Search results for: fuzzy sets

Authors: Khadidja Rahmani, Nahla Bouaziz

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The climate change and the economic crisis, which are currently running, are the origin of the emergence of many issues and problems, which are related to the domain of energy and environment in à direct or indirect manner. Since the urban space is the core element and the key to solve the current problem, particular attention is given to it in this study. For this reason, we rented to the later a very particular attention; this is for the opportunities that it provides and that can be invested to attenuate a little this situation, which is disastrous and worried, especially in the face of the requirements of sustainable development. Indeed, the purpose of this work is to develop a method, which will allow us to guide designers towards projects with a certain degree of thermo-aeraulic comfort while requiring a minimum energy consumption. In this context, the architects, the urban planners and the engineers (energeticians) have to collaborate jointly to establish a method based on indicators for the improvement of the urban environmental quality (aeraulic-thermo comfort), correlated with a reduction in the energy demand of the entities that make up this environment, in areas with a sub-humid climate. In order to test the feasibility and to validate the method developed in this work, we carried out a series of simulations using computer-based simulation. This research allows us to evaluate the impact of the use of the indicators in the design of the urban sets, on the economic and ecological plan. Using this method, we prove that an urban design, which carefully considered energetically, can contribute significantly to the preservation of the environment and the reduction of the consumption of energy.

Keywords: comfort, energy consumption, energy mastery, morpho-energetic indicators, simulation, sub-humid climate, urban sets

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Authors: S. S. Adamu, H. S. Muhammad, D. S. Shuaibu

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Poor power quality in electrical power systems can lead to medical equipment at healthcare centres to malfunction and present wrong medical diagnosis. Equipment such as X-rays, computerized axial tomography, etc. can pollute the system due to their high level of harmonics production, which may cause a number of undesirable effects like heating, equipment damages and electromagnetic interferences. The conventional approach of mitigation uses passive inductor/capacitor (LC) filters, which has some drawbacks such as, large sizes, resonance problems and fixed compensation behaviours. The current trends of solutions generally employ active power filters using suitable control algorithms. This work focuses on assessing the level of Total Harmonic Distortion (THD) on medical facilities and various ways of mitigation, using radiology unit of an existing hospital as a case study. The measurement of the harmonics is conducted with a power quality analyzer at the point of common coupling (PCC). The levels of measured THD are found to be higher than the IEEE 519-1992 standard limits. The system is then modelled as a harmonic current source using MATLAB/SIMULINK. To mitigate the unwanted harmonic currents a shunt active filter is developed using synchronous detection algorithm to extract the fundamental component of the source currents. Fuzzy logic controller is then developed to control the filter. The THD without the active power filter are validated using the measured values. The THD with the developed filter show that the harmonics are now within the recommended limits.

Keywords: power quality, total harmonics distortion, shunt active filters, fuzzy logic

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Authors: Roman Graf, Sergiu Gordea, Heather M. Ryan

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This paper presents an approach for the easy creation of an institutional risk profile for endangerment analysis of file formats. The main contribution of this work is the employment of data mining techniques to support risk factors set up with just the most important values that are important for a particular organisation. Subsequently, the risk profile employs fuzzy models and associated configurations for the file format metadata aggregator to support digital preservation experts with a semi-automatic estimation of endangerment level for file formats. Our goal is to make use of a domain expert knowledge base aggregated from a digital preservation survey in order to detect preservation risks for a particular institution. Another contribution is support for visualisation and analysis of risk factors for a requried dimension. The proposed methods improve the visibility of risk factor information and the quality of a digital preservation process. The presented approach is meant to facilitate decision making for the preservation of digital content in libraries and archives using domain expert knowledge and automatically aggregated file format metadata from linked open data sources. To facilitate decision-making, the aggregated information about the risk factors is presented as a multidimensional vector. The goal is to visualise particular dimensions of this vector for analysis by an expert. The sample risk profile calculation and the visualisation of some risk factor dimensions is presented in the evaluation section.

Keywords: digital information management, file format, endangerment analysis, fuzzy models

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Authors: Ankit Sinha, Soham Banerjee, Pratik Chattopadhyay

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Automated product recognition in retail stores is an important real-world application in the domain of Computer Vision and Pattern Recognition. In this paper, we consider the problem of automatically identifying the classes of the products placed on racks in retail stores from an image of the rack and information about the query/product images. We improve upon the existing approaches in terms of effectiveness and memory requirement by developing a two-stage object detection and recognition pipeline comprising of a Faster-RCNN-based object localizer that detects the object regions in the rack image and a ResNet-18-based image encoder that classifies the detected regions into the appropriate classes. Each of the models is fine-tuned using appropriate data sets for better prediction and data augmentation is performed on each query image to prepare an extensive gallery set for fine-tuning the ResNet-18-based product recognition model. This encoder is trained using a triplet loss function following the strategy of online-hard-negative-mining for improved prediction. The proposed models are lightweight and can be connected in an end-to-end manner during deployment to automatically identify each product object placed in a rack image. Extensive experiments using Grozi-32k and GP-180 data sets verify the effectiveness of the proposed model.

Keywords: retail stores, faster-RCNN, object localization, ResNet-18, triplet loss, data augmentation, product recognition

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Authors: Sinead Impey, Damon Berry, Selma Furtado, Miriam Galvin, Loretto Grogan, Orla Hardiman, Lucy Hederman, Mark Heverin, Vincent Wade, Linda Douris, Declan O'Sullivan, Gaye Stephens

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Healthcare is a knowledge-rich environment. This knowledge, while valuable, is not always accessible outside the borders of individual clinics. This research aims to address part of this problem (at a study site) by constructing a maximal data set (knowledge artefact) for motor neurone disease (MND). This data set is proposed as an initial knowledge base for a concurrent project to develop an MND patient data platform. It represents the domain knowledge at the study site for the duration of the research (12 months). A knowledge elicitation method was also developed from the lessons learned during this process - the WICKED method. WICKED is an anagram of the words: eliciting and confirming data, information, knowledge, wisdom. But it is also a reference to the concept of wicked problems, which are complex and challenging, as is eliciting expert knowledge. The method was evaluated at a second site, and benefits and limitations were noted. Benefits include that the method provided a systematic way to manage data, information, knowledge and wisdom (DIKW) from various sources, including healthcare specialists and existing data sets. Limitations surrounded the time required and how the data set produced only represents DIKW known during the research period. Future work is underway to address these limitations.

Keywords: healthcare, knowledge acquisition, maximal data sets, action design science

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Authors: Sarah K. Hagi, Majdi Alnowaimi

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In the last decade, there were immense advancements in the medical imaging modalities. These advancements can scan a whole volume of the lung organ in high resolution images within a short time. According to this performance, the physicians can clearly identify the complicated anatomical and pathological structures of lung. Therefore, these advancements give large opportunities for more advance of all types of lung cancer treatment available and will increase the survival rate. However, lung cancer is still one of the major causes of death with around 19% of all the cancer patients. Several factors may affect survival rate. One of the serious effects is the breathing process, which can affect the accuracy of diagnosis and lung tumor treatment plan. We have therefore developed a semi automated algorithm to localize the 3D lung tumor positions across all respiratory data during respiratory motion. The algorithm can be divided into two stages. First, a lung tumor segmentation for the first phase of the 4D computed tomography (CT). Lung tumor segmentation is performed using an active contours method. Then, localize the tumor 3D position across all next phases using a 12 degrees of freedom of an affine transformation. Two data set where used in this study, a compute simulate for 4D CT using extended cardiac-torso (XCAT) phantom and 4D CT clinical data sets. The result and error calculation is presented as root mean square error (RMSE). The average error in data sets is 0.94 mm ± 0.36. Finally, evaluation and quantitative comparison of the results with a state-of-the-art registration algorithm was introduced. The results obtained from the proposed localization algorithm show a promising result to localize alung tumor in 4D CT data.

Keywords: automated algorithm , computed tomography, lung tumor, tumor localization

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Authors: Sogol Zarafshan

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The present study aimed to identify important factors affecting the effectiveness of quality control circles in a hospital, as well as rank them using a combination of fuzzy VIKOR and Grey Relational Analysis (GRA). The study population consisted of five academic members and five experts in the field of nursing working in a hospital, who were selected using a purposive sampling method. Also, a sample of 107 nurses was selected through a simple random sampling method using their employee codes and the random-number table. The required data were collected using a researcher-made questionnaire which consisted of 12 factors. The validity of this questionnaire was confirmed through giving the opinions of experts and academic members who participated in the present study, as well as performing confirmatory factor analysis. Its reliability also was verified (α=0.796). The collected data were analyzed using SPSS 22.0 and LISREL 8.8, as well as VIKOR–GRA and IPA methods. The results of ranking the factors affecting the effectiveness of quality control circles showed that the highest and lowest ranks were related to ‘Managers’ and supervisors’ support’ and ‘Group leadership’. Also, the highest hospital performance was for factors such as ‘Clear goals and objectives’ and ‘Group cohesiveness and homogeneity’, and the lowest for ‘Reward system’ and ‘Feedback system’, respectively. The results showed that although ‘Training the members’, ‘Using the right tools’ and ‘Reward system’ were factors that were of great importance, the organization’s performance for these factors was poor. Therefore, these factors should be paid more attention by the studied hospital managers and should be improved as soon as possible.

Keywords: Quality control circles, Fuzzy VIKOR, Grey Relational Analysis, Importance–Performance Analysis

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Authors: David Blaine, George Raschbaum

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The evaluation of DHCP has synthesized SCSI disks, and current trends suggest that the exploration of e-business that would allow for further study into robots will soon emerge. Given the current status of embedded algorithms, hackers worldwide obviously desire the exploration of replication, which embodies the confusing principles of programming languages. In our research we concentrate our efforts on arguing that erasure coding can be made "fuzzy", encrypted, and game-theoretic.

Keywords: SCHI disks, robot, algorithm, hacking, programming language

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Authors: Shraddha Jain Sharma, Ratnalata Gupta

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Predicting stock movements in the financial market is both difficult and rewarding. Analysts and academics are increasingly using advanced approaches such as machine learning techniques to anticipate stock price patterns, thanks to the expanding capacity of computing and the recent advent of graphics processing units and tensor processing units. Stock market prediction is a type of time series prediction that is incredibly difficult to do since stock prices are influenced by a variety of financial, socioeconomic, and political factors. Furthermore, even minor mistakes in stock market price forecasts can result in significant losses for companies that employ the findings of stock market price prediction for financial analysis and investment. Soft computing techniques are increasingly being employed for stock market prediction due to their better accuracy than traditional statistical methodologies. The proposed research looks at the need for soft computing techniques in stock market prediction, the numerous soft computing approaches that are important to the field, past work in the area with their prominent features, and the significant problems or issue domain that the area involves. For constructing a predictive model, the major focus is on neural networks and fuzzy logic. The stock market is extremely unpredictable, and it is unquestionably tough to correctly predict based on certain characteristics. This study provides a complete overview of the numerous strategies investigated for high accuracy prediction, with a focus on the most important characteristics.

Keywords: stock market prediction, artificial intelligence, artificial neural networks, fuzzy logic, accuracy, deep learning, machine learning, stock price, trading volume

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Authors: Sanjivrao Katakam, Thanumoorthi I., Antony Gerald, Ratan Kulkarni, Shaju Nair

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With increasing energy costs and its impact on the business, sustainability today has evolved from a social expectation to an economic imperative. Therefore, finding methods to reduce cost has become a critical directive for Industry leaders. Effective energy management is the only way to cut costs. However, Energy Management has been a challenge because it requires a change in old habits and legacy systems followed for decades. Today exorbitant levels of energy and operational data is being captured and stored by Industries, but they are unable to convert these structured and unstructured data sets into meaningful business intelligence. It must be noted that for quick decisions, organizations must learn to cope with large volumes of operational data in different formats. Energy analytics not only helps in extracting inferences from these data sets, but also is instrumental in transformation from old approaches of energy management to new. This in turn assists in effective decision making for implementation. It is the requirement of organizations to have an established corporate strategy for reducing operational costs through visibility and optimization of energy usage. Energy analytics play a key role in optimization of operations. The paper describes how today energy data analytics is extensively used in different scenarios like reducing operational costs, predicting energy demands, optimizing network efficiency, asset maintenance, improving customer insights and device data insights. The paper also highlights how analytics helps transform insights obtained from energy data into sustainable solutions. The paper utilizes data from an array of segments such as retail, transportation, and water sectors.

Keywords: energy analytics, energy management, operational data, business intelligence, optimization

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Authors: Keerti Kembhavi, M. R. Archana, V. Anjaneyappa

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Most asphalt pavement condition evaluation use rating frameworks in which asphalt pavement distress is estimated by type, extent, and severity. Rating is carried out by the pavement condition rating (PCR), which is tedious and expensive. This paper presents the development of a low-cost technique for image pavement distress analysis that permits the identification of pothole and cracks. The paper explores the application of image processing tools for the detection of potholes and cracks. Longitudinal cracking and pothole are detected using Fuzzy-C- Means (FCM) and proceeded with the Spectral Theory algorithm. The framework comprises three phases, including image acquisition, processing, and extraction of features. A digital camera (Gopro) with the holder is used to capture pavement distress images on a moving vehicle. FCM classifier and Spectral Theory algorithms are used to compute features and classify the longitudinal cracking and pothole. The Matlab2016Ra Image preparing tool kit utilizes performance analysis to identify the viability of pavement distress on selected urban stretches of Bengaluru city, India. The outcomes of image evaluation with the utilization semi-computerized image handling framework represented the features of longitudinal crack and pothole with an accuracy of about 80%. Further, the detected images are validated with the actual dimensions, and it is seen that dimension variability is about 0.46. The linear regression model y=1.171x-0.155 is obtained using the existing and experimental / image processing area. The R2 correlation square obtained from the best fit line is 0.807, which is considered in the linear regression model to be ‘large positive linear association’.

Keywords: crack detection, pothole detection, spectral clustering, fuzzy-c-means

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Authors: Cesar Vazquez-Gonzalez, Sophie Avila-Foucat, Leonardo Ortiz-Lozano, Patricia Moreno-Casasola, Alejandro Granados-Barba

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In the last twenty years, academic and scientific literature has been focused on understanding the processes and factors of coastal social-ecological systems vulnerability and resilience. Some scholars argue that resilience and vulnerability are isolated concepts due to their epistemological origin, while others note the existence of a strong resilience-vulnerability relationship. Here we present an ordinal logistic regression model based on the analytical framework about dynamic resilience-vulnerability interaction along adaptive cycle of complex systems and disasters process phases (during, recovery and learning). In this way, we demonstrate that 1) during the disturbance, absorptive capacity (resilience as a core of attributes) and external response capacity explain the probability of households capitals to diminish the damage, and exposure sets the thresholds about the amount of disturbance that households can absorb, 2) at recovery, absorptive capacity and external response capacity explain the probability of households capitals to recovery faster (resilience as an outcome) from damage, and 3) at learning, adaptive capacity (resilience as a core of attributes) explains the probability of households adaptation measures based on the enhancement of physical capital. As a result, during the disturbance phase, exposure has the greatest weight in the probability of capital’s damage, and households with absorptive and external response capacity elements absorbed the impact of floods in comparison with households without these elements. At the recovery phase, households with absorptive and external response capacity showed a faster recovery on their capital; however, the damage sets the thresholds of recovery time. More importantly, diversity in financial capital increases the probability of recovering other capital, but it becomes a liability so that the probability of recovering the household finances in a longer time increases. At learning-reorganizing phase, adaptation (modifications to the house) increases the probability of having less damage on physical capital; however, it is not very relevant. As conclusion, resilience is an outcome but also core of attributes that interacts with vulnerability along the adaptive cycle and disaster process phases. Absorptive capacity can diminish the damage experienced by floods; however, when exposure overcomes thresholds, both absorptive and external response capacity are not enough. In the same way, absorptive and external response capacity diminish the recovery time of capital, but the damage sets the thresholds in where households are not capable of recovering their capital.

Keywords: absorptive capacity, adaptive capacity, capital, floods, recovery-learning, social-ecological systems

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Authors: Hyeon-Kyo Lim, Hyunjung Kim, Kune-Woo Lee

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Decommissioning process of nuclear facilities can be said to consist of a sequence of problem solving activities, partly because there may exist working environments contaminated by radiological exposure, and partly because there may also exist industrial hazards such as fire, explosions, toxic materials, and electrical and physical hazards. As for an individual hazard factor, risk assessment techniques are getting known to industrial workers with advance of safety technology, but the way how to integrate those results is not. Furthermore, there are few workers who experienced decommissioning operations a lot in the past. Therefore, not a few countries in the world have been trying to develop appropriate counter techniques in order to guarantee safety and efficiency of the process. In spite of that, there still exists neither domestic nor international standard since nuclear facilities are too diverse and unique. In the consequence, it is quite inevitable to imagine and assess the whole risk in the situation anticipated one by one. This paper aimed to find out an appropriate technique to integrate individual risk assessment results from the viewpoint of experts. Thus, on one hand the whole risk assessment activity for decommissioning operations was modeled as a sequence of individual risk assessment steps, and on the other, a hierarchical risk structure was developed. Then, risk assessment procedure that can elicit individual hazard factors one by one were introduced with reference to the standard operation procedure (SOP) and hierarchical task analysis (HTA). With an assumption of quantification and normalization of individual risks, a technique to estimate relative weight factors was tried by using the conventional Analytic Hierarchical Process (AHP) and its result was reviewed with reference to judgment of experts. Besides, taking the ambiguity of human judgment into consideration, debates based upon fuzzy inference was added with a mathematical case study.

Keywords: decommissioning, risk assessment, analytic hierarchical process (AHP), fuzzy inference

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Authors: Raphael M. Ritti-Dias, Marilia A. Correia, Wagner J. R. Domingues, Aline C. Palmeira, Paulo Longano, Nelson Wolosker, Lauro C. Vianna, Gabriel G. Cucato

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Patients with peripheral arterial disease (PAD) have a high prevalence of hypertension, which contributes to a high risk of acute cardiovascular events and cardiovascular mortality. Strategies to reduce cardiovascular risk of these patients are needed. Meta-analysis studies have shown that isometric handgrip training promotes reductions in clinical blood pressure in normotensive, pre-hypertensive and hypertensive individuals. However, the effect of this exercise training on other cardiovascular function indicators in PAD patients remains unknown. Thus, the aim of this study was to analyze the effects of isometric handgrip training on blood pressure in patients with PAD. In this clinical trial, 28 patients were randomly allocated into two groups: isometric handgrip training (HG) and control (CG). The HG conducted the unilateral handgrip training three days per week (four sets of two minutes, with 30% of maximum voluntary contraction with an interval of four minutes between sets). CG was encouraged to increase their physical activity levels. At baseline and after eight weeks blood pressure and heart rate were obtained. ANOVA two-way for repeated measures with the group (GH and GC) and time (pre- and post-intervention) as factors was performed. After 8 weeks of training there were no significant changes in systolic blood pressure (HG pre 141 ± 24.0 mmHg vs. HG post 142 ± 22.0 mmHg; CG pre 140 ± 22.1 mmHg vs. CG post 146 ± 16.2 mmHg; P=0.18), diastolic blood pressure (HG pre 74 ± 10.4 mmHg vs. HG post 74 ± 11.9 mmHg; CG pre 72 ± 6.9 mmHg vs. CG post 74 ± 8.0 mmHg; P=0.22) and heart rate (HG pre 61 ± 10.5 bpm vs. HG post 62 ± 8.0 bpm; CG pre 64 ± 11.8 bpm vs. CG post 65 ± 13.6 bpm; P=0.81). In conclusion, our preliminary data indicate that isometric handgrip training did not modify blood pressure and heart rate in patients with PAD.

Keywords: blood pressure, exercise, isometric, peripheral artery disease

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Authors: Sudhir Kumar Singh, Debashish Chakravarty

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Slope stability analysis is an important aspect in the field of geotechnical engineering. It is also important from safety, and economic point of view as any slope failure leads to loss of valuable lives and damage to property worth millions. This paper aims at mitigating the risk of slope failure by studying the effect of different joint sets on the stability of mine pit slopes under the influence of various external factors, namely degree of saturation, rainfall intensity, and seismic coefficients. Supervised machine learning approach has been utilized for making accurate and reliable predictions regarding the stability of slopes based on the value of Factor of Safety. Numerous cases have been studied for analyzing the stability of slopes using the popular Finite Element Method, and the data thus obtained has been used as training data for the supervised machine learning models. The input data has been trained on different supervised machine learning models, namely Random Forest, Decision Tree, Support vector Machine, and XGBoost. Distinct test data that is not present in training data has been used for measuring the performance and accuracy of different models. Although all models have performed well on the test dataset but Random Forest stands out from others due to its high accuracy of greater than 95%, thus helping us by providing a valuable tool at our disposition which is neither computationally expensive nor time consuming and in good accordance with the numerical analysis result.

Keywords: finite element method, geotechnical engineering, machine learning, slope stability

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Authors: Bjornar Kjellstadli, Shaher A. I. Shalfawi

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The aim of this study was to investigate the effect of strength training Cluster set-method compared to traditional set-method 30 m sprinting time and maximum strength in squats and bench-press. Thirteen Physical Education students, 7 males and 6 females between the age of 19-28 years old were recruited. The students were random divided in three groups. Traditional set group (TSG) consist of 2 males and 2 females aged (±SD) (22.3 ± 1.5 years), body mass (79.2 ± 15.4 kg) and height (177.5 ± 11.3 cm). Cluster set group (CSG) consist of 3 males and 2 females aged (22.4 ± 3.29 years), body mass (81.0 ± 24.0 kg) and height (179.2 ± 11.8 cm) and a control group (CG) consist of 2 males and 2 females aged (21.5 ± 2.4 years), body mass (82.1 ± 17.4 kg) and height (175.5 ± 6.7 cm). The intervention consisted of performing squat and bench press at 70% of 1RM (twice a week) for 8 weeks using 10 repetition and 4 sets. Two types of strength-training methods were used , cluster set (CS) where the participants (CSG) performed 2 reps 5 times with a 10 s recovery in between reps and 50 s recovery between sets, and traditional set (TS) where the participants (TSG) performed 10 reps each set with 90 s recovery in between sets. The pre-tests and post-tests conducted were 1 RM in both squats and bench press, and 10 and 30 m sprint time. The 1RM test were performed with Eleiko XF barbell (20 kg), Eleiko weight plates, rack and bench from Hammerstrength. The speed test was measured with the Brower speed trap II testing system (Brower Timing Systems, Utah, USA). The participants received an individualized training program based on the pre-test of the 1RM. In addition, a mid-term test of 1RM was carried out to adjust training intensity. Each training session were supervised by the researchers. Beast sensors (Milano, Italy) were also used to monitor and quantify the training load for the participants. All groups had a statistical significant improvement in bench press 1RM (TSG 1RM from 56.3 ± 28.9 to 66 ± 28.5 kg; CSG 1RM from 69.8 ± 33.5 to 77.2 ± 34.1 kg and CG 1RM from 67.8 ± 26.6 to 72.2 ± 29.1 kg), whereas only the TSG (1RM from 84.3 ± 26.8 to 114.3 ± 26.5 kg) and CSG (1RM from 100.4 ± 33.9 to 129 ± 35.1 kg) had a statistical significant improvement in Squats 1RM (P < 0.05). However, a between groups examination reveals that there were no marked differences in 1RM squat performance between TSG and CSG (P > 0.05) and both groups had a marked improvements compared to the CG (P < 0.05). On the other hand, no differences between groups were observed in Bench press 1RM. The within groups results indicate that none of the groups had any marked improvement in the distances from 0-10 m and 10-30 m except the CSG which had a notable improvement in the distance from 10-30 m (-0.07 s; P < 0.05). Furthermore, no differences in sprinting abilities were observed between groups. The results from this investigation indicate that traditional set strength training at 70% of 1RM gave close results compared to Cluster set strength training at the same intensity. However, the results indicate that the cluster set had an effect on flying time (10-30 m) indicating that the velocity at which those repetitions were performed could be the explanation factor of this this improvement.

Keywords: physical performance, 1RM, pushing velocity, velocity based training

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Authors: Abdolrasoul Daneshjoo, Fatemeh Akbari Ghara

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Background and Purpose: Obesity is an increasing factor in cardiovascular disease and serum levels of cellular adhesion molecule. It plays an important role in predicting risk for coronary artery disease. The purpose of this research was to study the effect of eight weeks moderate intensity interval training and curcumin intake on ICAM-1 & VCAM-1 levels of menopausal fat rats. Materials and methods: in this study, 28 Wistar Menopausal fat rats aged 6-8 weeks with an average weight of 250-300 (gr) were randomly divided into four groups: control, curcumin supplement, moderate intensity interval training and moderate intensity interval training + curcumin supplement. (7 rats each group). The training program was planned as 8 weeks and 3 sessions per week. Each session consisted of 10 one-min sets with 50 percent intensity and the 2-minutes interval between sets in the first week. Subjects started with 14 meters per minute, and 2 (m/min) was added to increase their speed weekly until the speed of 28 (m/min) in the 8th week. Blood samples were taken 48 hours after the last training session, and ICAM-1 A and VCAM-1 levels were measured. SPSS software, one-way analysis of variance (ANOVA) and Pearson correlation coefficient were used to assess the results. Results: The results showed that eight weeks of training and taking curcumin had significant effects on ICAM-1 levels of the rats (p ≤ 0.05). However, it had no significant effect on VCAM-1 levels in menopausal obese rates (p ≥ 0.05). There was no significant correlation between the levels of ICAM-1 and VCAM-1 in eight weeks training and taking curcumin. Conclusion: Implementation of moderate intensity interval training and the use of curcumin decreased ICAM-1 significantly.

Keywords: curcumin, interval training , ICMA, VCAM

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Authors: Travis Morris

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The purpose of this study is to measure the intersection of environmental security entities in terrorist propaganda. To the best of author’s knowledge, this is the first study of its kind to examine this intersection within terrorist propaganda. Rosoka, natural language processing software and frame analysis are used to advance our understanding of how environmental frames function as emotive triggers. Violent jihadi demagogues use frames to suggest violent and non-violent solutions to their grievances. Emotive triggers are framed in a way to leverage individual and collective attitudes in psychological warfare. A comparative research design is used because of the differences and similarities that exist between two variants of violent jihadi propaganda that target western audiences. Analysis is based on salience and network text analysis, which generates violent jihadi semantic networks. Findings indicate that environmental frames are used as emotive triggers across both data sets, but also as tactical and information data points. A significant finding is that certain core environmental emotive triggers like “water,” “soil,” and “trees” are significantly salient at the aggregate level across both data sets. All environmental entities can be classified into two categories, symbolic and literal. Importantly, this research illustrates how demagogues use environmental emotive triggers in cyber space from a subcultural perspective to mobilize target audiences to their ideology and praxis. Understanding the anatomy of propaganda construction is necessary in order to generate effective counter narratives in information operations. This research advances an additional method to inform practitioners and policy makers of how environmental security and propaganda intersect.

Keywords: propaganda analysis, emotive triggers environmental security, frames

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Authors: Benjamin Chijioke Agwah, Paulinus Chinaenye Eze

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Antilock braking system (ABS) is one of the important contributions by the automobile industry, designed to ensure road safety in such way that vehicles are kept steerable and stable when during emergency braking. This paper presents a wheel slip-based intelligent controller with variable zero lag compensation for ABS. It is required to achieve a very fast perfect wheel slip tracking during hard braking condition and eliminate chattering with improved transient and steady state performance, while shortening the stopping distance using effective braking torque less than maximum allowable torque to bring a braking vehicle to a stop. The dynamic of a vehicle braking with a braking velocity of 30 ms⁻¹ on a straight line was determined and modelled in MATLAB/Simulink environment to represent a conventional ABS system without a controller. Simulation results indicated that system without a controller was not able to track desired wheel slip and the stopping distance was 135.2 m. Hence, an intelligent control based on fuzzy logic controller (FLC) was designed with a variable zero lag compensator (VZLC) added to enhance the performance of FLC control variable by eliminating steady state error, provide improve bandwidth to eliminate the effect of high frequency noise such as chattering during braking. The simulation results showed that FLC- VZLC provided fast tracking of desired wheel slip, eliminate chattering, and reduced stopping distance by 70.5% (39.92 m), 63.3% (49.59 m), 57.6% (57.35 m) and 50% (69.13 m) on dry, wet, cobblestone and snow road surface conditions respectively. Generally, the proposed system used effective braking torque that is less than the maximum allowable braking torque to achieve efficient wheel slip tracking and overall robust control performance on different road surfaces.

Keywords: ABS, fuzzy logic controller, variable zero lag compensator, wheel slip tracking

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Authors: Chang-Fa Liu, Yi-Ting Wang, Yuan Liu, Hai-Feng Wei, Lei Fang, Jin Li

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Heavy metals in sediments can cause adverse ecological effects while it exceeds a given criteria. The present study investigated sediment environmental quality, pollutant enrichment, ecological risk, and source identification for copper, cadmium, lead, zinc, mercury, and arsenic in the sediments collected from tidal flat marsh of Shuangtai estuary, China. The arithmetic mean integrated pollution index, geometric mean integrated pollution index, fuzzy integrated pollution index, and principal component score were used to characterize sediment environmental quality; fuzzy similarity and geo-accumulation Index were used to evaluate pollutant enrichment; correlation matrix, principal component analysis, and cluster analysis were used to identify source of pollution; environmental risk index and potential ecological risk index were used to assess ecological risk. The environmental qualities of sediment are classified to very low degree of contamination or low contamination. The similar order to element background of soil in the Liaohe plain is region of Sanjiaozhou, Honghaitan, Sandaogou, Xiaohe by pollutant enrichment analysis. The source identification indicates that correlations are significantly among metals except between copper and cadmium. Cadmium, lead, zinc, mercury, and arsenic will be clustered in the same clustering as the first principal component. Copper will be clustered as second principal component. The environmental risk assessment level will be scaled to no risk in the studied area. The order of potential ecological risk is As > Cd > Hg > Cu > Pb > Zn.

Keywords: ecological risk assessment, heavy metals, sediment, marsh, Shuangtai estuary

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Authors: Mehmet Akif Aslan, Mehmet Simsek, Eyup Sensoy

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Big Data stands for today’s cutting-edge technology. As the technology becomes widespread, so does Data. Utilizing massive data sets enable companies to get competitive advantages over their adversaries. Out of many area of Big Data usage, logistics has significance role in both commercial sector and military. This paper lays out what big data is and how it is used in both military and commercial logistics.

Keywords: big data, logistics, operational efficiency, risk management

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Authors: Dmitry K. Shaytan, Georgy D. Laptev

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In our research we aimed to test a managerial approach for the fuzzy front end (FFE) of innovation by creating controlled experiment/ business case in a breakthrough innovation development. The experiment was in the sport industry and covered all aspects of the customer discovery stage from ideation to prototyping followed by patent application. In the paper we describe and analyze mile stones, tasks, management challenges, decisions made to create the break through innovation, evaluate overall managerial efficiency that was at the considered FFE stage. We set managerial outcome of the FFE stage as a valid product concept in hand. In our paper we introduce hypothetical construct “Q-factor” that helps us in the experiment to distinguish quality of FFE outcomes. The experiment simulated for entrepreneur the FFE of innovation and put on his shoulders responsibility for the outcome of valid product concept. While developing managerial approach to reach the outcome there was a decision to look on product concept from the cognitive psychology and cognitive science point of view. This view helped us to develop the profile of a person whose projection (mental representation) of a new product could optimize for a manager or entrepreneur FFE activities. In the experiment this profile was tested to develop breakthrough innovation for swimmers. Following the managerial approach the product concept was created to help swimmers to feel/sense water. The working prototype was developed to estimate the product concept validity and value added effect for customers. Based on feedback from coachers and swimmers there were strong positive effect that gave high value for customers, and for the experiment – the valid product concept being developed by proposed managerial approach for the FFE. In conclusions there is a suggestion of managerial approach that was derived from experiment.

Keywords: concept development, concept testing, customer discovery, entrepreneurship, entrepreneurial management, idea generation, idea screening, startup management

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Authors: Chhabi Nigam, S. Ramakrishnan

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This paper brings out the analysis of the airborne Synthetic Aperture Radar (SAR) data to study the impact of Doppler centroid on Image quality and geocoding accuracy from the perspective of Stripmap mode of data acquisition. Although in Stripmap mode of data acquisition radar beam points at 90 degrees broad side (side looking), shift in the Doppler centroid is invariable due to platform motion. In-accurate estimation of Doppler centroid leads to poor image quality and image miss-registration. The effect of Doppler centroid is analyzed in this paper using multiple sets of data collected from airborne platform. Occurrences of ghost (ambiguous) targets and their power levels have been analyzed that impacts appropriate choice of PRF. Effect of aircraft attitudes (roll, pitch and yaw) on the Doppler centroid is also analyzed with the collected data sets. Various stages of the RDA (Range Doppler Algorithm) algorithm used for image formation in Stripmap mode, range compression, Doppler centroid estimation, azimuth compression, range cell migration correction are analyzed to find the performance limits and the dependence of the imaging geometry on the final image. The ability of Doppler centroid estimation to enhance the imaging accuracy for registration are also illustrated in this paper. The paper also tries to bring out the processing of low squint SAR data, the challenges and the performance limits imposed by the imaging geometry and the platform dynamics on the final image quality metrics. Finally, the effect on various terrain types, including land, water and bright scatters is also presented.

Keywords: ambiguous target, Doppler Centroid, image registration, Airborne SAR

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Authors: Takashi Mitsuishi, Koji Saigusa

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This paper presents circular polar coordinates transformation of periodic fuzzy membership function. The purpose is identification of domain of periodic membership functions in consequent part of IF-THEN rules. The proposed methods are applied to the simple color construct system.

Keywords: periodic membership function, polar coordinates transformation, defuzzification, circular coordinates

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Authors: Zhongmin Wang, Wudong Fan, Hengshan Zhang, Yimin Zhou

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In data-driven prognostic methods, the prediction accuracy of the estimation for remaining useful life of bearings mainly depends on the performance of health indicators, which are usually fused some statistical features extracted from vibrating signals. However, the existing health indicators have the following two drawbacks: (1) The differnet ranges of the statistical features have the different contributions to construct the health indicators, the expert knowledge is required to extract the features. (2) When convolutional neural networks are utilized to tackle time-frequency features of signals, the time-series of signals are not considered. To overcome these drawbacks, in this study, the method combining convolutional neural network with gated recurrent unit is proposed to extract the time-frequency image features. The extracted features are utilized to construct health indicator and predict remaining useful life of bearings. First, original signals are converted into time-frequency images by using continuous wavelet transform so as to form the original feature sets. Second, with convolutional and pooling layers of convolutional neural networks, the most sensitive features of time-frequency images are selected from the original feature sets. Finally, these selected features are fed into the gated recurrent unit to construct the health indicator. The results state that the proposed method shows the enhance performance than the related studies which have used the same bearing dataset provided by PRONOSTIA.

Keywords: continuous wavelet transform, convolution neural net-work, gated recurrent unit, health indicators, remaining useful life

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Authors: Taimaz Larimian, Negin Sadeghi

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Very little is known about the impacts of urban security on the level of social sustainability within the cities of developing countries. Urban security is still struggling to find its position in the social sustainability agenda, despite the significant role of safety and security on different aspects of peoples’ lives. This paper argues that urban safety and security should be better integrated within the social sustainability framework. With this aim, this study investigates the hypothesized relationship between social sustainability and Crime Prevention through Environmental Design (CPTED) approach at the neighborhood scale. This study proposes a model of key influential dimensions of CPTED analyzed into localized factors and sub-factors. These factors are then prioritized using pairwise comparison logic and fuzzy group Analytic Hierarchy Process (AHP) method in order to determine the relative importance of each factor on achieving social sustainability. The proposed model then investigates social sustainability in six case study neighborhoods of Isfahan city based on residents’ perceptions of safety within their neighborhood. Mixed method of data collection is used by using a self-administered questionnaire to explore the residents’ perceptions of social sustainability in their area of residency followed by an on-site observation to measure the CPTED construct. In all, 150 respondents from selected neighborhoods were involved in this research. The model indicates that CPTED approach has a significant direct influence on increasing social sustainability in neighborhood scale. According to the findings, among different dimensions of CPTED, ‘activity support’ and ‘image/ management’ have the most influence on people’s feeling of safety within studied areas. This model represents a useful designing tool in achieving urban safety and security during the development of more socially sustainable and user-friendly urban areas.

Keywords: crime prevention through environmental design (CPTED), developing countries, fuzzy analytic hierarchy process (FAHP), social sustainability

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Authors: Lin Li, P. G. R. N. I. Pussella

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When representing spatial data and their attributes on different types of maps, the scale plays a key role in the process of map generalization. The process is consisted with two main operators such as selection and omission. Once some data were selected, they would undergo of several geometrical changing processes such as elimination, simplification, smoothing, exaggeration, displacement, aggregation and size reduction. As a result of these operations at different levels of data, the geometry of the spatial features such as length, sinuosity, orientation, perimeter and area would be altered. This would be worst in the case of preparation of small scale maps, since the cartographer has not enough space to represent all the features on the map. What the GIS users do is when they wanted to analyze a set of spatial data; they retrieve a data set and does the analysis part without considering very important characteristics such as the scale, the purpose of the map and the degree of generalization. Further, the GIS users use and compare different maps with different degrees of generalization. Sometimes, GIS users are going beyond the scale of the source map using zoom in facility and violate the basic cartographic rule 'it is not suitable to create a larger scale map using a smaller scale map'. In the study, the effect of map generalization for GIS analysis would be discussed as the main objective. It was used three digital maps with different scales such as 1:10000, 1:50000 and 1:250000 which were prepared by the Survey Department of Sri Lanka, the National Mapping Agency of Sri Lanka. It was used common features which were on above three maps and an overlay analysis was done by repeating the data with different combinations. Road data, River data and Land use data sets were used for the study. A simple model, to find the best place for a wild life park, was used to identify the effects. The results show remarkable effects on different degrees of generalization processes. It can see that different locations with different geometries were received as the outputs from this analysis. The study suggests that there should be reasonable methods to overcome this effect. It can be recommended that, as a solution, it would be very reasonable to take all the data sets into a common scale and do the analysis part.

Keywords: generalization, GIS, scales, spatial analysis

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Authors: Rajrupa Ghosh, Abhijit Mitra

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Future use of animal protein sources in prawn feeds is expected to be considerably reduced as a consequence of increasing economical, environmental and safety issues. Of main concern has been the use of expensive marine protein sources, such as fish meal which often results in fouling of water quality and disease outbreak in cultured species. To determine prawn capacity to use practical feeds with plant proteins as replacement ingredients to animal protein sources, 8-months growth trial was conducted in two sets of ponds using juvenile (0.02 gm) Macrobrachium rosenbergii. Among the two sets, one set (comprising of three ponds) is experimental pond included formulated feed prepared with 30% Porteresia coarctata dust along with other general ingredients and another set (comprising of another three ponds) is control pond with commercial feed. Mean final weight, percent weight gain, final net yield, feed conversion ratio and survival were evaluated. Higher condition index values, survival rate and gain in prawn weight were observed in experimental pond compared to control pond. Low FCR values were observed in the experimental pond than the control pond. Evaluation of production parameters at the end of the study demonstrated significant differences (P ≥ 0.05) among two ponds. The variation may be attributed to specially formulated plant based feed that not only boosted up the growth of prawns, but also upgraded the ambient aquatic health. These results indicate that fish meal can be replaced with algal protein sources in diets without affecting prawn growth and production.

Keywords: macrobrachium rosenbergii, porteresia coarctata, Indian sundarbans, feed

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Authors: Mary Joy C. Montenegro, Voltaire M. Mistades

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This action research was implemented to enhance the teaching of linear motion and to improve students' conceptual understanding and engagement using a developed 7R-based module called 'module on vectors and one-dimensional kinematics' (MVOK). MVOK was validated in terms of objectives, contents, format, and language used, presentation, usefulness, and overall presentation. The validation process revealed a value of 4.7 interpreted as 'Very Acceptable' with a substantial agreement (0. 60) from the validators. One intact class of 46 Grade 12 STEM students from one of the public schools in Paranaque City served as the participants of this study. The students were taught using the module during the first semester of the academic year 2019–2020. Employing the mixed-method approach, quantitative data were gathered using pretest/posttest, activity sheets, problem sets, and survey form, while qualitative data were obtained from surveys, interviews, observations, and reflection log. After the implementation, there was a significant difference of 18.4 on students’ conceptual understanding as shown in their pre-test and post-test scores on the 24-item test with a moderate Hake gain equal to 0.45 and an effect size of 0.83. Moreover, the scores on activity and problem sets have a 'very good' to 'excellent' rating, which signifies an increase in the level of students’ conceptual understanding. There also exists a significant difference between the mean scores of students’ engagement overall (t= 4.79, p = 0.000, p < 0.05) and in the dimension of emotion (t = 2.51, p = 0.03) and participation/interaction (t = 5.75, p = 0.001). These findings were supported by gathered qualitative data. Positive views were elicited from the students since it is an accessible tool for learning and has well-detailed explanations and examples. The results of this study may substantiate that using MVOK will lead to better physics content understanding and higher engagement.

Keywords: conceptual understanding, engagement, linear motion, module

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Authors: I. Arockiarani

Abstract:

The focus of the paper is to furnish the entropy measure for a neutrosophic set and neutrosophic soft set which is a measure of uncertainty and it permeates discourse and system. Various characterization of entropy measures are derived. Further we exemplify this concept by applying entropy in various real time decision making problems.

Keywords: entropy measure, Hausdorff distance, neutrosophic set, soft set

Procedia PDF Downloads 257