Search results for: critical features

Authors: Mustafa Saeed Omar

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The formula which contains the maximum increase of mean bond length, melting entropy and critical particle radius is used to calculate lattice volume in nanoscale size crystals of Ga₂Se₃. This compound belongs to the binary group of III₂VI₃. The critical radius is calculated from the values of the first surface atomic layer height which is equal to 0.336nm. The size-dependent mean bond length is calculated by using an equation-free from fitting parameters. The size-dependent lattice parameter then is accordingly used to calculate the size-dependent lattice volume. The lattice size in the nanoscale region increases to about 77.6 A³, which is up to four times of its bulk state value 19.97 A³. From the values of the nanosize scale dependence of lattice volume, the nanoscale size dependence of melting temperatures is calculated. The melting temperature decreases with the nanoparticles size reduction, it becomes zero when the radius reaches to its critical value. Bulk melting temperature for Ga₂Se₃, for example, has values of 1293 K. From the size-dependent melting temperature and mean bond length, the size-dependent lattice thermal expansion is calculated. Lattice thermal expansion decreases with the decrease of nanoparticles size and reaches to its minimum value as the radius drops down to about 5nm.

Keywords: Ga₂Se₃, lattice volume, lattice thermal expansion, melting point, nanoparticles

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Authors: Ahmed Amin Mousa, Mona Yacoub

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This study depends on tracking children style through what they have drawn after being introduced to 16 visual content through National Geographic Abu Dhabi Channel programs and the study of the changing features in their drawings before applying the visual act with them. The researchers used Goodenough-Harris Test to analyse children drawings and to extract the features which changed in their drawing before and after the visual content. The results showed a positive change especially in the shapes of animals and their properties. Children become more aware of animals’ shapes. The study sample was 220 kindergarten children divided into 130 girls and 90 boys at the Orman Experimental Language School in Dokki, Giza, Egypt. The study results showed an improvement in children drawing with 85% than they were before watching videos.

Keywords: National Geographic, children drawing, kindergarten, Goodenough-Harris Test

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Authors: V. Batishchev, V. Getman

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We study the self-similar fluid flows in the Marangoni layers with the axial symmetry. Such flows are induced by the radial gradients of the temperatures whose distributions along the free boundary obey some power law. The self-similar solutions describe thermo-capillar flows both in the thin layers and in the case of infinite thickness. We consider both positive and negative temperature gradients. In the former case the cooling of free boundary nearby the axis of symmetry gives rise to the rotation of fluid. The rotating flow concentrates itself inside the Marangoni layer while outside of it the fluid does not revolve. In the latter case we observe no rotating flows at all. In the layers of infinite thickness the separation of the rotating flow creates two zones where the flows are directed oppositely. Both the longitudinal velocity and the temperature have exactly one critical point inside the boundary layer. It is worth to note that the profiles are monotonic in the case of non-swirling flows. We describe the flow outside the boundary layer with the use of self-similar solution of the Euler equations. This flow is slow and non-swirling. The introducing of an outer flow gives rise to the branching of swirling flows from the non-swirling ones. There is such the critical velocity of the outer flow that a non-swirling flow exists for supercritical velocities and cannot be extended to the sub-critical velocities. For the positive temperature gradients there are two non-swirling flows. For the negative temperature gradients the non-swirling flow is unique. We determine the critical velocity of the outer flow for which the branching of the swirling flows happens. In the case of a thin layer confined within free boundaries we show that the cooling of the free boundaries near the axis of symmetry leads to the separating of the layer and creates two sub-layers with opposite rotations inside. This makes sharp contrast with the case of infinite thickness. We show that such rotation arises provided the thickness of the layer exceed some critical value. In the case of a thin layer confined within free and rigid boundaries we construct the branching equation and the asymptotic approximation for the secondary swirling flows near the bifurcation point. It turns out that the bifurcation gives rise to one pair of the secondary swirling flows with different directions of swirl.

Keywords: free surface, rotation, fluid flow, bifurcation, boundary layer, Marangoni layer

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Authors: Crescentia Ugwuona

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Appropriate language use has been central and critical in advancing education and human rights for women and girls in many countries the world over. Unfortunately, these lofty aims have often been violated by rural Igbo-Nigerians as they use stereotyping and dehumansing language in their cultural songs against women and girls. The psychological impact of the songs has a significant negative impact on education, human rights, quality of life, and opportunities for many rural Igbo-women and girls in Nigeria. This study, therefore, examines the forms, shades, and manifestations of derogatory and stereotypical language against women and girls the Igbo cultural songs; and how they impede education and human rights for females in Nigeria. Through Critical discourse analysis (CDA) of data collected via recording, the study identifies manifestations of women and girls’ stereotypes such as subjugations, male dominance, inequality in gender roles, suppression, and oppression, and derogatory use of the language against women and girls in the Igbo cultural songs. This study has a great promise of alerting the issues of derogatory and stereotypical language in songs, and contributes to an education aimed at gender equality, emancipator practice of appropriate language use in songs, equal education and human rights for both male and female, respect and solidarity in Nigeria and beyond.

Keywords: gender stereotypes, cultural songs, women and girls, language use in Nigeria, critical discourse analysis, CDA, education

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Authors: B. S. Harish, M. B. Revanasiddappa

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Due to rapid growth of text documents in digital form, automated text classification has become an important research in the last two decades. The major challenge of text document representations are high dimension, sparsity, volume and semantics. Since the terms are only features that can be found in documents, selection of good terms (features) plays an very important role. In text classification, feature selection is a strategy that can be used to improve classification effectiveness, computational efficiency and accuracy. In this paper, we present a quantitative analysis of most widely used feature selection (FS) methods, viz. Term Frequency-Inverse Document Frequency (tf
idf ), Mutual Information (MI), Information Gain (IG), CHISquare (x2), Term Frequency-Relevance Frequency (tf
rf ), Term Strength (TS), Ambiguity Measure (AM) and Symbolic Feature Selection (SFS) to classify text documents. We evaluated all the feature selection methods on standard datasets like 20 Newsgroups, 4 University dataset and Reuters-21578.

Keywords: classifiers, feature selection, text classification

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Authors: Chirawat Wattanapanich, Hong Wei

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This study presents new gait representations for improving gait recognition accuracy on cross gait appearances, such as normal walking, wearing a coat and carrying a bag. Based on the Gait Energy Image (GEI), two ideas are implemented to generate new gait representations. One is to append lower knee regions to the original GEI, and the other is to apply convolutional operations to the GEI and its variants. A set of new gait representations are created and used for training multi-class Support Vector Machines (SVMs). Tests are conducted on the CASIA dataset B. Various combinations of the gait representations with different convolutional kernel size and different numbers of kernels used in the convolutional processes are examined. Both the entire images as features and reduced dimensional features by Principal Component Analysis (PCA) are tested in gait recognition. Interestingly, both new techniques, appending the lower knee regions to the original GEI and convolutional GEI, can significantly contribute to the performance improvement in the gait recognition. The experimental results have shown that the average recognition rate can be improved from 75.65% to 87.50%.

Keywords: convolutional image, lower knee, gait

Procedia PDF Downloads 187

Authors: Subham Kharel, Sudha Ravindranath, A. Vidya, B. Chandrasekaran, K. Ganesha Raj, T. Shesadri

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Digitization of urban features is a tedious and time-consuming process when done manually. In addition to this problem, Indian cities have complex habitat patterns and convoluted clustering patterns, which make it even more difficult to map features. This paper makes an attempt to classify urban objects in the satellite image using object-oriented classification techniques in which various classes such as vegetation, water bodies, buildings, and shadows adjacent to the buildings were mapped semi-automatically. Building layer obtained as a result of object-oriented classification along with already available building layers was used. The main focus, however, lay in the extraction of high-rise buildings using spatial technology, digital image processing, and modeling, which would otherwise be a very difficult task to carry out manually. Results indicated a considerable rise in the total number of buildings in the city. High-rise buildings were successfully mapped using satellite imagery, spatial technology along with logical reasoning and mathematical considerations. The results clearly depict the ability of Remote Sensing and GIS to solve complex problems in urban scenarios like studying urban sprawl and identification of more complex features in an urban area like high-rise buildings and multi-dwelling units. Object-Oriented Technique has been proven to be effective and has yielded an overall efficiency of 80 percent in the classification of high-rise buildings.

Keywords: object oriented classification, shadow extraction, high-rise buildings, satellite imagery, spatial technology

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Authors: Mehak Masood

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Education is critical for promoting sustainable development and improving the capacity of people to address environment and development issues. It is also critical for achieving environmental and ethical awareness, values and attitudes, skills and behaviour consistent with sustainable development and for effective public participation in decision-making. In this regard, The British Council Pakistan have conducted a need assessment study conducted during the training sessions with three different groups of educationists belonging to both government and public sectors on the topic of Climate Change and Environmental Education (CCEE). This study aims to review perceptions about climate change and environmental education and analyze its need and importance according to educationists of Pakistan.

Keywords: environmental education, climate change, resilience development, awareness

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Authors: Davide Fuscà, Kanan Rahimli, Roberto Leuzzi

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Preventing abuse and illegal activities in a given area of the sea is a very difficult and expensive task. Artificial intelligence offers the possibility to implement new methods to identify the vessel class type from the kinematic features of the vessel itself. The task strictly depends on the quality of the data. This paper explores the application of a deep, long short-term memory model by using AIS flow only with a relatively low quality. The proposed model reaches high accuracy on detecting nine vessel classes representing the most common vessel types in the Ionian-Adriatic Sea. The model has been applied during the Adriatic-Ionian trial period of the international EU ANDROMEDA H2020 project to identify vessels performing behaviors far from the expected one depending on the declared type.

Keywords: maritime surveillance, artificial intelligence, behavior analysis, LSTM

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Authors: C. K. Peter Chuah

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Essentially, the intention of reflective journal is meant for students to develop higher-order thinking skills and to provide a 'space' to make their learning experience and thinking, making and feeling visible, i.e., it provides students an opportunity to evaluate their learning critically by focusing on the rationale behind their thinking, making and feeling. In addition, reflective journal also gets the students to focus on how could things be done differently—the possibility, alternative point of views, and opportunities for change. It is hoped that by getting communication design students to reflect at various intervals, they could move away from mere working on the design project and pay more attention to what they thought they have learned in relation to the development of their design ability. Unfortunately, a closer examination—through content analysis—of the learning journals submitted by a group of design students revealed that most of the reflections were descriptive and tended to be a summary of what occurred in the learning experience. While many students were able to describe what they did, very few were able to explain how they were able to do something critically. It can be concluded that to get design students to reflect is a fairly easy task, but to get them to reflect critically could be very challenging. To ensure that design students could benefit from the use of reflective journal as a tool to develop their critical thinking skills, a more systematic and structured approach to the introduction of critical thinking and reflective journal should be built into the design curriculum to provide as much practice and sufficient feedback as other studio subjects.

Keywords: communication design education, critical thinking, reflection, reflective journal

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Authors: Fredrick Ishengoma, Leonard Mselle, Hector Mongi

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The growing number of mobile phone subscribers in Tanzania offers the government a new channel for the delivery of information and government services to citizens, thus mobile Government (m-Government). In Tanzania, m-Government services usage is in the early stages, and factors that influence its adoption are yet to be known. This study seeks to identify and understand the critical success factors (CSFs) that influence citizens’ behavioural intention (BI) to adopt m-Government services in Tanzania. The study employed the mobile services acceptance model (MSAM) and extends it with external factors relevant in the Tanzanian context. A survey questionnaire was used to collect primary data from users of m-Government services in Dar es salaam and Dodoma cities, and 253 responses were received. Data were analyzed by IBM-SPSS AMOS 23.0 software using structural equation modeling (SEM). The findings of the study indicate that perceived usefulness, trust, perceived mobility, power distance, quality of service, awareness, perceived cost, personal initiatives, and characteristics significantly influence the BI to adopt m-Government services. However, perceived ease of use was found statistically insignificant to predict BI. Furthermore, the interplay between CSFs, discussion on theoretical and practical implications that follow from the results are presented.

Keywords: adoption, critical success factors, structural equation modeling, m-Government, MSAM, Tanzania

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Authors: Laicheng Cao, Xiangqian Su, Youxiao Wu

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Feature selection is one of the important problems in network security, pattern recognition, data mining and other fields. In order to remove redundant features, effectively improve the detection speed of intrusion detection system, proposes a new feature selection method, which is based on the invasive weed optimization (IWO) algorithm and tabu search algorithm(TS). Use IWO as a global search, tabu search algorithm for local search, to improve the results of IWO algorithm. The experimental results show that the feature selection method can effectively remove the redundant features of network data information in feature selection, reduction time, and to guarantee accurate detection rate, effectively improve the speed of detection system.

Keywords: intrusion detection, feature selection, iwo, tabu search

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Authors: Yoshinori Wakatake

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Many defects after the release of software packages are caused due to omissions of sufficient test items in test specifications. Poor test specifications are detected by manual review, which imposes a high human load. The prevention of omissions depends on the end-user awareness of test specification writers. If test specifications were written while envisioning the behavior of end-users, the number of omissions in test items would be greatly reduced. The paper pays attention to the point that writers who can achieve it differ from those who cannot in not only the description richness but also their gaze information. It proposes a method to estimate the degree of user-awareness of writers through the analysis of their gaze information when writing test specifications. We conduct an experiment to obtain the gaze information of a writer of the test specifications. Test specifications are automatically classified using gaze information. In this method, a Random Forest model is constructed for the classification. The classification is highly accurate. By looking at the explanatory variables which turn out to be important variables, we know behavioral features to distinguish test specifications of high quality from others. It is confirmed they are pupil diameter size and the number and the duration of blinks. The paper also investigates test specifications automatically classified with gaze information to discuss features in their writing ways in each quality level. The proposed method enables us to automatically classify test specifications. It also prevents test item omissions, because it reveals writing features that test specifications of high quality should satisfy.

Keywords: blink, eye tracking, gaze information, pupil diameter, quality improvement, specification document, user-awareness

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Authors: Mohd Iz'aan Paiz Bin Zamri, Anis Salwa Mohd Khairuddin, Norrima Mokhtar, Rubiyah Yusof

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Effective statistical feature extraction and classification are important in image-based automatic inspection and analysis. An automatic wood species recognition system is designed to perform wood inspection at custom checkpoints to avoid mislabeling of timber which will results to loss of income to the timber industry. The system focuses on analyzing the statistical pores properties of the wood images. This paper proposed a fuzzy-based feature extractor which mimics the experts’ knowledge on wood texture to extract the properties of pores distribution from the wood surface texture. The proposed feature extractor consists of two steps namely pores extraction and fuzzy pores management. The total number of statistical features extracted from each wood image is 38 features. Then, a backpropagation neural network is used to classify the wood species based on the statistical features. A comprehensive set of experiments on a database composed of 5200 macroscopic images from 52 tropical wood species was used to evaluate the performance of the proposed feature extractor. The advantage of the proposed feature extraction technique is that it mimics the experts’ interpretation on wood texture which allows human involvement when analyzing the wood texture. Experimental results show the efficiency of the proposed method.

Keywords: classification, feature extraction, fuzzy, inspection system, image analysis, macroscopic images

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Authors: Mark Harmon, Abdolghani Ebrahimi, Patrick Lucey, Diego Klabjan

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In this paper, we predict the likelihood of a player making a shot in basketball from multiagent trajectories. Previous approaches to similar problems center on hand-crafting features to capture domain-specific knowledge. Although intuitive, recent work in deep learning has shown, this approach is prone to missing important predictive features. To circumvent this issue, we present a convolutional neural network (CNN) approach where we initially represent the multiagent behavior as an image. To encode the adversarial nature of basketball, we use a multichannel image which we then feed into a CNN. Additionally, to capture the temporal aspect of the trajectories, we use “fading.” We find that this approach is superior to a traditional FFN model. By using gradient ascent, we were able to discover what the CNN filters look for during training. Last, we find that a combined FFN+CNN is the best performing network with an error rate of 39%.

Keywords: basketball, computer vision, image processing, convolutional neural network

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Authors: Alireza Mirrashid, Mohammad Khoshbin, Ali Atghaei, Hassan Shahbazi

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In various industries, smoke and fire are two of the most important threats in the workplace. One of the common methods for detecting smoke and fire is the use of infrared thermal and smoke sensors, which cannot be used in outdoor applications. Therefore, the use of vision-based methods seems necessary. The problem of smoke and fire detection is spatiotemporal and requires spatiotemporal solutions. This paper presents a method that uses spatial features along with temporal-based features to detect smoke and fire in the scene. It consists of three main parts; the task of each part is to reduce the error of the previous part so that the final model has a robust performance. This method also uses transformer modules to increase the accuracy of the model. The results of our model show the proper performance of the proposed approach in solving the problem of smoke and fire detection and can be used to increase workplace safety.

Keywords: attention, fire detection, smoke detection, spatio-temporal

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Authors: Inderpreet Kaur, Sukhjit Kaur, Balwinder Singh Sohi

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The design proposed in this paper mainly focuses on implementation of a single feed compact rectangular microstrip patch antenna (MSA) for single band application. The antenna presented here also works in dual band but its best performance has been obtained when optimised to work in single band mode. In this paper, a new feeding structure is applied in the patch antenna design to overcome undesirable features of the earlier multilayer feeding structures while maintaining their interesting features.To make the proposed antenna more efficient the optimization of the antenna design parameters have been done using HFSS’s optometric. For the proposed antenna one resonant frequency has been obtained at 6.03GHz, with Bandwidth of 167MHz and return loss of -33.82db. The characteristics of the designed structure are investigated by using FEM based electromagnetic solver.

Keywords: bandwidth, retun loss, parasitic lines, microstrip antenna

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Authors: Bubai Maji, Monorama Swain

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Speech is considered an essential and most natural medium for the interaction between machines and humans. However, extracting effective features for speech emotion recognition (SER) is remains challenging. The present studies show that the temporal information captured but high-level temporal-feature learning is yet to be investigated. In this paper, we present an efficient novel method using the Self-attention (SA) mechanism in a combination of Convolutional Neural Network (CNN) and Bi-directional Gated Recurrent Unit (Bi-GRU) network to learn high-level temporal-feature. In order to further enhance the representation of the high-level temporal-feature, we integrate a Bi-GRU output with learnable weights features by SA, and improve the performance. We evaluate our proposed method on our created SITB-OSED and IEMOCAP databases. We report that the experimental results of our proposed method achieve state-of-the-art performance on both databases.

Keywords: Bi-GRU, 1D-CNNs, self-attention, speech emotion recognition

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Authors: Ravid Inbar

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CaMKII (calcium-calmodulin dependent protein kinase II) makes up 2% of the protein in our brain and has a critical role in memory formation and long-term potentiation of neurons. Despite this, research has yet to uncover the role of one of the domains on the activation of this kinase. The following proposes to express the protein without the hub domain in E. coli, leaving only the kinase and regulatory segment of the protein. Next, a series of kinase assays will be conducted to elucidate the role the hub domain plays on CaMKII sensitivity to calcium/calmodulin activation. The hub domain may be important for activation; however, it may also be a variety of domains working together to influence protein activation and not the hub alone. Characterization of a protein is critical to the future understanding of the protein's function, as well as for producing pharmacological targets in cases of patients with diseases.

Keywords: CaMKII, hub domain, kinase assays, kinase + reg seg

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Authors: Olusegun Akinsiku, Olukayode Oyediran, Koleola Odusami

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A construction project is usually made up of a variety of stakeholders whose interests may positively or negatively impact on the outcome of the project execution. The variability of project stakeholders is apparent in their cultural differences, professional background and ethics, and differences in ideas. The need for the effectiveness of construction teams has been investigated as this is an important aspect to meeting client’s expectations in the construction industry. This study adopts a cross-sectional descriptive survey with the purpose of identifying the critical success factors (CSFs) associated with the team effectiveness of construction projects stakeholders, their relationship and the effects on construction project performance. The instrument for data collection was a designed questionnaire which was administered to construction professionals in the construction industry in Lagos State, Nigeria using proportionate stratified sampling. The highest ranked identified CSFs include “team trust”, “esprit de corps among members” and “team cohesiveness”. Using factor analysis and considering the effects of team cohesiveness on project performance, the identified CSFs were categorized into three groups namely cognitive attributes, behavior and processes attributes and affective attributes. All the three groups were observed to have a strong correlation with project performance. The findings of this study are useful in helping construction stakeholders benchmark the team effectiveness factors that will guarantee project success.

Keywords: construction, critical success factors, performance, stakeholders, team effectiveness

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Authors: Jameela Ali, Hamid A. Jalab, Loay E. George, Abdul Rahim Ahmad, Azizah Suliman, Karim Al-Jashamy

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Red blood cells (RBC) are the most common types of blood cells and are the most intensively studied in cell biology. The lack of RBCs is a condition in which the amount of hemoglobin level is lower than normal and is referred to as “anemia”. Abnormalities in RBCs will affect the exchange of oxygen. This paper presents a comparative study for various techniques for classifyig the red blood cells as normal, or abnormal (anemic) using WEKA. WEKA is an open source consists of different machine learning algorithms for data mining applications. The algorithm tested are Radial Basis Function neural network, Support vector machine, and K-Nearest Neighbors algorithm. Two sets of combined features were utilized for classification of blood cells images. The first set, exclusively consist of geometrical features, was used to identify whether the tested blood cell has a spherical shape or non-spherical cells. While the second set, consist mainly of textural features was used to recognize the types of the spherical cells. We have provided an evaluation based on applying these classification methods to our RBCs image dataset which were obtained from Serdang Hospital-Malaysia, and measuring the accuracy of test results. The best achieved classification rates are 97%, 98%, and 79% for Support vector machines, Radial Basis Function neural network, and K-Nearest Neighbors algorithm respectively

Keywords: red blood cells, classification, radial basis function neural networks, suport vector machine, k-nearest neighbors algorithm

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Authors: Mohammad Zavid Parvez, Manoranjan Paul

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Epilepsy is a common neurological disorders characterized by sudden recurrent seizures. Electroencephalogram (EEG) is widely used to diagnose possible epileptic seizure. Many research works have been devoted to predict epileptic seizure by analyzing EEG signal. Seizure prediction by analyzing EEG signals are challenging task due to variations of brain signals of different patients. In this paper, we propose a new approach for feature extraction based on phase correlation in EEG signals. In phase correlation, we calculate relative change between two consecutive segments of an EEG signal and then combine the changes with neighboring signals to extract features. These features are then used to classify preictal/ictal and interictal EEG signals for seizure prediction. Experiment results show that the proposed method carries good prediction rate with greater consistence for the benchmark data set in different brain locations compared to the existing state-of-the-art methods.

Keywords: EEG, epilepsy, phase correlation, seizure

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Authors: Adrià Arbués-Sangüesa, Coloma Ballester, Gloria Haro

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Tracking sports players is a widely challenging scenario, specially in single-feed videos recorded in tight courts, where cluttering and occlusions cannot be avoided. This paper presents an analysis of several geometric and semantic visual features to detect and track basketball players. An ablation study is carried out and then used to remark that a robust tracker can be built with Deep Learning features, without the need of extracting contextual ones, such as proximity or color similarity, nor applying camera stabilization techniques. The presented tracker consists of: (1) a detection step, which uses a pretrained deep learning model to estimate the players pose, followed by (2) a tracking step, which leverages pose and semantic information from the output of a convolutional layer in a VGG network. Its performance is analyzed in terms of MOTA over a basketball dataset with more than 10k instances.

Keywords: basketball, deep learning, feature extraction, single-camera, tracking

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Authors: Hind Bouami

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Handling life-critical systems complexity requires to be equipped with appropriate technology and the right human agents’ functions such as knowledge, experience, and competence in problem’s prevention and solving. Human agents are involved in the management and control of human-machine system’s performance. Documenting human agent’s situation awareness is crucial to support human-machine designers’ decision-making. Knowledge about risks, critical parameters and factors that can impact and threaten automation system’s performance should be collected using preventive and retrospective approaches. This paper aims to document operators’ situation awareness through the analysis of automated organizations’ feedback. The analysis of automated hospital pharmacies feedbacks helps to identify and control critical parameters influencing human machine interaction in order to enhance system’s performance and security. Our human machine system evaluation approach has been deployed in Macon hospital center’s pharmacy which is equipped with automated drug dispensing systems since 2015. Automation’s specifications are related to technical aspects, human-machine interaction, and human aspects. The evaluation of drug delivery automation performance in Macon hospital center has shown that the performance of the automated activity depends on the performance of the automated solution chosen, and also on the control of systemic factors. In fact, 80.95% of automation specification related to the chosen Sinteco’s automated solution is met. The performance of the chosen automated solution is involved in 28.38% of automation specifications performance in Macon hospital center. The remaining systemic parameters involved in automation specifications performance need to be controlled.

Keywords: life-critical systems, situation awareness, human-machine interaction, decision-making

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Authors: Divi Sharma

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The emergence of positive behaviour support (PBS) is directly linked to applied behaviour analysis that incorporates evidence-based approaches to addressing ethical challenges and improving autonomy, participation, and the overall quality of life of people living and learning in complex social environments. Its features include lifestyle improvement, collaboration with general caregivers, tracking progress with sound steps, comprehensive performance-based interventions, striving for contextual equality, and ensuring entry and implementation. This document aims to summarize its features with the support of case examples such as involving caregivers to play an active role in behavioural interventions, creating effective interventions within natural practices. Additionally, dealing with lifestyle changes, as well as a wide variety of behavioural changes, develop strong strategies which reduce professional dependence.

Keywords: positive behaviour support, quality of life, performance-based interventions, behavioural changes, participation

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Authors: Seyhan Karaçavuş, Bülent Yılmaz, Ömer Kayaaltı, Semra İçer, Arzu Taşdemir, Oğuzhan Ayyıldız, Kübra Eset, Eser Kaya

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In this study, our goal was to perform tumor staging and subtype determination automatically using different texture analysis approaches for a very common cancer type, i.e., non-small cell lung carcinoma (NSCLC). Especially, we introduced a texture analysis approach, called Law’s texture filter, to be used in this context for the first time. The 18F-FDG PET images of 42 patients with NSCLC were evaluated. The number of patients for each tumor stage, i.e., I-II, III or IV, was 14. The patients had ~45% adenocarcinoma (ADC) and ~55% squamous cell carcinoma (SqCCs). MATLAB technical computing language was employed in the extraction of 51 features by using first order statistics (FOS), gray-level co-occurrence matrix (GLCM), gray-level run-length matrix (GLRLM), and Laws’ texture filters. The feature selection method employed was the sequential forward selection (SFS). Selected textural features were used in the automatic classification by k-nearest neighbors (k-NN) and support vector machines (SVM). In the automatic classification of tumor stage, the accuracy was approximately 59.5% with k-NN classifier (k=3) and 69% with SVM (with one versus one paradigm), using 5 features. In the automatic classification of tumor subtype, the accuracy was around 92.7% with SVM one vs. one. Texture analysis of FDG-PET images might be used, in addition to metabolic parameters as an objective tool to assess tumor histopathological characteristics and in automatic classification of tumor stage and subtype.

Keywords: cancer stage, cancer cell type, non-small cell lung carcinoma, PET, texture analysis

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Authors: Daniel Pastor, Hyo-Sang Shin

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This paper presents a novel method to use optical flow navigation for long-term navigation. Unlike standard SLAM approaches for augmented reality, OFLAAM is designed for Micro Air Vehicles (MAV). It uses an optical flow camera pointing downwards, an IMU and a monocular camera pointing frontwards. That configuration avoids the expensive mapping and tracking of the 3D features. It only maps these features in a vocabulary list by a localization module to tackle the loss of the navigation estimation. That module, based on the well-established algorithm DBoW2, will be also used to close the loop and allow long-term navigation in confined areas. That combination of high-speed optical flow navigation with a low rate localization algorithm allows fully autonomous navigation for MAV, at the same time it reduces the overall computational load. This framework is implemented in ROS (Robot Operating System) and tested attached to a laptop. A representative scenarios is used to analyse the performance of the system.

Keywords: vision, UAV, navigation, SLAM

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Authors: Youssef R. Hassan, Mohamed S. Hasanin, Reda M. Abdelhameed

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Recently, innovations in packaging security features become more important to see the originality of packaging in industrial application. Luminescent 3d printing materials have been a promising property which can provides a unique opportunity for the design and application of 3D printing. Lack emission of terbium ions, as a source of green emission, in salt form prevent its uses in industrial applications, so searching about stable and highly emitter material become essential. Nowadays, metal organic frameworks (MOFs) play an important role in designing light emitter material. In this work, fluorescent high density polyethylene (FHDPE) composite filament with Tb-benzene 1,3,5-tricarboxylate (Tb-BTC) MOFs for 3D printing have been successfully developed.HDPE pellets were mixed with Tb-BTC and melting extrustion with single screw extruders. It was found that Tb-BTCuniformly dispersed in the HDPE matrix and significantly increased the crystallinity of PE, which not only maintained the good thermal property but also improved the mechanical properties of Tb-BTC@HDPE composites. Notably, the composite filaments emitted ultra-bright green light under UV lamp, and the fluorescence intensity increased as the content of Tb-BTC increased. Finally, several brightly luminescent exquisite articles could be manufactured by fused deposition modeling (FDM) 3D printer with these new fluorescent filaments. In this context, the development of novel fluorescent Tb-BTC@HDPE composites was combined with 3D printing technology to amplified the packaging Security Features.

Keywords: 3D printing, fluorescent, packaging, security

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Authors: Jerald Ruta, Ricardo Villar, Jojemar Bantugan, Nycel Barbadillo, Jigg Pelayo

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Streams are important in providing water supply for industrial, agricultural and human consumption, In short when there are streams there are lives. Identifying streams are essential since many developed cities are situated in the vicinity of these bodies of water and in flood management, it serves as basin for surface runoff within the area. This study aims to process and generate features from high-resolution digital terrain model (DTM) with 1-meter resolution using Hydrology Tools of ArcGIS. The raster was then filled, processed flow direction and accumulation, then raster calculate and provide stream order, converted to vector, and clearing undesirable features using the ancillary or google earth. In field validation streams were classified whether perennial, intermittent or ephemeral. Results show more than 90% of the extracted feature were accurate in assessment through field validation.

Keywords: digital terrain models, hydrology tools, strahler method, stream classification

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Authors: Esayas Geleta

Abstract:

Within the last two decades in many sub-Saharan African countries, a large-scale acquisition (lease, concession, outright purchase) of extensive areas of farmland commonly labeled as ‘idle’ and ‘under-utilized’ has resulted in displacement and dispossession and dispossession without ‘compensation.’ This paper seeks to critically illustrate the processes and the consequences of the ‘land grabbing project’ in Ethiopia. Drawing on the theory of participatory development and empirical studies undertaken in Ethiopia, the paper elucidates the power dynamics that influence how and why dislocation and dispossession occur. The paper then demonstrates why the land-grabbing project, which was hugely supported by many international organizations, has largely failed in Ethiopia. Through a critical analysis of the process of ‘land grabbing’ in Ethiopia, the paper contributes to a more adequate and critical understanding of contemporary land deals and their social and environmental consequences.

Keywords: land grabbing, human rights, dispossession, resistance, governance

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