Abstract: In this study, the absorbed dose of human organs after injection of 177Lu-DOTATOC was studied based on the biodistribution of the complex in adenocarcinoma breast cancer bearing mice. For this purpose, the biodistribution of the radiolabelled complex was studied and compartmental modeling was applied to calculate the absorbed dose with high precision. As expected, 177Lu-DOTATOC illustrated a notable specific uptake in tumor and pancreas, organs with high level of somatostatin receptor on their surface and the effectiveness of the radio-conjugate for targeting of the breast adenocarcinoma tumors was indicated. The elicited results of modeling were the exponential equations, and those are utilized for obtaining the cumulated activity data by taking their integral. The results also exemplified that non-target absorbed-doses such as the liver, spleen and pancreas were approximately 0.008, 0.004, and 0.039, respectively. While these values were so much lower than target (tumor) absorbed-dose, it seems due to this low toxicity, this complex is a good agent for therapy.
Abstract: Technological and sociological developments in the automotive sector are shifting the focus of design towards developing a better understanding of driver needs, desires and emotions. Human centred design methods are being more frequently applied to automotive research, including the use of systems to detect human emotions in real-time. One method for a non-contact measurement of emotion with low intrusiveness is Facial-Expression Analysis (FEA). This paper describes a research study investigating emotional responses of 22 participants in a naturalistic driving environment by applying a multi-method approach. The research explored the possibility to investigate emotional responses and their frequencies during naturalistic driving through real-time FEA. Observational analysis was conducted to assign causes to the collected emotional responses. In total, 730 emotional responses were measured in the collective study time of 440 minutes. Causes were assigned to 92% of the measured emotional responses. This research establishes and validates a methodology for the study of emotions and their causes in the driving environment through which systems and factors causing positive and negative emotional effects can be identified.
Abstract: Human motion recognition has been extensively increased in recent years due to its importance in a wide range of applications, such as human-computer interaction, intelligent surveillance, augmented reality, content-based video compression and retrieval, etc. However, it is still regarded as a challenging task especially in realistic scenarios. It can be seen as a general machine learning problem which requires an effective human motion representation and an efficient learning method. In this work, we introduce a descriptor based on Laban Movement Analysis technique, a formal and universal language for human movement, to capture both quantitative and qualitative aspects of movement. We use Discrete Hidden Markov Model (DHMM) for training and classification motions. We improve the classification algorithm by proposing two DHMMs for each motion class to process the motion sequence in two different directions, forward and backward. Such modification allows avoiding the misclassification that can happen when recognizing similar motions. Two experiments are conducted. In the first one, we evaluate our method on a public dataset, the Microsoft Research Cambridge-12 Kinect gesture data set (MSRC-12) which is a widely used dataset for evaluating action/gesture recognition methods. In the second experiment, we build a dataset composed of 10 gestures(Introduce yourself, waving, Dance, move, turn left, turn right, stop, sit down, increase velocity, decrease velocity) performed by 20 persons. The evaluation of the system includes testing the efficiency of our descriptor vector based on LMA with basic DHMM method and comparing the recognition results of the modified DHMM with the original one. Experiment results demonstrate that our method outperforms most of existing methods that used the MSRC-12 dataset, and a near perfect classification rate in our dataset.
Abstract: What people say on social media has turned into a
rich source of information to understand social behavior. Specifically,
the growing use of Twitter social media for political communication
has arisen high opportunities to know the opinion of large numbers
of politically active individuals in real time and predict the global
political tendencies of a specific country. It has led to an increasing
body of research on this topic. The majority of these studies have
been focused on polarized political contexts characterized by only
two alternatives. Unlike them, this paper tackles the challenge
of forecasting Spanish political trends, characterized by multiple
political parties, by means of analyzing the Twitters Users political
tendency. According to this, a new strategy, named Tweets Analysis
Strategy (TAS), is proposed. This is based on analyzing the users
tweets by means of discovering its sentiment (positive, negative or
neutral) and classifying them according to the political party they
support. From this individual political tendency, the global political
prediction for each political party is calculated. In order to do this,
two different strategies for analyzing the sentiment analysis are
proposed: one is based on Positive and Negative words Matching
(PNM) and the second one is based on a Neural Networks Strategy
(NNS). The complete TAS strategy has been performed in a Big-Data
environment. The experimental results presented in this paper reveal
that NNS strategy performs much better than PNM strategy to analyze
the tweet sentiment. In addition, this research analyzes the viability
of the TAS strategy to obtain the global trend in a political context
make up by multiple parties with an error lower than 23%.
Abstract: In order to study the topic of cities crossing rivers, a Four-Dimensional Analysis Method consisting of timeline, X-axis, Y-axis, and Z-axis is proposed. Policies, plans, and their implications are summarized and researched along with the timeline. The X-axis is the direction which is parallel to the river. The research area was chosen because of its important connection function. It is proposed that more surface water network should be built because of the ecological orientation of the research area. And the analysis of groundwater makes it for sure that the proposal is feasible. After the blue water network is settled, the green landscape network which is surrounded by it could be planned. The direction which is transversal to the river (Y-axis) should run through the transportation axis so that the urban texture could stretch in an ecological way. Therefore, it is suggested that the work of the planning bureau and river bureau should be coordinated. The Z-axis research is on the section view of the river, especially on the Yellow River’s special feature of being a perched river. Based on water control safety demands, river parks could be constructed on the embankment buffer zone, whereas many kinds of ornamental trees could be used to build the buffer zone. City Crossing River is a typical case where we make use of landscaping to build a symbiotic relationship between the urban landscape architecture and the environment. The local environment should be respected in the process of city expansion. The planning order of "Benefit- Flood Control Safety" should be replaced by "Flood Control Safety - Landscape Architecture- People - Benefit".
Abstract: In this paper we present a contribution for the modeling and control of wind energy conversion system based on a Doubly Fed Induction Generator (DFIG). Since the wind speed is random the system has to produce an optimal electrical power to the Network and ensures important strength and stability. In this work, the Backstepping controller is used to control the generator via two converter witch placed a DC bus capacitor and connected to the grid by a Filter R-L, in order to optimize capture wind energy. All is simulated and presented under MATLAB/Simulink Software to show performance and robustness of the proposed controller.
Abstract: Fe-based amorphous feedstock powders are used as the matrix into which various ratios of hard B4C nanoparticles (0, 5, 10, 15, 20 vol.%) as reinforcing agents were prepared using a planetary high-energy mechanical milling. The ball-milled nanocomposite feedstock powders were also sprayed by means of high-velocity oxygen fuel (HVOF) technique. The characteristics of the powder particles and the prepared coating depending on their microstructures and nanohardness were examined in detail using nanoindentation tester. The results showed that the formation of the Fe-based amorphous phase was noticed over the course of high-energy ball milling. It is interesting to note that the nanocomposite coating is divided into two regions, namely, a full amorphous phase region and homogeneous dispersion of B4C nanoparticles with a scale of 10–50 nm in a residual amorphous matrix. As the B4C content increases, the nanohardness of the composite coatings increases, but the fracture toughness begins to decrease at the B4C content higher than 20 vol.%. The optimal mechanical properties are obtained with 15 vol.% B4C due to the suitable content and uniform distribution of nanoparticles. Consequently, the changes in mechanical properties of the coatings were attributed to the changes in the brittle to ductile transition by adding B4C nanoparticles.
Abstract: The constant monitoring of blood glucose level is necessary for maintaining health of patients and to alert medical specialists to take preemptive measures before the onset of any complication as a result of diabetes. The current clinical monitoring of blood glucose uses invasive methods repeatedly which are uncomfortable and may result in infections in diabetic patients. Several attempts have been made to develop non-invasive techniques for blood glucose measurement. In this regard, the existing methods are not reliable and are less accurate. Other approaches claiming high accuracy have not been tested on extended dataset, and thus, results are not statistically significant. It is a well-known fact that acetone concentration in breath has a direct relation with blood glucose level. In this paper, we have developed the first of its kind, reliable and high accuracy breath analyzer for non-invasive blood glucose measurement. The acetone concentration in breath was measured using MQ 138 sensor in the samples collected from local hospitals in Pakistan involving one hundred patients. The blood glucose levels of these patients are determined using conventional invasive clinical method. We propose a linear regression classifier that is trained to map breath acetone level to the collected blood glucose level achieving high accuracy.
Abstract: Classification of high resolution polarimetric Synthetic Aperture Radar (PolSAR) images plays an important role in land cover and land use management. Recently, classification algorithms based on Bag of Visual Words (BOVW) model have attracted significant interest among scholars and researchers in and out of the field of remote sensing. In this paper, BOVW model with pixel based low-level features has been implemented to classify a subset of San Francisco bay PolSAR image, acquired by RADARSAR 2 in C-band. We have used segment-based decision-making strategy and compared the result with the result of traditional Support Vector Machine (SVM) classifier. 90.95% overall accuracy of the classification with the proposed algorithm has shown that the proposed algorithm is comparable with the state-of-the-art methods. In addition to increase in the classification accuracy, the proposed method has decreased undesirable speckle effect of SAR images.
Abstract: The aim of this study is to investigate four-week resistance exercise and milk supplement on NT-proBNP and plasma troponin I of male students. Concerning the methodology of the study, 21 senior high school students of Ardebil city were selected. The selected subjects were randomly shared in three groups of control, exercise- water and exercise- milk. The exercise program includes resistance exercise for a big muscle group. The subjects of control group rested during the study and did not participate in any training. The subjects of exercise- water experimental group immediately received 400 cc water after exercise and exercise- milk group immediately received 400 cc low fat milk. Control-water groups consumed the same amount of water. 48 hours before and after the last exercise session, the blood sample of the subjects were taken for measuring the variables. NT-proBNP and Troponin I concentrations were measured by ELISA. For data analysis, one-way variance analysis test, correlated t-test and Bonferroni post hoc test were used. The significant difference of p ≤ 0.05 was accepted. Resistance training along with milk consumption leads to increase of plasma NT-proBNP, however; this increase has not reached the significant level. Furthermore, meaningful increase was observed in plasma NT–proBNP in exercise group between pretest and posttest values. Furthermore, no meaningful difference was observed between groups in terms of Troponin I after milk consumption. It seems that endurance exercises lead to change in the structure of heart muscle and is along with an increase of NT-proBNP. Furthermore, there is the possibility that milk consumption can lead to release of heart troponin I. The mechanism through which protein supplements have been put on heart troponin I is unknown and requires more research.
Abstract: Underground structures are of those structures that have uncertainty in design procedures. That is due to the complexity of soil condition around. Under passing tunnels are also such affected structures. Despite geotechnical site investigations, lots of uncertainties exist in soil properties due to unknown events. As results, it possibly causes conflicting settlements in numerical analysis with recorded values in the project. This paper aims to report a case study on a specific under passing tunnel constructed by New Austrian Tunnelling Method in Iran. The intended tunnel has an overburden of about 11.3m, the height of 12.2m and, the width of 14.4m with 2.5 traffic lane. The numerical modeling was developed by a 2D finite element program (PLAXIS Version 8). Comparing displacement histories at the ground surface during the entire installation of initial lining, the estimated surface settlement was about four times the field recorded one, which indicates that some local unknown events affect that value. Also, the displacement ratios were in a big difference between the numerical and field data. Consequently, running several numerical back analyses using laboratory and field tests data, the geotechnical parameters were accurately revised to match with the obtained monitoring data. Finally, it was found that usually the values of soil parameters are conservatively low-estimated up to 40 percent by typical engineering judgment. Additionally, it could be attributed to inappropriate constitutive models applied for the specific soil condition.
Abstract: 5
In order to eradicate the degradation of reinforced concrete structures due to the steel corrosion, professionals in constructions suggest using fiber reinforced polymers (FRP) for their excellent properties. Nevertheless, high temperatures may affect the bond between FRP bar and concrete, and consequently the serviceability of FRP-reinforced concrete structures. This paper presents a nonlinear numerical investigation using ADINA software to investigate the effect of the spacing between glass FRP (GFRP) bars embedded in concrete on circumferential thermal deformations and the distribution of radial thermal cracks in reinforced concrete beams submitted to high temperature variations up to 60 °C for asymmetrical problems. The thermal deformations predicted from nonlinear finite elements model, at the FRP bar/concrete interface and at the external surface of concrete cover, were established as a function of the ratio of concrete cover thickness to FRP bar diameter (c/db) and the ratio of spacing between FRP bars in concrete to FRP bar diameter (e/db). Numerical results show that the circumferential thermal deformations at the external surface of concrete cover are linear until cracking thermal load varied from 32 to 55 °C corresponding to the ratio of e/db varied from 1.3 to 2.3, respectively. However, for ratios e/db >2.3 and c/db >1.6, the thermal deformations at the external surface of concrete cover exhibit linear behavior without any cracks observed on the specified surface. The numerical results are compared to those obtained from analytical models validated by experimental tests.
Abstract: This research investigates the acoustical characteristics
of Al-Madinah Holy Mosque. Extensive field measurements were
conducted in different locations of Al-Madinah Holy Mosque to
characterize its acoustic characteristics. The acoustical characteristics
are usually evaluated by the use of objective parameters in unoccupied
rooms due to practical considerations. However, under normal
conditions, the room occupancy can vary such characteristics due
to the effect of the additional sound absorption present in the room
or by the change in signal-to-noise ratio. Based on the acoustic
measurements carried out in Al-Madinah Holy Mosque with and
without occupancy, and the analysis of such measurements, the
existence of acoustical deficiencies has been confirmed.
Abstract: A critical problem in wireless sensor networks is limited battery and memory of nodes. Therefore, each node in the network could maintain only a subset of its neighbors to communicate with. This will increase the battery usage in the network because each packet should take more hops to reach its destination. In order to tackle these problems, spanner graphs are defined. Since each node has a small degree in a spanner graph and the distance in the graph is not much greater than its actual geographical distance, spanner graphs are suitable candidates to be used for the topology of a wireless sensor network. In this paper, we study Yao graphs and their behavior for a randomly selected set of points. We generate several random point sets and compare the properties of their Yao graphs with the complete graph. Based on our data sets, we obtain several charts demonstrating how Yao graphs behave for a set of randomly chosen point set. As the results show, the stretch factor of a Yao graph follows a normal distribution. Furthermore, the stretch factor is in average far less than the worst case stretch factor proved for Yao graphs in previous results. Furthermore, we use Yao graph for a realistic point set and study its stretch factor in real world.
Abstract: The present work proposes the development of an adaptive control system which enables the suppression of Pilot Induced Oscillations (PIO) in Digital Fly-By-Wire (DFBW) aircrafts. The proposed system consists of a Modified Model Reference Adaptive Control (M-MRAC) integrated with the Gain Scheduling technique. The PIO oscillations are detected using a Real Time Oscillation Verifier (ROVER) algorithm, which then enables the system to switch between two reference models; one in PIO condition, with low proneness to the phenomenon and another one in normal condition, with high (or medium) proneness. The reference models are defined in a closed loop condition using the Linear Quadratic Regulator (LQR) control methodology for Multiple-Input-Multiple-Output (MIMO) systems. The implemented algorithms are simulated in software implementations with state space models and commercial flight simulators as the controlled elements and with pilot dynamics models. A sequence of pitch angles is considered as the reference signal, named as Synthetic Task (Syntask), which must be tracked by the pilot models. The initial outcomes show that the proposed system can detect and suppress (or mitigate) the PIO oscillations in real time before it reaches high amplitudes.
Abstract: During the last 10 years, in spite of the economic crisis, the number of tourists arriving in Greece has increased, particularly during the tourist season from April to October. In this paper, the number of annual tourist arrivals is studied to explore their preferences with regard to the month of travel, the selected destinations, as well the amount of money spent. The collected data are processed with statistical methods, yielding numerical and graphical results. From the computation of statistical parameters and the forecasting with exponential smoothing, useful conclusions are arrived at that can be used by the Greek tourism authorities, as well as by tourist organizations, for planning purposes for the coming years. The results of this paper and the computed forecast can also be used for decision making by private tourist enterprises that are investing in Greece. With regard to the statistical methods, the method of Simple Exponential Smoothing of time series of data is employed. The search for a best forecast for 2017 and 2018 provides the value of the smoothing coefficient. For all statistical computations and graphics Microsoft Excel is used.
Abstract: Construction industry plays a significant role in fulfilling the major requirements of the human being. It is one of the major constituents of every developed country. Although the construction industry of both the developing and developed countries encompasses a major part of the economy, and millions of rupees are utilized every year on various kinds of construction projects. But, this industry is facing numerous hurdles in terms of its budget and timely completion. Construction projects generally consist of several phases like planning, designing, execution, and finishing. This research study aims to determine the significant factors of time overrun in pre-construction planning (PCP) phase of construction projects in Pakistan. Questionnaires were distributed by various means and responses of respondents were compiled and collected data were then analyzed through a statistical technique using SPSS version 24. Major causes of time overrun in pre-construction planning phase; which is an extremely important phase of construction projects, were revealed. The research conclusion will provide a pathway for stakeholders to pay attention to the mentioned causes to overcome the major issue of time overrun.
Abstract: The purpose of this paper is to examine the relationship between the fashion corporate social responsibility (CSR) ad framing and consumer purchase behaviours with the focus on consumer’s concern and involvement towards fashion brands. A self-completion questionnaire was administered to 200 respondents. Factor analysis and other statistical analyses were applied to test hypotheses. The results suggested that the quality of the product was the most important factor when consumers purchase fashion brand products with high level of responsibility towards unethical practices but surprisingly favourability for fast fashion. Unexpectedly, it was shown that consumers took the plenty of blame, but not much responsibility on buying fast fashion evading their responsibility to CSR ad, and their purchase intentions remained unchanged. The result, on the other hand, showed that fashion CSR ads can significantly moderate individuals’ emotions even though this had no significant correlation with the purchase intentions. Despite the limited sample size and geographical region, this research has important implications for contemporary fashion brands that use ad framing to understand how consumers’ involvement and concernedness toward the CSR actions in ad, influence their favourability (purchase intention) for fashion brands.
Abstract: A growing list of cancers might be influenced by obesity. Obesity is associated with an increased risk for the occurrence and development of some cancers. Inflammation can lead to cancer. It is one of the characteristic features of cancer and plays a critical role in cancer development. C-reactive protein (CRP) is under evaluation related to the new and simple prognostic factors in patients with metastatic renal cell cancer. Obesity can predict and promote systemic inflammation in healthy adults. BMI is correlated with hs-CRP. In this study, SII index and CRP values were evaluated in children with normal BMI and those within the range of different obesity grades to detect the tendency towards cancer in pediatric obesity. A total of one hundred and ninety-four children; thirty-five children with normal BMI, twenty overweight (OW), forty-seven obese (OB) and ninety-two morbid obese (MO) participated in the study. Age- and sex-matched groups were constituted using BMI-for age percentiles. Informed consent was obtained. Ethical Committee approval was taken. Weight, height, waist circumference (C), hip C, head C and neck C of the children were measured. The complete blood count test was performed. C-reactive protein analysis was performed. Statistical analyses were performed using SPSS. The degree for statistical significance was p≤0.05. SII index values were progressively increasing starting from normal weight (NW) to MO children. There is a statistically significant difference between NW and OB as well as MO children. No significant difference was observed between NW and OW children, however, a correlation was observed between NW and OW children. MO constitutes the only group, which exhibited a statistically significant correlation between SII index and CRP. Obesity-related bladder, kidney, cervical, liver, colorectal, endometrial cancers are still being investigated. Obesity, characterized as a chronic low-grade inflammation, is a crucial risk factor for colon cancer. Elevated childhood BMI values may be indicative of processes leading to cancer, initiated early in life. Prevention of childhood adiposity may decrease the cancer incidence in adults. To authors’ best knowledge, this study is the first to introduce SII index values during obesity of varying degrees of severity. It is suggested that this index seems to affect all stages of obesity with an increasing tendency and may point out the concomitant status of obesity and cancer starting from very early periods of life.
Abstract: Ambient air pollution with fine particulate matter (PM10) is a systematic permanent problem in many countries around the world. The accumulation of a large number of measurements of both the PM10 concentrations and the accompanying atmospheric factors allow for their statistical modeling to detect dependencies and forecast future pollution. This study applies the classification and regression trees (CART) method for building and analyzing PM10 models. In the empirical study, average daily air data for the city of Pleven, Bulgaria for a period of 5 years are used. Predictors in the models are seven meteorological variables, time variables, as well as lagged PM10 variables and some lagged meteorological variables, delayed by 1 or 2 days with respect to the initial time series, respectively. The degree of influence of the predictors in the models is determined. The selected best CART models are used to forecast future PM10 concentrations for two days ahead after the last date in the modeling procedure and show very accurate results.