Search results for: fuzzy network project

Authors: Tomas Menard

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The design of control-law for engineering systems has been investigated for many decades. While many results are concerned with continuous systems with continuous output, nowadays, many controlled systems have to transmit their output measurements through network, hence making it discrete-time. But it is well known that the sampling of a system whose control-law is based on the continuous output may render the system unstable, especially when this sampling period is long compared to the system dynamics. The control design then has to be adapted in order to cope with this issue. In this paper, we consider systems which can be modeled as double integrator with uncertainty on the input since many mechanical systems can be put under such form. We present a control scheme based on an observer using only discrete time measurement and which provides continuous time estimation of the state, combined with a continuous control law, which stabilized a system with second-order dynamics even in the presence of uncertainty. It is further shown that arbitrarily long sampling periods can be dealt with properly setting the control scheme parameters.

Keywords: dynamical system, control law design, sampled output, observer design

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Authors: Mohd Zamri Osman, Mohd Aizaini Maarof, Mohd Foad Rohani

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Human skin detection recognized as the primary step in most of the applications such as face detection, illicit image filtering, hand recognition and video surveillance. The performance of any skin detection applications greatly relies on the two components: feature extraction and classification method. Skin color is the most vital information used for skin detection purpose. However, color feature alone sometimes could not handle images with having same color distribution with skin color. A color feature of pixel-based does not eliminate the skin-like color due to the intensity of skin and skin-like color fall under the same distribution. Hence, the statistical color analysis will be exploited such mean and standard deviation as an additional feature to increase the reliability of skin detector. In this paper, we studied the effectiveness of statistical color feature for human skin detection. Furthermore, the paper analyzed the integrated color and texture using eight classifiers with three color spaces of RGB, YCbCr, and HSV. The experimental results show that the integrating statistical feature using Random Forest classifier achieved a significant performance with an F1-score 0.969.

Keywords: color space, neural network, random forest, skin detection, statistical feature

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Authors: C. Samita, W. Chalermchai

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This project is aimed to be a preliminary study of using Tapioca Starch as a viscosity modifying agent (VMA) in concrete work. Tapioca starch effects on the viscosity of concrete, which could be investigated from the workability of corresponding mortar. Cement only mortars with water to cement ratio (w/c) 0.25 to 0.48, superplasticizer dosage of 1% to 2.5%, starch concentration of 0%, 0.25% and 0.5%, was tested for workability. Mortar mixes that have equivalent workability (flow diameter of 250 mm, and funnel flow time of 5 seconds) for each starch concentration were identified and checked for concrete properties. Concrete were tested for initial workability, workability loss, bleeding, setting times, and compressive strength. The results showed that all concrete mixes provide same initial workability, however the mix with higher starch concentration provides slower loss. Bleeding occurs when concrete has w/c more than 0.45. For setting times, mixing with higher starch concentration provide longer setting times (around 4 hours in this experiment). Compressive strength of starch concretes which always have higher w/c, are lower than that of cement only concrete as in this experiment initial workability were controlled to be same.

Keywords: viscosity modifying agent(VMA), self-leveling concrete, self-compacting concrete(SCC), low-binder SCC

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Authors: Olumuyiwa Idowu Ojo, Oluwatobi Peter Olowo

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The study developed a Geographical Information Systems (GIS) database and mapped inter-annual changes in crop yields of cassava, cowpea, maize, rice, melon and yam as a response to inter-annual rainfall and temperature variability in Southwest, Nigeria. The aim of this project is to study the comparative analysis of the weather variability impact of six crops yield (Rice, melon, yam, cassava, Maize and cowpea) in South Western States of Nigeria (Oyo, Osun, Ekiti, Ondo, Ogun and Lagos) from 1991 – 2007. The data was imported and analysed in the Arch GIS 9 – 3 software environment. The various parameters (temperature, rainfall, crop yields) were interpolated using the kriging method. The results generated through interpolation were clipped to the study area. Geographically weighted regression was chosen from the spatial statistics toolbox in Arch GIS 9.3 software to analyse and predict the relationship between temperature, rainfall and the different crops (Cowpea, maize, rice, melon, yam, and cassava).

Keywords: GIS, crop yields, comparative analysis, temperature, rainfall, weather variability

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Authors: Ming-Jen Huang, Chun-Fang Huang, Chiching Wei

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With the recent advance of the deep neural network, we observe new applications of NLP (natural language processing) and CV (computer vision) powered by deep neural networks for processing business documents. However, creating a real-world document processing system needs to integrate several NLP and CV tasks, rather than treating them separately. There is a need to have a unified approach for processing documents containing textual and graphical elements with rich formats, diverse layout arrangement, and distinct semantics. In this paper, a framework that fulfills this unified approach is presented. The framework includes a representation model definition for holding the information generated by various tasks and specifications defining the coordination between these tasks. The framework is a blueprint for building a system that can process documents with rich formats, styles, and multiple types of elements. The flexible and lightweight design of the framework can help build a system for diverse business scenarios, such as contract monitoring and reviewing.

Keywords: document processing, framework, formal definition, machine learning

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Authors: Z. Gharaee

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Recognizing actions performed by others is important in our daily lives since it is necessary for communicating with others in a proper way. We perceive an action by observing the kinematics of motions involved in the performance. We use our experience and concepts to make a correct recognition of the actions. Although building the action concepts is a life-long process, which is repeated throughout life, we are very efficient in applying our learned concepts in analyzing motions and recognizing actions. Experiments on the subjects observing the actions performed by an actor show that an action is recognized after only about two hundred milliseconds of observation. In this study, hierarchical action recognition architecture is proposed by using growing grid layers. The first-layer growing grid receives the pre-processed data of consecutive 3D postures of joint positions and applies some heuristics during the growth phase to allocate areas of the map by inserting new neurons. As a result of training the first-layer growing grid, action pattern vectors are generated by connecting the elicited activations of the learned map. The ordered vector representation layer receives action pattern vectors to create time-invariant vectors of key elicited activations. Time-invariant vectors are sent to second-layer growing grid for categorization. This grid creates the clusters representing the actions. Finally, one-layer neural network developed by a delta rule labels the action categories in the last layer. System performance has been evaluated in an experiment with the publicly available MSR-Action3D dataset. There are actions performed by using different parts of human body: Hand Clap, Two Hands Wave, Side Boxing, Bend, Forward Kick, Side Kick, Jogging, Tennis Serve, Golf Swing, Pick Up and Throw. The growing grid architecture was trained by applying several random selections of generalization test data fed to the system during on average 100 epochs for each training of the first-layer growing grid and around 75 epochs for each training of the second-layer growing grid. The average generalization test accuracy is 92.6%. A comparison analysis between the performance of growing grid architecture and self-organizing map (SOM) architecture in terms of accuracy and learning speed show that the growing grid architecture is superior to the SOM architecture in action recognition task. The SOM architecture completes learning the same dataset of actions in around 150 epochs for each training of the first-layer SOM while it takes 1200 epochs for each training of the second-layer SOM and it achieves the average recognition accuracy of 90% for generalization test data. In summary, using the growing grid network preserves the fundamental features of SOMs, such as topographic organization of neurons, lateral interactions, the abilities of unsupervised learning and representing high dimensional input space in the lower dimensional maps. The architecture also benefits from an automatic size setting mechanism resulting in higher flexibility and robustness. Moreover, by utilizing growing grids the system automatically obtains a prior knowledge of input space during the growth phase and applies this information to expand the map by inserting new neurons wherever there is high representational demand.

Keywords: action recognition, growing grid, hierarchical architecture, neural networks, system performance

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Authors: Anika Zafiah M. Rus, Nik Normunira Mat Hassan

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Biopolymer made from renewable material are one of the most important group of polymer because of their versatility and they can be manufactured in a wide range of densities and stiffness. In this project, biopolymer based on waste vegetable oil were synthesized and crosslink with commercial polymethane polyphenyl isocyanate (known as BF).The BF was compressed by using hot compression moulding technique at 90 oC based on the evaporation of volatile matter and known as compress biopolymer (CB). The density, vibration and damping characteristic of CB were determined after UV irradiation. Treatment with titanium dioxide (TiO2) was found to affect the physical property of compress biopolymer composite (CBC). The density of CBC samples was steadily increased with an increase of UV irradiation time and TiO2 loading. The highest density of CBC samples is at 10 % of TiO2 loading of 1.1088 g/cm3 due to the amount of filler loading. The vibration and damping characteristic of CBC samples was generated at displacements of 1 mm and 1.5 mm and acceleration of 0.1 G and 0.15 G base excitation according to ASTM D3580-9. It was revealed that, the vibration and damping characteristic of CBC samples is significantly increased with the increasing of UV irradiation time, lowest thickness and percentages of TiO2 loading at the frequency range of 15 - 25 Hz. Therefore, this study indicated that the damping property of CBC could be improved upon prolonged exposure to UV irradiation.

Keywords: biopolymer flexible foam, TGA, UV irradiation, vibration and damping

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Authors: Armin Nabaei, M. Omair Ahmad, M. N. S. Swamy

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Many facial expression and emotion recognition methods in the traditional approaches of using LDA, PCA, and EBGM have been proposed. In recent years deep learning models have provided a unique platform addressing by automatically extracting the features for the detection of facial expression and emotions. However, deep networks require large training datasets to extract automatic features effectively. In this work, we propose an efficient emotion detection algorithm using face images when only small datasets are available for training. We design a deep network whose feature extraction capability is enhanced by utilizing several parallel modules between the input and output of the network, each focusing on the extraction of different types of coarse features with fined grained details to break the symmetry of produced information. In fact, we leverage long range dependencies, which is one of the main drawback of CNNs. We develop this work by introducing a Dynamic Soft-Margin SoftMax.The conventional SoftMax suffers from reaching to gold labels very soon, which take the model to over-fitting. Because it’s not able to determine adequately discriminant feature vectors for some variant class labels. We reduced the risk of over-fitting by using a dynamic shape of input tensor instead of static in SoftMax layer with specifying a desired Soft- Margin. In fact, it acts as a controller to how hard the model should work to push dissimilar embedding vectors apart. For the proposed Categorical Loss, by the objective of compacting the same class labels and separating different class labels in the normalized log domain.We select penalty for those predictions with high divergence from ground-truth labels.So, we shorten correct feature vectors and enlarge false prediction tensors, it means we assign more weights for those classes with conjunction to each other (namely, “hard labels to learn”). By doing this work, we constrain the model to generate more discriminate feature vectors for variant class labels. Finally, for the proposed optimizer, our focus is on solving weak convergence of Adam optimizer for a non-convex problem. Our noteworthy optimizer is working by an alternative updating gradient procedure with an exponential weighted moving average function for faster convergence and exploiting a weight decay method to help drastically reducing the learning rate near optima to reach the dominant local minimum. We demonstrate the superiority of our proposed work by surpassing the first rank of three widely used Facial Expression Recognition datasets with 93.30% on FER-2013, and 16% improvement compare to the first rank after 10 years, reaching to 90.73% on RAF-DB, and 100% k-fold average accuracy for CK+ dataset, and shown to provide a top performance to that provided by other networks, which require much larger training datasets.

Keywords: computer vision, facial expression recognition, machine learning, algorithms, depp learning, neural networks

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Authors: Yan Zhang

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Predictive maintenance is a technique to predict when an in-service machine will fail so that maintenance can be planned in advance. Analytics-driven predictive maintenance is gaining increasing attention in many industries such as manufacturing, utilities, aerospace, etc., along with the emerging demand of Internet of Things (IoT) applications and the maturity of technologies that support Big Data storage and processing. This study aims to build an end-to-end analytics solution that includes both real-time machine condition monitoring and machine learning based predictive analytics capabilities. The goal is to showcase a general predictive maintenance solution architecture, which suggests how the data generated from field machines can be collected, transmitted, stored, and analyzed. We use a publicly available aircraft engine run-to-failure dataset to illustrate the streaming analytics component and the batch failure prediction component. We outline the contributions of this study from four aspects. First, we compare the predictive maintenance problems from the view of the traditional reliability centered maintenance field, and from the view of the IoT applications. When evolving to the IoT era, predictive maintenance has shifted its focus from ensuring reliable machine operations to improve production/maintenance efficiency via any maintenance related tasks. It covers a variety of topics, including but not limited to: failure prediction, fault forecasting, failure detection and diagnosis, and recommendation of maintenance actions after failure. Second, we review the state-of-art technologies that enable a machine/device to transmit data all the way through the Cloud for storage and advanced analytics. These technologies vary drastically mainly based on the power source and functionality of the devices. For example, a consumer machine such as an elevator uses completely different data transmission protocols comparing to the sensor units in an environmental sensor network. The former may transfer data into the Cloud via WiFi directly. The latter usually uses radio communication inherent the network, and the data is stored in a staging data node before it can be transmitted into the Cloud when necessary. Third, we illustrate show to formulate a machine learning problem to predict machine fault/failures. By showing a step-by-step process of data labeling, feature engineering, model construction and evaluation, we share following experiences: (1) what are the specific data quality issues that have crucial impact on predictive maintenance use cases; (2) how to train and evaluate a model when training data contains inter-dependent records. Four, we review the tools available to build such a data pipeline that digests the data and produce insights. We show the tools we use including data injection, streaming data processing, machine learning model training, and the tool that coordinates/schedules different jobs. In addition, we show the visualization tool that creates rich data visualizations for both real-time insights and prediction results. To conclude, there are two key takeaways from this study. (1) It summarizes the landscape and challenges of predictive maintenance applications. (2) It takes an example in aerospace with publicly available data to illustrate each component in the proposed data pipeline and showcases how the solution can be deployed as a live demo.

Keywords: Internet of Things, machine learning, predictive maintenance, streaming data

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Authors: Veruska C. Dutra, Afonso R. Aquino

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Since the 1980s, global tourism activity has consolidated worldwide to become an important economic contributor, and consequently, the sociocultural and environmental impacts are starting to become evidenced. This raises the need of discussing about actions for sustainable tourism that should be linked not only to the economy, but also to the environment and social aspects. The work that is going to be presented is part of a doctoral research project in Sciences undertaken at the Sao Paulo University, Brazil. It aims to analyze whether the monitoring of the tourism sector with a focus on sustainability is applicable or not, through those indicators, put in a case study in the Jalapão State Park (JSP) conservation unit, in the state of Tocantins, Brazil. This is a study of an interdisciplinary nature that had the deductive method as its guide. We concluded that the key points of the sustainable tourism, when analyzed with the focal point in environmental indicators, are an important evaluation and quantification tool of that activity in the study locus. It displayed itself as an adequate tool for monitoring, thus decoding, the main environmental impacts that occur in tourism regions and their intensity, which is made possible through analysis, and has the objective to trace ways to prevent and correct the presented impacts.

Keywords: indicators, tourism, sustainability, Jalapão

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Authors: Azeddine El-Hassouny, Chandrashekhar Meshram, Geraldin Nanfack

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In recent years, learning from data with noisy labels (Label Noise) has been a major concern in supervised learning. This problem has become even more worrying in Deep Learning, where the generalization capabilities have been questioned lately. Indeed, deep learning requires a large amount of data that is generally collected by search engines, which frequently return data with unreliable labels. In this paper, we investigate the Label Noise in Deep Learning using variational inference. Our contributions are : (1) exploiting Label Noise concept where the true labels are learnt using reparameterization variational inference, while observed labels are learnt discriminatively. (2) the noise transition matrix is learnt during the training without any particular process, neither heuristic nor preliminary phases. The theoretical results shows how true label distribution can be learned by variational inference in any discriminate neural network, and the effectiveness of our approach is proved in several target datasets, such as MNIST and CIFAR32.

Keywords: label noise, deep learning, discrete latent variable, variational inference, MNIST, CIFAR32

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Authors: Hossam Abdelrahman, Sama Rostom, Reem Yassein, Yara Mohamed, Salma Salah, Nour Awny

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In today’s environment, people are becoming increasingly interested in their appearance. However, they are afraid of their unknown appearance after a plastic surgery or treatment. Accidents, burns and genetic problems such as bowing of body parts of people have a negative impact on their mental health with their appearance and this makes them feel uncomfortable and underestimated. The approach presents a revolutionary deep learning-based image inpainting method that analyses the various picture structures and corrects damaged images. In this study, A model is proposed based on the in-painting of medical images with Stable Diffusion Inpainting method. Reconstructing missing and damaged sections of an image is known as image inpainting is a key progress facilitated by deep neural networks. The system uses the input of the user of an image to indicate a problem, the system will then modify the image and output the fixed image, facilitating for the patient to see the final result.

Keywords: generative adversarial network, large mask inpainting, stable diffusion inpainting, plastic surgery

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Authors: Saud Al Mahdi

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Traditionally,portfolio managers have been discouraged from timing the market. This means, for example, that equity managers have been forced to adhere strictly to a benchmark with static or relatively stable components, such as the SP 500 or the Russell 3000. This means that the portfolio’s exposures to all risk factors should mimic as closely as possible the corresponding exposures of the benchmark. The main risk factor, of course, is the market itself. Effectively, a long-only portfolio would be constrained to have a beta 1. More recently, however, managers have been given greater discretion to adjust their portfolio’s risk exposures (in particular, the beta of their portfolio) dynamically to match the manager’s beliefs about future performance of the risk factors themselves. This freedom translates into the manager’s ability to adjust the portfolio’s beta dynamically. These strategies have come to be known as smart beta strategies. Adjusting beta dynamically amounts to attempting to "time" the market; that is, to increase exposure when one anticipates that the market will rise, and to decrease it when one anticipates that the market will fall. Traditionally, market timing has been believed to be impossible to perform effectively and consistently. Moreover, if a majority of market participants do it, their combined actions could destabilize the market. The aim of this project is to investigate so-called smart beta strategies to determine if they really can add value, or if they are merely marketing gimmicks used to sell dubious investment strategies.

Keywords: beta, alpha, active portfolio management, trading strategies

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Authors: Brian Ceh, Alison Jackson-Holland

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Food deserts are a reality in some cities. These deserts can be described as a shortage of healthy food options within close proximity of consumers. The shortage in this case is typically facilitated by a lack of stores in an urban area that provide adequate fruit and vegetable choices. This study explores new avenues to better understand food deserts by examining modes of transportation that are available to shoppers or consumers, e.g. walking, automobile, or public transit. Further, this study is unique in that it not only explores the location of large grocery stores, but small grocery and convenience stores too. In this study, the relationship between some socio-economic indicators, such as personal income, are also explored to determine any possible association with food deserts. In addition, to help facilitate our understanding of food deserts, complex network spatial models that are built on adequate algorithms are used to investigate the possibility of food deserts in the city of Hamilton, Canada. It is found that Hamilton, Canada is adequate serviced by retailers who provide healthy food choices and that the food desert phenomena is almost absent.

Keywords: Canada, desert, food, Hamilton, store

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Authors: Annalise Weckesser, Alex Wade, Clara Joergensen, Jerome Turner

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Introduction: Digital mobile technologies afford Generation M a number of opportunities in terms of communication, creativity and connectivity in their social interactions. Yet these young people’s use of such technologies is often the source of moral panic with accordant social anxiety especially prevalent in media representations of teen ‘sexting,’ or the sending of sexually explicit images via smartphones. Thus far, most responses to youth sexting have largely been ineffective or unjust with adult authorities sometimes blaming victims of non-consensual sexting, using child pornography laws to paradoxically criminalise those they are designed to protect, and/or advising teenagers to simply abstain from the practice. Prevention strategies are further skewed, with sex education initiatives often targeted at girls, implying that they shoulder the responsibility of minimising the risks associated with sexting (e.g. revenge porn and sexual predation). Purpose of Study: Despite increasing public interest and concern about ‘teen sexting,’ there remains a dearth of research with young people regarding their experiences of navigating sex and relationships in the current digital media landscape. Furthermore, young people's views on sexting are rarely solicited in the policy and educational strategies aimed at them. To address this research-policy-education gap, an interdisciplinary team of four researchers (from anthropology, media, sociology and education) have undertaken a peer-to-peer research project to co-create a sexual health intervention. Methods: In the winter of 2015-2016, the research team conducted serial group interviews with four cohorts of students (aged 13 to 15) from a secondary school in the West Midlands, UK. To facilitate open dialogue, girls and boys were interviewed separately, and each group consisted of no more than four pupils. The team employed a range of participatory techniques to elicit young people’s views on sexting, its consequences, and its interventions. A final focus group session was conducted with all 14 male and female participants to explore developing a peer-to-peer ‘safe sexting’ education intervention. Findings: This presentation will highlight the ongoing, ‘old school’ sexual double standards at work within this new digital frontier. In the sharing of ‘nudes’ (teens’ preferred term to ‘sexting’) via social media apps (e.g. Snapchat and WhatsApp), girls felt sharing images was inherently risky and feared being blamed and ‘slut-shamed.’ In contrast, boys were seen to gain in social status if they accumulated nudes of female peers. Further, if boys had nudes of themselves shared without consent, they felt they were expected to simply ‘tough it out.’ The presentation will also explore what forms of supports teens desire to help them in their day-to-day navigation of these digitally mediated, heteronormative performances of teen femininity and masculinity expected of them. Conclusion: This is the first research project, within UK, conducted with rather than about teens and the phenomenon of sexting. It marks a timely and important contribution to the nascent, but growing body of knowledge on gender, sexual politics and the digital mobility of sexual images created by and circulated amongst young people.

Keywords: teens, sexting, gender, sexual politics

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Authors: Sofia Quaglia, Carmine Di Martino, Brendan Tierney

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Artificial Intelligence (A.I.) constitutes various aspects of modern life, from the Machine Learning algorithms predicting the stocks on Wall streets to the killing of belligerents and innocents alike on the battlefield. Moreover, the end goal is to create autonomous A.I.; this means that the presence of humans in the decision-making process will be absent. The question comes naturally: when an A.I. does something wrong when its behavior is harmful to the community and its actions go against the law, which is to be held responsible? This research’s subject matter in A.I. and Robot Ethics focuses mainly on Robot Rights and its ultimate objective is to answer the questions: (i) What is the function of rights? (ii) Who is a right holder, what is personhood and the requirements needed to be a moral agent (therefore, accountable for responsibility)? (iii) Can an A.I. be a moral agent? (ontological requirements) and finally (iv) if it ought to be one (ethical implications). With the direction to answer this question, this research project was done via a collaboration between the School of Computer Science in the Technical University of Dublin that oversaw the technical aspects of this work, as well as the Department of Philosophy in the University of Milan, who supervised the philosophical framework and argumentation of the project. Firstly, it was found that all rights are positive and based on consensus; they change with time based on circumstances. Their function is to protect the social fabric and avoid dangerous situations. The same goes for the requirements considered necessary to be a moral agent: those are not absolute; in fact, they are constantly redesigned. Hence, the next logical step was to identify what requirements are regarded as fundamental in real-world judicial systems, comparing them to that of ones used in philosophy. Autonomy, free will, intentionality, consciousness and responsibility were identified as the requirements to be considered a moral agent. The work went on to build a symmetrical system between personhood and A.I. to enable the emergence of the ontological differences between the two. Each requirement is introduced, explained in the most relevant theories of contemporary philosophy, and observed in its manifestation in A.I. Finally, after completing the philosophical and technical analysis, conclusions were drawn. As underlined in the research questions, there are two issues regarding the assignment of moral agency to artificial agent: the first being that all the ontological requirements must be present and secondly being present or not, whether an A.I. ought to be considered as an artificial moral agent. From an ontological point of view, it is very hard to prove that an A.I. could be autonomous, free, intentional, conscious, and responsible. The philosophical accounts are often very theoretical and inconclusive, making it difficult to fully detect these requirements on an experimental level of demonstration. However, from an ethical point of view it makes sense to consider some A.I. as artificial moral agents, hence responsible for their own actions. When considering artificial agents as responsible, there can be applied already existing norms in our judicial system such as removing them from society, and re-educating them, in order to re-introduced them to society. This is in line with how the highest profile correctional facilities ought to work. Noticeably, this is a provisional conclusion and research must continue further. Nevertheless, the strength of the presented argument lies in its immediate applicability to real world scenarios. To refer to the aforementioned incidents, involving the murderer of innocents, when this thesis is applied it is possible to hold an A.I. accountable and responsible for its actions. This infers removing it from society by virtue of its un-usability, re-programming it and, only when properly functioning, re-introducing it successfully

Keywords: artificial agency, correctional system, ethics, natural agency, responsibility

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Authors: Kumars ZandParsa, Quynh Nguyen, Hadi Moradi

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There are old cast-in-place concrete box girder bridges in California with inter-span hinges that are designed based on old earthquake codes. Hinge removal is part of the bridges’ earthquake retrofitting project, and hinges were removed and replaced with modified hinges per new earthquake codes. The span that has a hinge is divided into short and long cantilevers in which the short cantilever supports the long cantilever. In the recent bridge hinge replacement, the length of the short and long cantilevers were 20ft and 80ft, respectively. The seat in the new design is wider than the old design, and the horizontal and vertical movements of the deck at the hinge location must be computed to check if restraints are needed. In this paper, besides considering the conventional reinforced concrete box girder bridges, the hinge removal operations, along with the response spectrum analysis based on the El Centro 1940 earthquake, will be presented to verify if vertical and horizontal restrainers are needed.

Keywords: hinge replacement, restrainers, vertical earthquake, response spectrum analysis

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Authors: Elena Bellini, Daniele Mugnaini, Michele Boschetto

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People with an Autistic Spectrum Disorder and an Intellectual Disability who need to attend a Hospital Emergency Waiting Room frequently present high levels of discomfort and challenging behaviors due to stress-related hyperarousal, sensory sensitivity, novelty-anxiety, communication and self-regulation difficulties. Increased agitation and acting out also disturb the diagnostic and therapeutic processes, and the emergency room climate. Architectural design disciplines aimed at reducing distress in hospitals or creating autism-friendly environments are called for to find effective answers to this particular need. A growing number of researchers are considering the physical environment as an important point of intervention for people with autism. It has been shown that providing the right setting can help enhance confidence and self-esteem and can have a profound impact on their health and wellbeing. Environmental psychology has evaluated the perceived quality of care, looking at the design of hospital rooms, paths and circulation, waiting rooms, services and devices. Furthermore, many studies have investigated the influence of the hospital environment on patients, in terms of stress-reduction and therapeutic intervention’ speed, but also on health professionals and their work. Several services around the world are organizing autism-friendly hospital environments which involve the architecture and the specific staff training. In Italy, the association Spes contra spem has promoted and published, in 2013, the ‘Chart of disabled people in the hospital’. It stipulates that disabled people should have equal rights to accessible and high-quality care. There are a few Italian examples of therapeutic programmes for autistic people as the Dama project in Milan and the recent experience of Children and Autism Foundation in Pordenone. Careggi’s Emergency Waiting Room in Florence has been built to satisfy this challenge. This project of research comes from a collaboration between the technical staff of Careggi Hospital, the Center for autism PAMAPI and some architects expert in the sensory environment. The methodology of focus group involved architects, psychologists and professionals through a transdisciplinary research, centered on the links between the spatial characteristics and clinical state of people with ASD. The relationship between architectural space and quality of life is studied to pay maximum attention to users’ needs and to support the medical staff in their work by a specific program of training. The result of this research is a sum of criteria used to design the emergency waiting room, that will be illustrated. A protected room, with a clear space design, maximizes comprehension and predictability. The multisensory environment is thought to help sensory integration and relaxation. Visual communication through Ipad allows an anticipated understanding of medical procedures, and a specific technological system supports requests, choices and self-determination in order to fit sensory stimulation to personal preferences, especially for hypo and hypersensitive people. All these characteristics should ensure a better regulation of the arousal, less behavior problems, improving treatment accessibility, safety, and effectiveness. First results about patient-satisfaction levels will be presented.

Keywords: accessibility of care, autism-friendly architecture, personalized therapeutic process, sensory environment

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Authors: Bilge Yaman Islak, Erhan Ayas

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The densification behavior of TiB₂ – Ti₃SiC₂ composites is investigated for temperatures in the range of 1200°C to 1400°C, for the pressure of 40 and 50MPa, and for holding time between 15-30 min by spark plasma sintering (SPS) technique. Ti, Si, TiC and 5 wt.% TiB₂ were used to synthesize TiB₂ – Ti₃SiC₂ composites and the effect of different sintering parameters on the densification and phase evolution of these composites were investigated. The bulk densities were determined by using the Archimedes method. The polished and fractured surfaces of the samples were examined using a scanning electron microscope equipped with an energy dispersive spectroscopy (EDS). The phase analyses were accomplished by using the X-Ray diffractometer. Sintering temperature and holding time are found to play a dominant role in the phase development of composites. TiₓCᵧ and TiSi₂ secondary phases were found in 5 wt.%TiB₂ – Ti₃SiC₂ composites densified at 1200°C and 1400°C under the pressure of 40 MPa, due to decomposition of Ti₃SiC₂. The results indicated that 5 wt.%TiB₂ – Ti₃SiC₂ composites were densified into the dense parts with a relative density of 98.77% by sintering at 1300 °C, for 15 min, under a pressure of 50 MPa via SPS without the formation of any other ancillary phase. This work was funded and supported by Scientific Research Projects Commission of Eskisehir Osmangazi University with the Project Number 201915C103 (2019-2517).

Keywords: densification, phase evolution, sintering, TiB₂ – Ti₃SiC₂ composites

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Authors: Prudvi Paresi, Fatemeh Javidan

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In recent years, greenhouse gas, or in particular, carbon emissions, have received special attention from environmentalists and designers due to the fact that they significantly contribute to the temperature rise. The building industry is one of the top seven major industries contributing to embodied carbon emission. Signage systems are an integral part of the building industry and bring completeness to the space-building by providing the required information and guidance. A significant amount of building materials, such as steel, aluminium, acrylic, LED, etc., are utilized in these systems, but very limited information is available on their sustainability and carbon footprint. Therefore, there is an urgent need to assess the emissions associated with the signage industry and for controlling these by adopting different mitigation techniques without sacrificing the efficiency of the project. The present paper investigates the embodied carbon of two case studies in the Australian signage industry within the cradle – gate (A1-A3) and gate–site (A4-A5) stages. A material source-based database is considered to achieve more accuracy. The study identified that aluminium is the major contributor to embodied carbon in the signage industry compared to other constituents. Finally, an attempt is made to suggest strategies for mitigating embodied carbon in this industry.

Keywords: carbon accounting, small-scale construction, signage industry, construction materials

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Authors: Dong Wook Lee, Seung Hyun Kim

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Trial blasting is conducted to identify the characteristics of the blasting of the applicable ground before production blasting and to investigate various problems posed by blasting. The methods and pattern of production blasting are determined based on an analysis of the results of trial blasting. The bedrock in Jeju Island, South Korea is formed through the volcanic activities unlike the inland areas, composed of porous basalt. Trial blasting showed that the blast vibration frequency of sedimentary and metamorphic rocks in the inland areas is in a high frequency band of about 80 Hz while the blast vibration frequency of Jeju Island is in a low frequency band of 10~25 Hz. The frequency band is analyzed to be low due to the large cycle of blasting pattern as blast vibration passes through the layered structured ground layer where the rock formation and clickers irregularly repeat. In addition, the blast vibration equation derived from trial blasting was R: 0.885, S.E: 0.216 when applying the square root scaled distance (SRSD) relatively suitable for long distance, estimated at the confidence level of 95%.

Keywords: attenuation index, basaltic ground, blast vibration constant, blast vibration equation, clinker layer

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Authors: Sonal H. Singh, Bhaskar Bhowmick

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The research hypothesis formulated in this paper examines the importance of microfinance provision for entrepreneurship development by engendering a high level of entrepreneurial orientation among the grassroots entrepreneurs. A theoretically well supported empirical framework is proposed to identify the influence of financial services and non-financial services provided by microfinance institutes in strengthening the impact of grassroots innovation on entrepreneurial orientation under resource constraints. In this paper, Grassroots innovation is perceived in three dimensions: new learning practice, localized solution, and network development. The study analyzes the moderating effect of microfinance provision on the relationship between grassroots innovation and entrepreneurial orientation. The paper employed structural equation modelling on 400 data entries from the grassroots entrepreneurs in India. The research intends to help policymakers, entrepreneurs and microfinance providers to promote the innovative design of microfinance services for the well-being of grassroots entrepreneurs and to foster sustainable entrepreneurship development.

Keywords: entrepreneurship development, grassroots innovation, India, structural equation model

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Authors: Somayyeh Naeemi, Ezhar Tamam

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In most of the countries, Facebook allocated high rank of usage among other social network sites. Several studies have examined the effect of Facebook intensity on individuals’ psychological well-being. However, few studies have investigated its effect on eudaimonic well-being. The current study explored how emotional dependency to Facebook relates to psychological well-being in terms of eudaimonic well-being. The number of 402 adolescents 13-16 years old who studied in upper secondary school in Malaysia participated in this study. It was expected to find out a negative association between emotional dependency to Facebook and time spent on Facebook and psychological well-being. It also was examined the moderation effects of self-efficacy on psychological well-being. The results by Structural Equation Modeling revealed that emotional dependency to Facebook has a negative effect on adolescents’ psychological well-being. Surprisingly self-efficacy did not have moderation effect on the relationship between emotional dependency to Facebook and psychological well-being. Lastly, the emotional dependency to Facebook and not the time spent on Facebook lessen adolescents’ psychological well-being, suggesting the value of investigating Facebook usage among college students in future studies.

Keywords: emotional dependency to facebook, psychological well-being, eudaimonic well-being, self-efficacy, adolescent

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Authors: Olga V. Chervyakova, Elmira T. Tailakova, Vitaliy M. Strochkov, Kulyaisan T. Sultankulova, Nurlan T. Sandybayev, Lev G. Nemchinov, Rosemarie W. Hammond

Abstract:

Sheep pox is a highly contagious infection that OIE regards to be one of the most dangerous animal diseases. It causes enormous economic losses because of death and slaughter of infected animals, lower productivity, cost of veterinary and sanitary as well as quarantine measures. To control spread of sheep pox infection the attenuated vaccines are widely used in the Republic of Kazakhstan and other Former Soviet Union countries. In spite of high efficiency of live vaccines, the possible presence of the residual virulence, potential genetic instability restricts their use in disease-free areas that leads to necessity to exploit new approaches in vaccine development involving recombinant DNA technology. Vaccines on the basis of recombinant proteins are the newest generation of prophylactic preparations. The main advantage of these vaccines is their low reactogenicity and this fact makes them widely used in medical and veterinary practice for vaccination of humans and farm animals. The objective of the study is to produce recombinant immunogenic proteins for development of the high-performance means for sheep pox prophylaxis. The SPV proteins were chosen for their homology with the known immunogenic vaccinia virus proteins. Assay of nucleotide and amino acid sequences of the target SPV protein genes. It has been shown that four proteins SPPV060 (ortholog L1), SPPV074 (ortholog H3), SPPV122 (ortholog A33) and SPPV141 (ortholog B5) possess transmembrane domains at N- or C-terminus while in amino acid sequences of SPPV095 (ortholog А 4) and SPPV117 (ortholog А 27) proteins these domains were absent. On the basis of these findings the primers were constructed. Target genes were amplified and subsequently cloned into the expression vector рЕТ26b(+) or рЕТ28b(+). Six constructions (pSPPV060ΔТМ, pSPPV074ΔТМ, pSPPV095, pSPPV117, pSPPV122ΔТМ and pSPPV141ΔТМ) were obtained for expression of the SPV genes under control of T7 promoter in Escherichia coli. To purify and detect recombinant proteins the amino acid sequences were modified by adding six histidine molecules at C-terminus. Induction of gene expression by IPTG was resulted in production of the proteins with molecular weights corresponding to the estimated values for SPPV060, SPPV074, SPPV095, SPPV117, SPPV122 and SPPV141, i.e. 22, 30, 20, 19, 17 and 22 kDa respectively. Optimal protocol of expression for each gene that ensures high yield of the recombinant protein was identified. Assay of cellular lysates by western blotting confirmed expression of the target proteins. Recombinant proteins bind specifically with antibodies to polyhistidine. Moreover all produced proteins are specifically recognized by the serum from experimentally SPV-infected sheep. The recombinant proteins SPPV060, SPPV074, SPPV117, SPPV122 and SPPV141 were also shown to induce formation of antibodies with virus-neutralizing activity. The results of the research will help to develop a new-generation high-performance means for specific sheep pox prophylaxis that is one of key moments in animal health protection. The research was conducted under the International project ISTC # K-1704 “Development of methods to construct recombinant prophylactic means for sheep pox with use of transgenic plants” and under the Grant Project RK MES G.2015/0115RK01983 "Recombinant vaccine for sheep pox prophylaxis".

Keywords: prophylactic preparation, recombinant protein, sheep pox virus, subunit vaccine

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Authors: Ibrahim Aliyu Isaha, Mohd Mansor Ismailb , Salim Hassanc, Norsida Bint Man

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The Global and National income derive from a stingless beekeeping project is a new source of wealth to Malaysia. A common stingless bee species, Trigona itama, potential production through effective utilization of highly competent agents of extension services will lead to higher output that guaranteed maximum income. The study covers a sample beekeepers in ten states and it was designed to examine various impacts of extension services as variables in enhancing sustainable stingless beekeeping production. In addition, the study also determined the profitability of stingless beekeeping production through technology transfer and human resource development. Correlation and Regression analyses were used on a sample size of 87 stingless beekeepers representing 72% of filled questionnaires. The cost-benefit analysis showed participants received lucrative monthly income of more than rm3500. The results indicated positive outcome from extension services that increased production, and hence, generated better additional income to participants. In summary, it is possible for the extension services to increase output of stingless beekeeping through technology transfer

Keywords: extension services, malaysia, profitability, stingless bee, trigona itama production

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Authors: Pankaj Negi

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Nowadays the demand for receiving the high quality electrical energy is being increasing as consumer wants not only reliable but also quality power. Custom power instruments are of the most well-known compensators of power quality in distributed network. This paper present a comprehensive review of compensating custom power devices mainly DSTATCOM (distribution static compensator),DVR (dynamic voltage restorer), and UPQC (unified power quality compensator) and also deals with sliding mode control and its applications to custom power devices. The sliding mode control strategy provides robustness to custom power device and enhances the dynamic response for compensating voltage sag, swell, voltage flicker, and voltage harmonics. The aim of this paper is to provide a broad perspective on the status of compensating devices in electric power distribution system and sliding mode control strategies to researchers and application engineers who are dealing with power quality and stability issues.

Keywords: active power filters(APF), custom power device(CPD), DSTATCOM, DVR, UPQC, sliding mode control (SMC), power quality

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Authors: Harshith Gowda K. S, Tejaskumar N, Shubhanga R. B, Gowtham N, Deekshith Gowda H. S

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Phasor measurement units (PMU) are playing an important role in the current power system for state estimation. It is necessary to have complete observability of the power system while minimizing the cost. For this purpose, the optimal location of the phasor measurement units in the power system is essential. In a bus system, zero injection buses need to be evaluated to minimize the number of PMUs. In this paper, the optimization problem is formulated using mixed integer programming to obtain the optimal location of the PMUs with increased observability. The formulation consists of with and without zero injection bus as constraints. The formulated problem is simulated using a CPLEX solver in the GAMS software package. The proposed method is tested on IEEE 30, IEEE 39, IEEE 57, and IEEE 118 bus systems. The results obtained show that the number of PMUs required is minimal with increased observability.

Keywords: PMU, observability, mixed integer programming (MIP), zero injection buses (ZIB)

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Authors: Yuheng Tao

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Rapid urbanization has brought the consequences of incompatible and excessive homogeneity of urban system, and urban color planning has become one of the most effective ways to restore the characteristics of cities. Among the many urban color design research, the establishment of urban theme colors has rarely been discussed. This study took the "New Taipei City Environmental Aesthetic Color” project as a research case and conducted mixed-method research that included expert interviews and quantitative survey data. This study introduces how theme colors were selected by the experts and investigates public’s perception and preference of the selected theme colors. Several findings include 1) urban memory plays a significant role in determining urban theme colors; 2) When establishing urban theme colors, areas/cities with relatively weak urban memory are given priority to be defined; 3) Urban theme colors that imply cultural attributes are more widely accepted by the public; 4) A representative city theme color helps conserve culture rather than guiding innovation. In addition, this research rearranges the urban color symbolism and specific content of urban theme colors and provides a more scientific urban theme color selection scheme for urban planners.

Keywords: urban theme color, urban color attribute, public perception, public preferences

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Authors: Jorn Meyer, Stefan Sturmer

Abstract:

The assessment of attachment patterns in toddlers and adults has been well researched, and valid diagnostic methods (e.g., Strange Situation Test, Adult Attachment Interview) are applicable. For middle and late childhood, on the other hand, there are only few validated methods available so far. For the Child Attachment Interview (CAI) promising validation studies from English-speaking countries are available, but so far a comprehensive study on the validity of a German sample is lacking. Within the scope of a longitudinal project, the results of the first point of measurement are reported in this study. A German-language version of the CAI was carried out with 111 primary school children (56% female; age: M = 8.34, SD = 0.49). In relation to psychometric quality criteria, parameters on interrater reliability, construct validity and discriminant, and convergent validity are reported. Analyses of the correlations between attachment patterns and internalizing and externalizing behavior problems from parent and teacher reports are presented. The implications for the German-language assessment of attachment in middle and late childhood in research and individual case diagnostics, e.g., in the context of conducting expert evaluation reports for family courts, are discussed.

Keywords: attachment, attachment assessment, developmental psychology, longitudinal study

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Authors: Amhimmid Mohammed Saffour, Mustafa Mohamed Abdullah

Abstract:

The use of computers for handling image data in the healthcare is growing. However, the amount of data produced by modern image generating techniques is vast. This data might be a problem from a storage point of view or when the data is sent over a network. This paper using wavelet transform technique for medical images compression. MATLAB program, are designed to evaluate medical images storage and transmission time problem at Sebha Medical Center Libya. In this paper, three different Computed Tomography images which are abdomen, brain and chest have been selected and compressed using wavelet transform. Objective evaluation has been performed to measure the quality of the compressed images. For this evaluation, the results show that the Peak Signal to Noise Ratio (PSNR) which indicates the quality of the compressed image is ranging from (25.89db to 34.35db for abdomen images, 23.26db to 33.3db for brain images and 25.5db to 36.11db for chest images. These values shows that the compression ratio is nearly to 30:1 is acceptable.

Keywords: medical image, Matlab, image compression, wavelet's, objective evaluation

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