intensity factors. This paper investigates the prospects of reducing the complexity and required variables to determine stress-intensity factors through the utilization of the stress gradient and a weighting function. The heart of this work resides in the understanding that fracture emanating from stress concentration locations cannot be explained by a single maximum stress value approach, but requires use of a critical volume in which the crack exists. In order to understand the effectiveness of this technique, this study investigated components of different notch geometry and varying levels of stress gradients. Two forms of weighting functions were employed to determine stress-intensity factors and results were compared to analytical exact methods. The results indicated that the “exponential” weighting function was superior to the “absolute” weighting function. An error band +/- 10% was met for cases ranging from a steep stress gradient in a sharp v-notch to the less severe stress transitions of a large circular notch. The incorporation of the proposed method has shown to be a worthwhile consideration.

Keywords: fracture mechanics, finite element method, stress intensity factor, stress gradient

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Authors: O. Lasaga

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Defining professional objectives and the search for work are some of the greatest stress factors for final year university students and recent graduates. To manage correctly the stress brought about by the uncertainty, confusion and frustration this process often generates, a course to control stress based on mindfulness has been designed and taught. This course provides tools based on relaxation, mindfulness and meditation that enable students to address personal and professional challenges in the transition to the job market, eliminating or easing the anxiety involved. The course is extremely practical and experiential, combining theory classes and practical classes of relaxation, meditation and mindfulness, group dynamics, reflection, application protocols and session integration. The evaluation of the courses highlighted on the one hand the high degree of satisfaction and, on the other, the usefulness for the students in becoming aware of stressful situations and how these affect them and learning new coping techniques that enable them to reach their goals more easily and with greater satisfaction and well-being.

Keywords: employability, meditation, mindfulness, relaxation techniques, stress

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Authors: Bhuvnesh Narayanan, Bernhard Weiss, Tvrtko Mandic, Adrijan Baric

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This paper discusses a method to reconstruct internal high-frequency signals through subsampling techniques in an IC using an on-chip sensor. Though there are existing methods to internally probe and reconstruct high frequency signals through subsampling techniques; these methods have been applicable mainly for synchronized systems. This paper demonstrates a method for making such non-intrusive on-chip reconstructions possible also in non-synchronized systems. The TLP pulse is used to demonstrate the experimental validation of the concept. The on-chip sensor measures the voltage in an internal node. The jitter in the input pulse causes a varying pulse delay with respect to the on-chip sampling command. By measuring this pulse delay and by correlating it with the measured on-chip voltage, time domain waveforms can be reconstructed, and the influence of the pulse on the internal nodes can be better understood.

Keywords: on-chip sensor, jitter, transmission line pulse, subsampling

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Authors: Lim Kar Sing, Siti Nur Afifah Azraai, Norhazilan Md Noor, Nordin Yahaya

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Polymeric composites are being increasingly used as repair material for repairing critical infrastructures such as building, bridge, pressure vessel, piping and pipeline. Technique in repairing damaged pipes is one of the major concerns of pipeline owners. Considerable researches have been carried out on the repair of corroded pipes using composite materials. This article attempts a short review of the subject matter to provide insight into various techniques used in repairing corroded pipes, focusing on a wide range of composite repair systems. These systems including pre-cured layered, flexible wet lay-up, pre-impregnated, split composite sleeve and flexible tape systems. Both advantages and limitations of these repair systems were highlighted. Critical technical aspects have been discussed through the current standards and practices. Research gaps and future study scopes in achieving more effective design philosophy are also presented.

Keywords: composite materials, pipeline, repair technique, polymers

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Authors: Kittipob Kondee, Chutima Prommak

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In this paper, we propose an optimization technique that can be used to optimize the placements of reference nodes and improve the location determination performance for the multi-floor building. The proposed technique is based on Simulated Annealing algorithm (SA) and is called MSMR-M. The performance study in this work is based on simulation. We compare other node-placement techniques found in the literature with the optimal node-placement solutions obtained from our optimization. The results show that using the optimal node-placement obtained by our proposed technique can improve the positioning error distances up to 20% better than those of the other techniques. The proposed technique can provide an average error distance within 1.42 meters.

Keywords: indoor positioning system, optimization system design, multi-floor building, wireless sensor networks

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Authors: Arti Bagada, Kantilal Vadalia, Mihir Raval

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Cilnidipine is practically insoluble in water which results in its insufficient oral bioavailability. The purpose of the present investigation was to formulate cilnidipine nanoparticles by nanoprecipitation method to increase the aqueous solubility and dissolution rate and hence bioavailability by utilizing various experimental statistical design modules. Experimental design were used to investigate specific effects of independent variables during preparation cilnidipine nanoparticles and corresponding responses in optimizing the formulation. Plackett Burman design for independent variables was successfully employed for optimization of nanoparticles of cilnidipine. The influence of independent variables studied were drug concentration, solvent to antisolvent ratio, polymer concentration, stabilizer concentration and stirring speed. The dependent variables namely average particle size, polydispersity index, zeta potential value and saturation solubility of the formulated nanoparticles of cilnidipine. The experiments were carried out according to 13 runs involving 5 independent variables (higher and lower levels) employing Plackett-Burman design. The cilnidipine nanoparticles were characterized by average particle size, polydispersity index value, zeta potential value and saturation solubility and it results were 149 nm, 0.314, 43.24 and 0.0379 mg/ml, respectively. The experimental results were good correlated with predicted data analysed by Plackett-Burman statistical method.

Keywords: dissolution enhancement, nanoparticles, Plackett-Burman design, nanoprecipitation

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Authors: Raja Asad Zaheer, Aisha Tanveer, Hafza Mehreen Fatima

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Global Software Development (GSD) is becoming a common norm in software industry, despite of the fact that global distribution of the teams presents special issues for effective communication and coordination of the teams. Now trends are changing and project management for distributed teams is no longer in a limbo. GSD can be effectively established using agile and project managers can use different agile techniques/tools for solving the problems associated with distributed teams. Agile methodologies like scrum and XP have been successfully used with distributed teams. We have employed exploratory research method to analyze different recent studies related to challenges of GSD and their proposed solutions. In our study, we had deep insight in six commonly faced challenges: communication and coordination, temporal differences, cultural differences, knowledge sharing/group awareness, speed and communication tools. We have established that each of these challenges cannot be neglected for distributed teams of any kind. They are interlinked and as an aggregated whole can cause the failure of projects. In this paper we have focused on creating a scalable framework for detecting and overcoming these commonly faced challenges. In the proposed solution, our objective is to suggest agile techniques/tools relevant to a particular problem faced by the organizations related to the management of distributed teams. We focused mainly on scrum and XP techniques/tools because they are widely accepted and used in the industry. Our solution identifies the problem and suggests an appropriate technique/tool to help solve the problem based on globally shared knowledgebase. We can establish a cause and effect relationship using a fishbone diagram based on the inputs provided for issues commonly faced by organizations. Based on the identified cause, suitable tool is suggested, our framework suggests a suitable tool. Hence, a scalable, extensible, self-learning, intelligent framework proposed will help implement and assess GSD to achieve maximum out of it. Globally shared knowledgebase will help new organizations to easily adapt best practices set forth by the practicing organizations.

Keywords: agile project management, agile tools/techniques, distributed teams, global software development

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Authors: Lingxia Li, Frédéric Schnell, Thibault Lachard, Anne-Charlotte Dupont, Shuzhe Ding, Solène Le Douairon Lahaye

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Background: Repetitive participation in triathlon training results in significant myocardial changes. However, whether the cardiac remodeling in triathletes is related to the specificities of the sport (consisting of three sports) raises questions. Methods: Elite triathletes and cyclists registered on the French ministerial lists of high-level athletes were involved. The basic information and routine electrocardiogram records were obtained. Electrocardiograms were evaluated according to clinical criteria. Results: Of the 105 athletes included in the study, 42 were from the short-distance triathlon (40%), and 63 were from the road cycling (60%). The average age was 22.1±4.2 years. The P wave amplitude was significantly lower in triathletes than in cyclists (p=0.005), and no significant statistical difference was found in heart rate, RR interval, PR or PQ interval, QRS complex, QRS axe, QT interval, and QTc (p>0.05). All the measured parameters were within normal ranges. The most common electrical manifestations were early repolarization (60.95%) and incomplete right bundle branch block (43.81%); there was no statistical difference between the groups (p>0.05). Conclusions: Prolonged intensive endurance exercise training induces physiological cardiac remodeling in both triathletes and cyclists. The most common electrocardiogram manifestations were early repolarization and incomplete right bundle branch block.

Keywords: cardiac screening, electrocardiogram, triathlon, cycling, elite athletes

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Authors: Zenaide Carvalho da Silva, Leandro Rodrigues Ferreira, Andrey Ricardo Pimentel

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Learning styles (LS) refer to the ways and forms that the student prefers to learn in the teaching and learning process. Each student has their own way of receiving and processing information throughout the learning process. Therefore, knowing their LS is important to better understand their individual learning preferences, and also, understand why the use of some teaching methods and techniques give better results with some students, while others it does not. We believe that knowledge of these styles enables the possibility of making propositions for teaching; thus, reorganizing teaching methods and techniques in order to allow learning that is adapted to the individual needs of the student. Adapting learning would be possible through the creation of online educational resources adapted to the style of the student. In this context, this article presents the structure of a learning object interface adaptation based on the LS. The structure created should enable the creation of the adapted learning object according to the student's LS and contributes to the increase of student’s motivation in the use of a learning object as an educational resource.

Keywords: adaptation, interface, learning object, learning style

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Authors: Strahinja Kovačević, Lidija Jevrić, Miloš Kuzmanović, Sanja Podunavac-Kuzmanović

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In this paper, various derivatives of benzimidazole have been evaluated against Gram-negative bacteria Escherichia coli. For all investigated compounds the minimum inhibitory concentration (MIC) was determined. Quantitative structure-activity relationships (QSAR) attempts to find consistent relationships between the variations in the values of molecular properties and the biological activity for a series of compounds so that these rules can be used to evaluate new chemical entities. The correlation between MIC and some absorption, distribution, metabolism and excretion (ADME) parameters was investigated, and the mathematical models for predicting the antibacterial activity of this class of compounds were developed. The quality of the multiple linear regression (MLR) models was validated by the leave-one-out (LOO) technique, as well as by the calculation of the statistical parameters for the developed models and the results are discussed on the basis of the statistical data. The results of this study indicate that ADME parameters have a significant effect on the antibacterial activity of this class of compounds. Principal component analysis (PCA) and agglomerative hierarchical clustering algorithms (HCA) confirmed that the investigated molecules can be classified into groups on the basis of the ADME parameters: Madin-Darby Canine Kidney cell permeability (MDCK), Plasma protein binding (PPB%), human intestinal absorption (HIA%) and human colon carcinoma cell permeability (Caco-2).

Keywords: benzimidazoles, QSAR, ADME, in silico

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Authors: K. A. Chinmaya, M. Udaya Bhaskar

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Multiphase motor drives are now a day considered for numerous applications due to the advantages that they offer when compared to their three-phase counterparts. Proper modeling of inverters and motors are important in devising an appropriate control algorithm. This paper develops a complete modeling of a five-phase inverter and five-phase space vector modulation schemes which can be used for five-phase motor drives. A novel modified algorithm is introduced which enables the sinusoidal output voltages up to certain voltage value. The waveforms of phase to neutral voltage are compared with the different modulation techniques and also different modulation indexes in terms of Low-order Harmonic (LH) voltage of 3rd and 7th present. A detailed performance evolution of existing and newly modified schemes is done in terms of Total Harmonic Distortion (THD).

Keywords: multi-phase drives, space vector modulation, voltage source inverter, low order harmonic voltages, total harmonic distortion

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Authors: Rakesh Kumar Soni

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Under free living conditions, human biological clocks show a periodicity of 24 hour for numerous physiological, behavioral and biochemical variables. However, this period is not the original period; rather it merely exhibits synchronization with the solar clock. It is, therefore, most important to investigate characteristics of human circadian clock, essentially in shift workers, who normally confront with contrasting social clocks. Aim of the present study was to investigate rest-activity rhythm and to vouch for the best technique for the computation of periods in this rhythm in subjects randomly selected from different groups of shift workers. The rest-activity rhythm was studied in forty-eight shift workers from three different organizations, namely Newspaper Printing Press (NPP), Chhattisgarh State Electricity Board (CSEB) and Raipur Alloys (RA). Shift workers of NPP (N = 20) were working on a permanent night shift schedule (NS; 20:00-04:00). However, in CSEB (N = 14) and RA (N = 14), shift workers were working in a 3-shift system comprising of rotations from night (NS; 22:00-06:00) to afternoon (AS; 14:00-22:00) and to morning shift (MS; 06:00-14:00). Each subject wore an Actiwatch (AW64, Mini Mitter Co. Inc., USA) for 7 and/or 21 consecutive days, only after furnishing a certificate of consent. One-minute epoch length was chosen for the collection of wrist activity data. Period was determined by using Actiware sleep software (Periodogram), Lomb-Scargle Periodogram (LSP) and Spectral analysis software (Spectre). Other statistical techniques, such as ANOVA and Duncan’s multiple-range test were also used whenever required. A statistically significant circadian rhythm in rest-activity, gauged by cosinor, was documented in all shift workers, irrespective of shift work. Results indicate that the efficiency of the technique to determine the period (τ) depended upon the clipping limits of the τs. It appears that the technique of spectre is more reliable.

Keywords: biological clock, rest activity rhythm, spectre, periodogram

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Authors: Marwah Mohsin, Thomas Beach, Alan Kwan, Mahdi Ismail

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This paper describes the differences in views on sustainable development between the general public and experts in a developing country, Iraq. This paper will answer the question: How do the views of the public differ from the generally accepted view of experts in the context of sustainable urban development in Iraq? In order to answer this question, the views of both the public and the experts will be analysed. These results are taken from a public survey and a Delphi questionnaire. These will be analysed using statistical methods in order to identify the significant differences. This will enable investigation of the different perceptions between the public perceptions and the experts’ views towards urban sustainable development factors. This is important due to the fact that different viewpoints between policy-makers and the public will impact on the acceptance by the public of any future sustainable development work that is undertaken. The brief findings of the statistical analysis show that the views of both the public and the experts are considered different in most of the variables except six variables show no differences. Those variables are ‘The importance of establishing sustainable cities in Iraq’, ‘Mitigate traffic congestion’, ‘Waste recycling and separating’, ‘Use wastewater recycling’, ‘Parks and green spaces’, and ‘Promote investment’.

Keywords: urban sustainability, experts views, public views, principle component analysis, PCA

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Authors: Rahul R. Gurpude, Pallvita Yadav, Amrut Mulay

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In recent years, there has been a growing focus on advanced manufacturing processes, and one such emerging process is wire electric discharge machining (WEDM). WEDM is a precision machining process specifically designed for cutting electrically conductive materials with exceptional accuracy. It achieves material removal from the workpiece metal through spark erosion facilitated by electricity. Initially developed as a method for precision machining of hard materials, WEDM has witnessed significant advancements in recent times, with numerous studies and techniques based on electrical discharge phenomena being proposed. These research efforts and methods in the field of ED encompass a wide range of applications, including mirror-like finish machining, surface modification of mold dies, machining of insulating materials, and manufacturing of micro products. WEDM has particularly found extensive usage in the high-precision machining of complex workpieces that possess varying hardness and intricate shapes. During the cutting process, a wire with a diameter ranging from 0.18mm is employed. The evaluation of EDM performance typically revolves around two critical factors: material removal rate (MRR) and surface roughness (SR). To comprehensively assess the impact of machining parameters on the quality characteristics of EDM, an Analysis of Variance (ANOVA) was conducted. This statistical analysis aimed to determine the significance of various machining parameters and their relative contributions in controlling the response of the EDM process. By undertaking this analysis, optimal levels of machining parameters were identified to achieve desirable material removal rates and surface roughness.

Keywords: WEDM, MRR, optimization, surface roughness

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Authors: Ilario Gabriele Gerloni, Vincenza Carchiolo, Alessandro Longheu, Ugo Becciani, Eva Sciacca, Fabio Vitello

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ICT tools and platforms endorse more and more educational process. Many models and techniques for people to be educated and trained about specific topics and skills do exist, as classroom lectures with textbooks, computers, handheld devices and others. The choice to what extent ICT is applied within learning contexts is related to personal access to technologies as well as to the infrastructure surrounding environment. Among recent techniques, the adoption of Virtual Reality (VR) and Augmented Reality (AR) provides significant impulse in fully engaging users senses. In this paper, an application of AR/VR within Geographic Information Systems (GIS) context is presented. It aims to provide immersive environment experiences for educational and training purposes (e.g. for civil protection personnel), useful especially for situations where real scenarios are not easily accessible by humans. First acknowledgments are promising for building an effective tool that helps civil protection personnel training with risk reduction.

Keywords: education, virtual reality, augmented reality, GIS, civil protection

Procedia PDF Downloads 178

Authors: Ahmed ElAzab, Amira M. Idrees, Mahmoud A. Mahmoud, Hesham Hefny

Abstract:

Social networking sites such as Twitter and Facebook attracts over 500 million users across the world, for those users, their social life, even their practical life, has become interrelated. Their interaction with social networking has affected their life forever. Accordingly, social networking sites have become among the main channels that are responsible for vast dissemination of different kinds of information during real time events. This popularity in Social networking has led to different problems including the possibility of exposing incorrect information to their users through fake accounts which results to the spread of malicious content during life events. This situation can result to a huge damage in the real world to the society in general including citizens, business entities, and others. In this paper, we present a classification method for detecting fake accounts on Twitter. The study determines the minimized set of the main factors that influence the detection of the fake accounts on Twitter, then the determined factors have been applied using different classification techniques, a comparison of the results for these techniques has been performed and the most accurate algorithm is selected according to the accuracy of the results. The study has been compared with different recent research in the same area, this comparison has proved the accuracy of the proposed study. We claim that this study can be continuously applied on Twitter social network to automatically detect the fake accounts, moreover, the study can be applied on different Social network sites such as Facebook with minor changes according to the nature of the social network which are discussed in this paper.

Keywords: fake accounts detection, classification algorithms, twitter accounts analysis, features based techniques

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Authors: George Abu-Aqil, Leah Tsror, Elad Shufan, Shaul Mordechai, Mahmoud Huleihel, Ahmad Salman

Abstract:

Pectobacterium and Dickeya spp which negatively affect a wide range of crops are the main causes of the aggressive diseases of agricultural crops. These aggressive diseases are responsible for a huge economic loss in agriculture including a severe decrease in the quality of the stored vegetables and fruits. Therefore, it is important to detect these pathogenic bacteria at their early stages of infection to control their spread and consequently reduce the economic losses. In addition, early detection is vital for producing non-infected propagative material for future generations. The currently used molecular techniques for the identification of these bacteria at the strain level are expensive and laborious. Other techniques require a long time of ~48 h for detection. Thus, there is a clear need for rapid, non-expensive, accurate and reliable techniques for early detection of these bacteria. In this study, infrared spectroscopy, which is a well-known technique with all its features, was used for rapid detection of Pectobacterium and Dickeya spp. at the strain level. The bacteria were isolated from potato plants and tubers with soft rot symptoms and measured by infrared spectroscopy. The obtained spectra were analyzed using different machine learning algorithms. The performances of our approach for taxonomic classification among the bacterial samples were evaluated in terms of success rates. The success rates for the correct classification of the genus, species and strain levels were ~100%, 95.2% and 92.6% respectively.

Keywords: soft rot enterobacteriaceae (SRE), pectobacterium, dickeya, plant infections, potato, solanum tuberosum, infrared spectroscopy, machine learning

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Authors: Zahid Ullah, Atlas Khan

Abstract:

This abstract focuses on the advancements in mathematical modeling and optimization techniques that play a crucial role in enhancing the efficiency, reliability, and performance of these systems. In this era of rapidly evolving technology, mathematical modeling and optimization offer powerful tools to tackle the complex challenges faced by control, signal processing, and energy systems. This abstract presents the latest research and developments in mathematical methodologies, encompassing areas such as control theory, system identification, signal processing algorithms, and energy optimization. The abstract highlights the interdisciplinary nature of mathematical modeling and optimization, showcasing their applications in a wide range of domains, including power systems, communication networks, industrial automation, and renewable energy. It explores key mathematical techniques, such as linear and nonlinear programming, convex optimization, stochastic modeling, and numerical algorithms, that enable the design, analysis, and optimization of complex control and signal processing systems. Furthermore, the abstract emphasizes the importance of addressing real-world challenges in control, signal processing, and energy systems through innovative mathematical approaches. It discusses the integration of mathematical models with data-driven approaches, machine learning, and artificial intelligence to enhance system performance, adaptability, and decision-making capabilities. The abstract also underscores the significance of bridging the gap between theoretical advancements and practical applications. It recognizes the need for practical implementation of mathematical models and optimization algorithms in real-world systems, considering factors such as scalability, computational efficiency, and robustness. In summary, this abstract showcases the advancements in mathematical modeling and optimization techniques for control, signal processing, and energy systems. It highlights the interdisciplinary nature of these techniques, their applications across various domains, and their potential to address real-world challenges. The abstract emphasizes the importance of practical implementation and integration with emerging technologies to drive innovation and improve the performance of control, signal processing, and energy.

Keywords: mathematical modeling, optimization, control systems, signal processing, energy systems, interdisciplinary applications, system identification, numerical algorithms

Procedia PDF Downloads 113