Abstract: Structural design and analysis is an important and time-consuming process, particularly at the conceptual design stage. Decisions made at this stage can have an enormous effect on the entire project, as it becomes ever costlier and more difficult to alter the choices made early on in the construction process. Hence, optimisation of the early stages of structural design can provide important efficiencies in terms of cost and time. This paper suggests a structural design optimisation (SDO) framework in which Genetic Algorithms (GAs) may be used to semi-automate the production and optimisation of early structural design alternatives. This framework has the potential to leverage conceptual structural design innovation in Architecture, Engineering and Construction (AEC) projects. Moreover, this framework improves the collaboration between the architectural stage and the structural stage. It will be shown that this SDO framework can make this achievable by generating the structural model based on the extracted data from the architectural model. At the moment, the proposed SDO framework is in the process of validation, involving the distribution of an online questionnaire among structural engineers in the UK.
Abstract: This paper studies in details about the need of quadcopter in various fields especially in the place of remote area where the road transportation facility is very less. It is used to monitor and collect data in a specific region. The movement of this quadcopter is controlled by the Raspberry Pi. FPV camera is used for capturing the image and will transmit the image to the receiver which can be monitored using an android smart phone. This is mainly used for surveillance purpose and hidden activities can be captured.
Abstract: Globular clusters (GC) are important objects for tracing
the early evolution of a galaxy. We study the correlation between the
cluster population and the global properties of the host galaxy. We
found that the correlation between cluster population (NGC) and
the baryonic mass (Mb) of the host galaxy are best described as
10
−5.6038Mb. In order to understand the origin of the U -shape
relation between the GC specific frequency (SN) and Mb (caused
by the high value of SN for dwarfs galaxies and giant ellipticals
and a minimum SN for intermediate mass galaxies≈ 1010M), we
derive a theoretical model for the specific frequency (SNth). The
theoretical model for SNth is based on the slope of the power-law
embedded cluster mass function (β) and different time scale (Δt) of
the forming galaxy. Our results show a good agreement between the
observation and the model at a certain β and Δt. The model seems
able to reproduce higher value of SNth of β = 1.5 at the midst
formation time scale.
Abstract: High-density polyethylene reinforced with carbon nanofibers (HDPE/CNF) have been prepared via melt processing using dialkylimidazolium tetrafluoroborate (ionic liquid) as a dispersion agent. The prepared samples were characterized by thermogravimetric (TGA) and differential scanning calorimetric (DSC) analyses. The samples blended with imidazolium ionic liquid exhibit higher thermal stability. DSC analysis showed clear miscibility of ionic liquid in the HDPE matrix and showed single endothermic peak. The melt rheological analysis of HDPE/CNF composites was performed using an oscillatory rheometer. The influence of CNF and ionic liquid concentration (ranging from 0, 0.5, and 1 wt%) on the viscoelastic parameters was investigated at 200 °C with an angular frequency range of 0.1 to 100 rad/s. The rheological analysis shows the shear-thinning behavior for the composites. An improvement in the viscoelastic properties was observed as the nanofiber concentration increases. The progress in the modulus values was attributed to the structural rigidity imparted by the high aspect ratio CNF. The modulus values and complex viscosity of the composites increased significantly at low frequencies. Composites blended with ionic liquid exhibit slightly lower values of complex viscosity and modulus over the corresponding HDPE/CNF compositions. Therefore, reduction in melt viscosity is an additional benefit for polymer composite processing as a result of wetting effect by polymer-ionic liquid combinations.
Abstract: Grape seeds are waste products of wineries and often referred to as an important agricultural and industrial waste product with the potential to be used in pharmaceutical, food, and cosmetic applications. In this study, grape seed oil from traditional Ionian varieties was examined for the determination of the quality and the characteristics of each variety. Initially, the fatty acid methyl ester (FAME) profiles were analyzed using Gas Chromatography-Mass Spectrometry, after transesterification. Furthermore, other quality parameters of the grape seed oils were determined by Spectroscopy techniques, UV-Vis and Raman included. Moreover, the antioxidant capacity of the oil was measured by 2,2'-azino-bis-3-ethylbenzothiazoline-6-sulfonic acid (ABTS) and 2,2-Diphenyl-1-picrylhydrazyl (DPPH) assays and their antioxidant capacity expressed in Trolox equivalents. K and ΔΚ indices were measured in 232, 268, 270 nm, as an oil quality index. The results indicate that the air-dried grape seed total oil content ranged from 5.26 to 8.77% w/w, which is in accordance with the other grape seed varieties tested in similar studies. The composition of grape seed oil is predominated with linoleic and oleic fatty acids, with the linoleic fatty acid ranging from 53.68 to 69.95% and both the linoleic and oleic fatty acids totaling 78-82% of FAMEs, which is analogous to the fatty acid composition of safflower oil. The antioxidant assays ABTS and DPPH scored high, exhibiting that the oils have potential in the cosmetic and culinary businesses. Above that, our results demonstrate that Ionian grape seed oils have prospects that can go further than cosmetic or culinary use, into the pharmaceuticals industry. Finally, the reclamation of grape seeds from wineries waste stream is in accordance with the bio-economy strategic framework and contributes to environmental protection.
Abstract: While struggling to succeed in today’s complex market environment and provide better customer experience and services, enterprises encompass digital transformation as a means for reaching competitiveness and foster value creation. A digital transformation process consists of information technology implementation projects, as well as organizational factors such as top management support, digital transformation strategy, and organizational changes. However, to the best of our knowledge, there is little evidence about digital transformation endeavors in organizations and how they perceive it – is it only about digital technologies adoption or a true organizational shift is needed? In order to address this issue and as the first step in our research project, a literature review is conducted. The analysis included case study papers from Scopus and Web of Science databases. The following attributes are considered for classification and analysis of papers: time component; country of case origin; case industry and; digital transformation concept comprehension, i.e. focus. Research showed that organizations – public, as well as private ones, are aware of change necessity and employ digital transformation projects. Also, the changes concerning digital transformation affect both manufacturing and service-based industries. Furthermore, we discovered that organizations understand that besides technologies implementation, organizational changes must also be adopted. However, with only 29 relevant papers identified, research positioned digital transformation as an unexplored and emerging phenomenon in information systems research. The scarcity of evidence-based papers calls for further examination of this topic on cases from practice.
Abstract: Digital transformation is one of the latest trends on the global market. In order to maintain the competitive advantage and sustainability, increasing number of organizations are conducting digital transformation processes. Those organizations are changing their business processes and creating new business models with the help of digital technologies. In that sense, one should also observe the role of business process management (BPM) and its maturity in driving digital transformation. Therefore, the goal of this paper is to investigate the role of BPM in digital transformation process within one organization. Since experiences from practice show that organizations from financial sector could be observed as leaders in digital transformation, an insurance company has been selected to participate in the study. That company has been selected due to the high level of its BPM maturity and the fact that it has previously been through a digital transformation process. In order to fulfill the goals of the paper, several interviews, as well as questionnaires, have been conducted within the selected company. The results are presented in a form of a case study. Results indicate that digital transformation process within the observed company has been successful, with special focus on the development of digital strategy, BPM and change management. The role of BPM in the digital transformation of the observed company is further discussed in the paper.
Abstract: This study investigates the perceptual features of Japanese obstruent geminates among Chinese learners of Japanese, focusing on the dialectal effect of the checked-tone, a syllable that ends in a stop consonant or a glottal stop, which is similar to Japanese obstruent geminates phonetically. In this study, 41 native speakers of Cantonese are divided into two groups based on their proficiency as well as learning period of Japanese. All stimuli employed in this study are made into C[p,k,s]+V[a,e,i] structure such as /apa/, /eke/, /isi/. Both original sounds and synthesized sounds are used in three different parts of this study. The results of the present study show that the checked-tone does have the positive effect on the perception of Japanese gemination. Furthermore, the proportion of closure duration in the entire word would be a more reliable and appropriate criterion in testing this kind of task.
Abstract: In this paper, the combined free and forced convection heat transfer of the Copper Oxide-Heat Transfer Oil (CuO-HTO) nanofluid flow in horizontal and inclined microfin tubes is studied experimentally. The flow regime is laminar, and pipe surface temperature is constant. The effect of nanoparticle and microfin tube on the heat transfer rate is investigated with the Richardson number which is between 0.1 and 0.7. The results show an increasing nanoparticle concentration between 0% and 1.5% leads to enhance the combined free and forced convection heat transfer rate. According to the results, five correlations are proposed to provide estimating the free and forced heat transfer rate as the increasing Richardson number from 0.1 to 0.7. The maximum deviation of both correlations is less than 16%. Moreover, four correlations are suggested to assess the Nusselt number based on the Rayleigh number in inclined tubes from 1800000 to 7000000. The maximum deviation of the correlation is almost 16%. The Darcy friction factor of the nanofluid flow has been investigated. Furthermore, CuO-HTO nanofluid flows in inclined microfin tubes.
Abstract: Focus on reducing energy consumption in existing
buildings at large scale, e.g. in cities or countries, has been
increasing in recent years. In order to reduce energy consumption
in existing buildings, political incentive schemes are put in place and
large scale investments are made by utility companies. Prioritising
these investments requires a comprehensive overview of the energy
consumption in the existing building stock, as well as potential
energy-savings. However, a building stock comprises thousands
of buildings with different characteristics making it difficult to
model energy consumption accurately. Moreover, the complexity of
the building stock makes it difficult to convey model results to
policymakers and other stakeholders. In order to manage the complexity of the building stock, building
archetypes are often employed in building stock energy models
(BSEMs). Building archetypes are formed by segmenting the building
stock according to specific characteristics. Segmenting the building
stock according to building type and building age is common, among
other things because this information is often easily available. This
segmentation makes it easy to convey results to non-experts. However, using a single archetypical building to represent all
buildings in a segment of the building stock is associated with
loss of detail. Thermal characteristics are aggregated while other
characteristics, which could affect the energy efficiency of a building,
are disregarded. Thus, using a simplified representation of the
building stock could come at the expense of the accuracy of the
model. The present study evaluates the accuracy of a conventional
archetype-based BSEM that segments the building stock according
to building type- and age. The accuracy is evaluated in terms of the
archetypes’ ability to accurately emulate the average energy demands
of the corresponding buildings they were meant to represent. This is
done for the buildings’ energy demands as a whole as well as for
relevant sub-demands. Both are evaluated in relation to the type- and
the age of the building. This should provide researchers, who use
archetypes in BSEMs, with an indication of the expected accuracy
of the conventional archetype model, as well as the accuracy lost in
specific parts of the calculation, due to use of the archetype method.
Abstract: Knee collateral ligaments play a significant role in restraining excessive frontal motion (varus/valgus rotations). In this investigation, a multiscale frame was developed based on structural hierarchies of the collateral ligaments starting from the bottom (tropocollagen molecule) to up where the fibred reinforced structure established. Experimental data of failure tensile test were considered as the principal driver of the developed model. This model was calibrated statistically using Bayesian calibration due to the high number of unknown parameters. Then the model is scaled up to fit the real structure of the collateral ligaments and simulated under realistic boundary conditions. Predications have been successful in describing the observed transient response of the collateral ligaments during tensile test under pre- and post-damage loading conditions. Collateral ligaments maximum stresses and strengths were observed near to the femoral insertions, a results that is in good agreement with experimental investigations. Also for the first time, damage initiation and propagation were documented with this model as a function of the cross-link density between tropocollagen molecules.
Abstract: In the present study, the reactions involving 4-bromo-2,6-bis-hydroxymethyl-phenol (BBHMP) and nicotinamide (NA) in the presence of Pd(II) ions were investigated. Optimum conditions for the reactions were established as pH = 7 and λ = 450 nm. According to absorbance measurements, the molar ratio of BBHMP: NA: Pd2+ was found to be 1: 2: 2. As a result of physicochemical, spectrophotometric and thermal analyses, the reactions of BBHMP and NA with Pd(II) are complexation reactions and one molecule of BBHMP and two molecules of NA react with two molecules of the Pd(II) ion.
Abstract: One of the major shortcomings of widely used
scientometric indicators is that different disciplines cannot be
compared with each other. The issue of cross-disciplinary
normalization has been long discussed, but even the classification
of publications into scientific domains poses problems. Structural
properties of citation networks offer new possibilities, however, the
large size and constant growth of these networks asks for precaution.
Here we present a new tool that in order to perform cross-field
normalization of scientometric indicators of individual publications
relays on the structural properties of citation networks. Due to the
large size of the networks, a systematic procedure for identifying
scientific domains based on a local community detection algorithm
is proposed. The algorithm is tested with different benchmark
and real-world networks. Then, by the use of this algorithm, the
mechanism of the scientometric indicator normalization process is
shown for a few indicators like the citation number, P-index and
a local version of the PageRank indicator. The fat-tail trend of the
article indicator distribution enables us to successfully perform the
indicator normalization process.
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: This paper presents parameter estimation of a
single-phase rectifier using extended Kalman filter (EKF). The state
space model has been obtained using Kirchhoff’s current law (KCL)
and Kirchhoff’s voltage law (KVL). The capacitor voltage and diode
current of the circuit have been estimated using EKF. Simulation
results validate the better accuracy of the proposed method as
compared to the least mean square method (LMS). Further, EKF
has the advantage that it can be used for nonlinear systems.
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: Many research projects require accurate delineation of the different land cover type of the agricultural area. Especially it is critically important for the definition of specific plants like cannabis. However, the complexity of vegetation stands structure, abundant vegetation species, and the smooth transition between different seconder section stages make vegetation classification difficult when using traditional approaches such as the maximum likelihood classifier. Most of the time, classification distinguishes only between trees/annual or grain. It has been difficult to accurately determine the cannabis mixed with other plants. In this paper, a mixed distribution models approach is applied to classify pure and mix cannabis parcels using Worldview-2 imagery in the Lakes region of Turkey. Five different land use types (i.e. sunflower, maize, bare soil, and cannabis) were identified in the image. A constrained Gaussian mixture discriminant analysis (GMDA) was used to unmix the image. In the study, 255 reflectance ratios derived from spectral signatures of seven bands (Blue-Green-Yellow-Red-Rededge-NIR1-NIR2) were randomly arranged as 80% for training and 20% for test data. Gaussian mixed distribution model approach is proved to be an effective and convenient way to combine very high spatial resolution imagery for distinguishing cannabis vegetation. Based on the overall accuracies of the classification, the Gaussian mixed distribution model was found to be very successful to achieve image classification tasks. This approach is sensitive to capture the illegal cannabis planting areas in the large plain. This approach can also be used for monitoring and determination with spectral reflections in illegal cannabis planting areas.
Abstract: The study of static dielectric properties in a binary mixture of 1,2 dichloroethane (DE) and n,n dimethylformamide (DMF) polar liquids has been carried out in the frequency range of 10 MHz to 30 GHz for 11 different concentration using time domain reflectometry technique at 10ºC temperature. The dielectric relaxation study of solute-solvent mixture at microwave frequencies gives information regarding the creation of monomers and multimers as well as interaction between the molecules of the binary mixture. The least squares fit method is used to determine the values of dielectric parameters such as static dielectric constant (ε0), dielectric constant at high frequency (ε∞) and relaxation time (τ).
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%.