Search results for: object oriented classification

Authors: Wang Yunhong

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The present paper is preoccupied with the different styles of three translators in their translating a Chinese classical novel Shuihu Zhuan. Based on a parallel corpus, it adopts a target-oriented approach to look into whether and what stylistic differences and shifts the three translations have revealed. The findings show that the three translators demonstrate different styles concerning their word choices and sentence preferences, which implies that identification of recurrent textual patterns may be a basic step for investigating the style of a translator.

Keywords: corpus, lexical choices, sentence characteristics, style

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Authors: Kyi Pyar Zaw, Zin Mar Kyu

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Character recognition is the process of converting a text image file into editable and searchable text file. Feature Extraction is the heart of any character recognition system. The character recognition rate may be low or high depending on the extracted features. In the proposed paper, 25 features for one character are used in character recognition. Basically, there are three steps of character recognition such as character segmentation, feature extraction and classification. In segmentation step, horizontal cropping method is used for line segmentation and vertical cropping method is used for character segmentation. In the Feature extraction step, features are extracted in two ways. The first way is that the 8 features are extracted from the entire input character using eight direction chain code frequency extraction. The second way is that the input character is divided into 16 blocks. For each block, although 8 feature values are obtained through eight-direction chain code frequency extraction method, we define the sum of these 8 feature values as a feature for one block. Therefore, 16 features are extracted from that 16 blocks in the second way. We use the number of holes feature to cluster the similar characters. We can recognize the almost Myanmar common characters with various font sizes by using these features. All these 25 features are used in both training part and testing part. In the classification step, the characters are classified by matching the all features of input character with already trained features of characters.

Keywords: chain code frequency, character recognition, feature extraction, features matching, segmentation

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Authors: Mohammed Abdullah Alshahrani

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The research focuses on investigating the use of partial least squares (PLS) methodology for addressing challenges associated with high-dimensional correlated data. Recent technological advancements have led to experiments producing data characterized by a large number of variables compared to observations, with substantial inter-variable correlations. Such data patterns are common in chemometrics, where near-infrared (NIR) spectrometer calibrations record chemical absorbance levels across hundreds of wavelengths, and in genomics, where thousands of genomic regions' copy number alterations (CNA) are recorded from cancer patients. PLS serves as a widely used method for analyzing high-dimensional data, functioning as a regression tool in chemometrics and a classification method in genomics. It handles data complexity by creating latent variables (components) from original variables. However, applying PLS can present challenges. The study investigates key areas to address these challenges, including unifying interpretations across three main PLS algorithms and exploring unusual negative shrinkage factors encountered during model fitting. The research presents an alternative approach to addressing the interpretation challenge of predictor weights associated with PLS. Sparse estimation of predictor weights is employed using a penalty function combining a lasso penalty for sparsity and a Cauchy distribution-based penalty to account for variable dependencies. The results demonstrate sparse and grouped weight estimates, aiding interpretation and prediction tasks in genomic data analysis. High-dimensional data scenarios, where predictors outnumber observations, are common in regression analysis applications. Ordinary least squares regression (OLS), the standard method, performs inadequately with high-dimensional and highly correlated data. Copy number alterations (CNA) in key genes have been linked to disease phenotypes, highlighting the importance of accurate classification of gene expression data in bioinformatics and biology using regularized methods like PLS for regression and classification.

Keywords: partial least square regression, genetics data, negative filter factors, high dimensional data, high correlated data

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Authors: Pooja Khanal, Huaming Zhang

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Labeling of issues/bugs, also known as bug classification, plays a vital role in software engineering. Some known labels/classes of bugs are 'User Interface', 'Security', and 'API'. Most of the time, when a reporter reports a bug, they try to assign some predefined label to it. Those issues are reported for a project, and each project is a repository in GitHub/GitLab, which contains multiple issues. There are many software project repositories -ranging from individual projects to commercial projects. The labels assigned for different repositories may be dependent on various factors like human instinct, generalization of labels, label assignment policy followed by the reporter, etc. While the reporter of the issue may instinctively give that issue a label, another person reporting the same issue may label it differently. This way, it is not known mathematically if a label in one repository is similar or different to the label in another repository. Hence, the primary goal of this research is to find the semantic differences between bug labeling of different repositories via machine learning. Independent optimal classifiers for individual repositories are built first using the text features from the reported issues. The optimal classifiers may include a combination of multiple classifiers stacked together. Then, those classifiers are used to cross-test other repositories which leads the result to be deduced mathematically. The produce of this ongoing research includes a formalized open-source GitHub issues database that is used to deduce the similarity of the labels pertaining to the different repositories.

Keywords: bug classification, bug labels, GitHub issues, semantic differences

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Authors: Tahra AlMahmoud, Sameeha Mohamed Al Hadhrami, Mohamed Elhanan, Hanan Naser Alshamsi, Fikri Abu-Zidan

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Background: To the best of the author(s)’ knowledge there are no epidemiological studies for traumatic eye injuries in UAE, neither data on groups at risk or mechanisms for ocular trauma. Purpose: To report the epidemiology of eye injuries that required hospital admission and surgery at a referral center at the eastern part of Abu Dhabi. Method: Retrospective charts review of all patients who had suffered an eye injury that required surgical intervention between 2012 and 2017 at Al Ain Hospital. Demographic data, place of occurrence, the cause of injury, visual acuity (VA) before and after treatment, number of admission days and follow up were extracted. Data were tabulated and presented as number (%), mean (SD), or median (range) as appropriate. Wilcoxon signed rank test was used for VA outcome. Results: One hundred forty-one patients were identified, 96 eyes with open-globe and 48 other types of injuries. The mean age of the patients was 26±15.5 years, and 89% were male. Majority of injuries occurred at the workplace (50.4%) followed by home (31.2%). Trauma with a sharp object (24.1%), blunt object (16.3%), nail (11.3%), and hammer on metal (7.8%) were the most common etiologies of injury. Corneas injuries (48.2%) was the most frequent cause for visual acuity limitation followed by lens/cataract (23.4%). Among the traumatized eyes, 30 eyes (21.3%) retained intraocular foreign body, Mean admission days was 3.16± 2.81days (1-16) and a number of follow up visit was 3.17± 4.11times (0-26). Conclusion: Ocular trauma requiring surgical intervention is an area of concern in particular for occupations involving work with metals. This work may give insight into the value and necessity of implementing preventive measures.

Keywords: epidemiology, Middle Eastern area, occupational injury, ocular traumas

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Authors: V. Suganya, V. Balasuriya

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The functioning of Micro and Small Scale (MSS) businesses in the northern part of Sri Lanka was vulnerable due to three decades of internal conflict and the subsequent post-war economic openings has resulted new market prospects for MSS businesses. MSS businesses survive and operate with limited resources and struggle to access finance, raw material, markets, and technology. This study attempts to identify the manner in which entrepreneurial orientation puts into practice by the business operators to overcome these business challenges. Business operators in the traditional food processing sector are taken for this study as this sub-sector of the food industry is developing at a rapid pace. A review of the literature was done to recognize the concepts of entrepreneurial orientation, defining MMS businesses and the manner in which business performance is measured. Direct interview method supported by a structured questionnaire is used to collect data from 80 respondents; based on a fixed interval random sampling technique. This study reveals that more than half of the business operators have opted to commence their business ventures as a result of identifying a market opportunity. 41 per cent of the business operators are highly entrepreneurial oriented in a scale of 1 to 5. Entrepreneurial orientation shows significant relationship and strongly correlated with business performance. Pro-activeness, innovativeness and competitive aggressiveness shows a significant relationship with business performance while risk taking is negative and autonomy is not significantly related to business performance. It is evident that entrepreneurial oriented business practices contribute to better business performance even though 70 per cent prefer the ideas/views of the support agencies than the stakeholders when making business decisions. It is recommended that appropriate training should be introduced to develop entrepreneurial skills focusing to improve business networks so that new business opportunities and innovative business practices are identified.

Keywords: Micro and Small Scale (MMS) businesses, entrepreneurial orientation (EO), food processing, business operators

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Authors: Jaouadi Imen, Ben Djemaa Raoudha, Ben Abdallah Hanene

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The access to relevant information that is adapted to users’ needs, preferences and environment is a challenge in many applications running. That causes an appearance of context-aware systems. To facilitate the development of this class of applications, it is necessary that these applications share a common context meta-model. In this article, we will present our context meta-model that is defined using the OMG Meta Object facility (MOF). This meta-model is based on the analysis and synthesis of context concepts proposed in literature.

Keywords: context, meta-model, MOF, awareness system

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Authors: Halina Veloso e Zarate, Manuela Triggianese

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The urban challenges posed by rapid population growth, climate adaptation, and sustainable living have compelled Dutch cities to reimagine their built environment and transportation systems. As a pivotal contributor to CO₂ emissions, the transportation sector in the Netherlands demands innovative solutions for transitioning to low-carbon mobility. This study investigates the potential of transit oriented development (TOD) as a strategy for achieving carbon reduction and sustainable urban transformation. Focusing on the Lombardijen station area in Rotterdam, which is targeted for significant densification, this paper presents a design-oriented exploration of a low-carbon transport node. By employing a research-by-design methodology, this study delves into multifaceted factors and scales, aiming to propose future scenarios for Lombardijen. Drawing from a synthesis of existing literature, applied research, and practical insights, a robust design framework emerges. To inform this framework, governmental data concerning the built environment and material embodied carbon are harnessed. However, the restricted access to crucial datasets, such as property ownership information from the cadastre and embodied carbon data from De Nationale Milieudatabase, underscores the need for improved data accessibility, especially during the concept design phase. The findings of this research contribute fundamental insights not only to the Lombardijen case but also to TOD studies across Rotterdam's 13 nodes and similar global contexts. Spatial data related to property ownership facilitated the identification of potential densification sites, underscoring its importance for informed urban design decisions. Additionally, the paper highlights the disparity between the essential role of embodied carbon data in environmental assessments for building permits and its limited accessibility due to proprietary barriers. Although this study lays the groundwork for sustainable urbanization through TOD-based design, it acknowledges an area of future research worthy of exploration: the socio-economic dimension. Given the complex socio-economic challenges inherent in the Lombardijen area, extending beyond spatial constraints, a comprehensive approach demands integration of mobility infrastructure expansion, land-use diversification, programmatic enhancements, and climate adaptation. While the paper adopts a TOD lens, it refrains from an in-depth examination of issues concerning equity and inclusivity, opening doors for subsequent research to address these aspects crucial for holistic urban development.

Keywords: Rotterdam zuid, transport oriented development, carbon emissions, low-carbon design, cross-scale design, data-supported design

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Authors: Mohammadhossein Moghimi, Robert Stone, Pia Rotshtein

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Recording psychological and physiological correlates of human performance within virtual environments and interpreting their impacts on human engagement, ‘immersion’ and related emotional or ‘effective’ states is both academically and technologically challenging. By exposing participants to an effective, real-time (game-like) virtual environment, designed and evaluated in an earlier study, a psychophysiological database containing the EEG, GSR and Heart Rate of 30 male and female gamers, exposed to 10 games, was constructed. Some 174 features were subsequently identified and extracted from a number of windows, with 28 different timing lengths (e.g. 2, 3, 5, etc. seconds). After reducing the number of features to 30, using a feature selection technique, K-Nearest Neighbour (KNN) and Support Vector Machine (SVM) methods were subsequently employed for the classification process. The classifiers categorised the psychophysiological database into four effective clusters (defined based on a 3-dimensional space – valence, arousal and dominance) and eight emotion labels (relaxed, content, happy, excited, angry, afraid, sad, and bored). The KNN and SVM classifiers achieved average cross-validation accuracies of 97.01% (±1.3%) and 92.84% (±3.67%), respectively. However, no significant differences were found in the classification process based on effective clusters or emotion labels.

Keywords: virtual reality, effective computing, effective VR, emotion-based effective physiological database

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Authors: Tomás Speziale

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This paper aims to analyse how Ian Hacking states the theoretical basis of his research on the classification of people. Although all his early philosophical education had been based in Foucault, it is also true that Erving Goffman’s perspective provided him with epistemological and methodological tools for understanding face-to-face relationships. Hence, all his works must be thought of as social science texts that combine the research on how the individuals are constituted ‘top-down’ (as in Foucault), with the inquiry into how people renegotiate ‘bottom-up’ the classifications about them. Thus, Hacking´s proposal constitutes a middle ground between the French Philosopher and the American Sociologist. Placing himself between both authors allows Hacking to build a frame that is expected to adjust to Social Sciences’ main particularity: the fact that they study interactive kinds. These are kinds of people, which imply that those who are classified can change in certain ways that prompt the need for changing previous classifications themselves. It is all about the interaction between the labelling of people and the people who are classified. Consequently, understanding the way in which Hacking uses Foucault’s and Goffman’s theories is essential to fully comprehend the social dynamic between individuals and concepts, what Bert Hansen had called dialectical realism. His theoretical proposal, therefore, is not only valuable because it combines diverse perspectives, but also because it constitutes an utterly original and relevant framework for Sociological theory and particularly for Criminology.

Keywords: classification of people, Foucault's archaeology, Goffman's interpersonal sociology, interactive kinds

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Authors: Evgeny A. Rybakov, Dmitry P. Starikov

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This article contains information about creating special unit for scanning 3D objects different nature, different materials, for example plastic, plaster, cardboard, wood, metal and etc. The main part of the unit is infrared transducer, which is sends the wave to the object and receive back wave for calculating distance. After that, microcontroller send to PC data, and computer program create model for printing from the plastic, gypsum, brass, etc.

Keywords: clutch, infrared, microcontroller, plastic, shaft, stage

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Authors: Gramza-Michalowska Anna, Skrety Joanna, Anna Zywica, Kobus-Cisowska Joanna, Kmiecik Dominik, Korczak Jozef

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Background: Modern consumer highly appreciates the positive influence of consumed products on well-being and overall health. High acceptance of new food is a result of intensified research showing many proofs confirming that food offers significant prophylactic and therapeutic potential, next to its basic nutritional function. Objective: Proposition of the technology of unsweetened oat drinks enriched with plant extracts for pro-health oriented individuals. We investigated the effects of selected plant extracts addition on antioxidative capacity and consumer’s acceptance of drinks as representative of all day diet product. Methods: The analysis of the basic composition and antioxidant properties of the drinking product was conducted. Basic composition included protein, lipids and fiber content. Antioxidant capacity of drink was evaluated with use radical scavenging methods (DPPH, ABTS), ORAC value and FRAP. Proposed drink as new product was also characterized with sensory analysis, which included color, aroma, taste, consistency and overall acceptance. Results: Results showed that addition of plant extracts into a oat drink allowed to enhance its antioxidant potential and influenced significantly its sensory values. The preferred composition and properties of designed beverage permit claim that it can have a positive impact on the health of the consumers. Conclusion: Designed oat drink would be an answer for pro-healthy life style of the consumers. Results showed that product with plant extracts addition would be accepted by the consumers and because of its antioxidative potential could be an important factor in prevention of free radicals influence on human organism.

Keywords: phytonutrients, pro-health, well-being, antioxidant potential, sensory value

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Authors: Fruzsina Luca Kézér, Viktor Jurkovich, Ottó Szenci, János Tőzsér, Levente Kovács

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Positive interaction between people and animals could have a favorable effect on the welfare and production by reducing stress levels. However, to the repeated contact with humans (e.g. farm staff, veterinarians or herdsmen), animals may respond with escape behavior or avoidance, which both have negative effects on the ease of handling, welfare and may lead to the expression of aggressive behaviors. Rough or aversive handling can impair health and the function of the cardiac autonomic activity due to fear and stress, which also can be determined by certain parameters of heart rate variability (HRV). Although the essential relationships between fear from humans and basal tone of the autonomic nervous system were described by the authors previously, several questions remained unclear in terms of the associations between different coping strategies (behavioral syndromes) of the animals and physiological responsiveness to humans. The main goal of this study was to find out whether human behavior and emotions to the animals have an impact on cardiac function and behavior of animals with different coping styles in response situations. Therefore, in the present study, special (fear, approaching, restraint, novel arena, novel object) tests were performed on healthy, 2-year old heifers (n = 104) differing in coping styles [reactive (passive) vs. proactive (active) coping]. Animals were categorized as reactive or proactive based on the following tests: 1) aggressive behavior at the feeding bunk, 2) avoidance from an approaching person, 3) immobility, and 4) daily activity (number of posture changes). Heart rate, the high frequency (HF) component of HRV as a measure of vagal activity and the ratio between the low frequency (LF) and HF components (LF/HF ratio) as a parameter of sympathetic nervous system activity were calculated for all individual during lying posture (baseline) and for response situations in novel object, novel arena, and unfamiliar person tests (both for 5 min), respectively. The differences between baseline and response were compared between groups. Higher sympathetic (higher heart rates and LF/HF ratios) and lower parasympathetic activity (lower HF) was found for proactive animals in response situations than for reactive (passive) animals either during the novel object, the novel arena and the unfamiliar person test. It suggests that animals with different behavioral traits differ in their immediate autonomic adaptation to novelty and people. Based on our preliminary results, it seems, that the analysis of HRV can help to understand the physiological manifestation of responsiveness to novelty and human presence in dairy cattle with different behavioral syndromes.

Keywords: behavioral syndromes, human-cattle interaction, novel arena test, physiological responsiveness, proactive coping, reactive coping

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Authors: Stephanie Fabris Russo, Daiane Costa Guimarães, Jonas Pedro Fabris, Maria Emilia Camargo, Suzana Leitão Russo, José Augusto Andrade Filho

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Among renewable energy sources, solar energy is the one that has stood out. Solar radiation can be used as a thermal energy source and can also be converted into electricity by means of effects on certain materials, such as thermoelectric and photovoltaic panels. These panels are often used to generate energy in homes, buildings, arenas, etc., and have low pollution emissions. Thus, a technological prospecting was performed to find patents related to the use of photovoltaic plates in urban residences. The patent search was based on ESPACENET, associating the keywords photovoltaic and home, where we found 136 patent documents in the period of 1994-2015 in the fields title and abstract. Note that the years 2009, 2010, 2011, 2012, 2013 and 2014 had the highest number of applicants, with respectively, 11, 13, 23, 29, 15 and 21. Regarding the country that deposited about this technology, it is clear that China leads with 67 patent deposits, followed by Japan with 38 patents applications. It is important to note that most depositors, 50% are companies, 44% are individual inventors and only 6% are universities. On the International Patent classification (IPC) codes, we noted that the most present classification in results was H02J3/38, which represents provisions in parallel to feed a single network by two or more generators, converters or transformers. Among all categories, there is the H session, which means Electricity, with 70% of the patents.

Keywords: photovoltaic, urban residences, technology forecasting, prospecting

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Authors: Haifeng Wang, Haili Zhang

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Most movie recommendation systems have been developed for customers to find items of interest. This work introduces a predictive model usable by small and medium-sized enterprises (SMEs) who are in need of a data-based and analytical approach to stock proper movies for local audiences and retain more customers. We used classification models to extract features from thousands of customers’ demographic, behavioral and social information to predict their movie genre preference. In the implementation, a Gaussian kernel support vector machine (SVM) classification model and a logistic regression model were established to extract features from sample data and their test error-in-sample were compared. Comparison of error-out-sample was also made under different Vapnik–Chervonenkis (VC) dimensions in the machine learning algorithm to find and prevent overfitting. Gaussian kernel SVM prediction model can correctly predict movie genre preferences in 85% of positive cases. The accuracy of the algorithm increased to 93% with a smaller VC dimension and less overfitting. These findings advance our understanding of how to use machine learning approach to predict customers’ preferences with a small data set and design prediction tools for these enterprises.

Keywords: computational social science, movie preference, machine learning, SVM

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Authors: Jiameng Wang, Yunfei Yin, Xiyu Deng

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Parallel optimization is one of the important research topics of data mining at this stage. Taking Classification and Regression Tree (CART) parallelization as an example, this paper proposes a parallel data mining algorithm based on SSP-OGini-PCCP. Aiming at the problem of choosing the best CART segmentation point, this paper designs an S-SP model without data association; and in order to calculate the Gini index efficiently, a parallel OGini calculation method is designed. In addition, in order to improve the efficiency of the pruning algorithm, a synchronous PCCP pruning strategy is proposed in this paper. In this paper, the optimal segmentation calculation, Gini index calculation, and pruning algorithm are studied in depth. These are important components of parallel data mining. By constructing a distributed cluster simulation system based on SPARK, data mining methods based on SSP-OGini-PCCP are tested. Experimental results show that this method can increase the search efficiency of the best segmentation point by an average of 89%, increase the search efficiency of the Gini segmentation index by 3853%, and increase the pruning efficiency by 146% on average; and as the size of the data set increases, the performance of the algorithm remains stable, which meets the requirements of contemporary massive data processing.

Keywords: classification, Gini index, parallel data mining, pruning ahead

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Authors: Neda Orak, Mostafa Zarei

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Environmental assessment is an important session in environment management. Since various methods and techniques have been produces and implemented. Remote sensing (RS) is widely used in many scientific and research fields such as geology, cartography, geography, agriculture, forestry, land use planning, environment, etc. It can show earth surface objects cyclical changes. Also, it can show earth phenomena limits on basis of electromagnetic reflectance changes and deviations records. The research has been done on mangrove forests assessment by RS techniques. Mangrove forests quantitative analysis in Basatin and Bidkhoon estuaries was the aim of this research. It has been done by Landsat satellite images from 1975- 2013 and match to ground control points. This part of mangroves are the last distribution in northern hemisphere. It can provide a good background to improve better management on this important ecosystem. Landsat has provided valuable images to earth changes detection to researchers. This research has used MSS, TM, +ETM, OLI sensors from 1975, 1990, 2000, 2003-2013. Changes had been studied after essential corrections such as fix errors, bands combination, georeferencing on 2012 images as basic image, by maximum likelihood and IPVI Index. It was done by supervised classification. 2004 google earth image and ground points by GPS (2010-2012) was used to compare satellite images obtained changes. Results showed mangrove area in bidkhoon was 1119072 m2 by GPS and 1231200 m2 by maximum likelihood supervised classification and 1317600 m2 by IPVI in 2012. Basatin areas is respectively: 466644 m2, 88200 m2, 63000 m2. Final results show forests have been declined naturally. It is due to human activities in Basatin. The defect was offset by planting in many years. Although the trend has been declining in recent years again. So, it mentioned satellite images have high ability to estimation all environmental processes. This research showed high correlation between images and indexes such as IPVI and NDVI with ground control points.

Keywords: IPVI index, Landsat sensor, maximum likelihood supervised classification, Nayband National Park

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Authors: Benjamin Beltzung, Marie Pelé, Julien P. Renoult, Cédric Sueur

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To this day, it is not known if drawing is specifically human behavior or if this behavior finds its origins in ancestor species. An interesting window to enlighten this question is to analyze the drawing behavior in genetically close to human species, such as non-human primate species. A good candidate for this approach is the orangutan, who shares 97% of our genes and exhibits multiple human-like behaviors. Focusing on figurative aspects may not be suitable for orangutans’ drawings, which may appear as scribbles but may have meaning. A manual feature selection would lead to an anthropocentric bias, as the features selected by humans may not match with those relevant for orangutans. In the present study, we used deep learning to analyze the drawings of a female orangutan named Molly († in 2011), who has produced 1,299 drawings in her last five years as part of a behavioral enrichment program at the Tama Zoo in Japan. We investigate multiple ways to decipher Molly’s drawings. First, we demonstrate the existence of differences between seasons by training a deep learning model to classify Molly’s drawings according to the seasons. Then, to understand and interpret these seasonal differences, we analyze how the information spreads within the network, from shallow to deep layers, where early layers encode simple local features and deep layers encode more complex and global information. More precisely, we investigate the impact of feature complexity on classification accuracy through features extraction fed to a Support Vector Machine. Last, we leverage style transfer to dissociate features associated with drawing style from those describing the representational content and analyze the relative importance of these two types of features in explaining seasonal variation. Content features were relevant for the classification, showing the presence of meaning in these non-figurative drawings and the ability of deep learning to decipher these differences. The style of the drawings was also relevant, as style features encoded enough information to have a classification better than random. The accuracy of style features was higher for deeper layers, demonstrating and highlighting the variation of style between seasons in Molly’s drawings. Through this study, we demonstrate how deep learning can help at finding meanings in non-figurative drawings and interpret these differences.

Keywords: cognition, deep learning, drawing behavior, interpretability

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Authors: Brigitta Szabo, Miklosi Monika

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Background/Aims: In early childhood, the context of development is the caregiver-child relationship. Maternal attachment style plays a major role in the intergenerational transmission of psychopathology. The aim of this study was to explore the relationship between the mothers’ attachment style, mindful parenting, and perception of the child. Method: Data was collected from 144 non-clinical mothers who have a child below the age of 3 years. Mothers completed self-report questionnaires, including the following scales: a demographic questionnaire, Attachment Style Questionnaire (ASQ), Interpersonal Mindfulness in Parenting Scale (IMP), and the Mothers’ Object Relations Scale (MORS-SF). K-means cluster analysis was used to identify the mothers’ attachment styles. Mediation analyses with Mothers’ Object Relations Scale (MORS-SF) positive emotions and dominance subscales as dependent variables, mothers’ attachment style (ASQ) as an independent variable, and mindful parenting (IMP) as a mediator were conducted. Results: Four attachment styles (secure, preoccupied, fearful, dismissing) were identified. The relationship between mothers’ attachment style and mindful parenting was significant (R2 = .51; F(4,139) = 36.60; p < .001). Compared to the secure attachment style as a reference group, both preoccupied and dismissing styles were related to lower levels of mindful parenting; however, this relationship was the strongest in case of fearful style. In mediation analysis the direct effects of mothers’ attachment style on the perception of the child were not significant (MORS positive emotions: R2= .29; F(5,138) = 11.22; p < .001; MORS dominance: R2= .39 F(5,138) = 17.54, p < .001). However, indirect effects through mindful parenting were significant; higher levels of mindful parenting were associated with higher levels of MORS positive emotions and lower levels of MORS dominance. Conclusions: These findings suggest that attachment styles are related to the perception of the child through mindful parenting. Mindfulness-based parenting training might be useful in case of attachment-related problems to improve the parent-child relationship.

Keywords: mindfulness, mindful parenting, attachement, perception

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Authors: M. Eckert, D. Mendes, J P. Gonçalves, C. Moço, M. Oliveira

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The intensive extraction of natural aggregates leads to both depletion of natural resources and unwanted environmental impacts. On the other hand, uncontrolled disposal of Construction and Demolition Wastes (C&DW) causes the lifetime reduction of landfills. It is known that the European Union produces, each year, about 850 million tons of C&DW. For all the member States of the European Union, one of the milestones to be reached by 2020, according to the Resource Efficiency Roadmap (COM (2011) 571) of the European Commission, is to recycle 70% of the C&DW. In this work, properties of different types of recycled C&DW aggregates and natural aggregates were compared. Assays were performed according to European Standards (EN 13285; EN 13242+A1; EN 12457-4; EN 12620; EN 13139) for the characterization of there: physical, mechanical and chemical properties. Not standardized tests such as water absorption over time, mass stability and post compaction sieve analysis were also carried out. The tested recycled C&DW aggregates were classified according to the requirements of the European Standards regarding there potential use in concrete, mortar, unbound layers of road pavements and embankments. The results of the physical and mechanical properties of recycled C&DW aggregates indicated, in general, lower quality properties when compared to natural aggregates, particularly, for concrete preparation and unbound layers of road pavements. The results of the chemical properties attested that the C&DW aggregates constitute no environmental risk. It was concluded that recycled aggregates produced from C&DW have the potential to be used in many applications.

Keywords: recycled aggregate, sustainability, aggregate properties, European Standard Classification

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Authors: Kwan Hee Han

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In today’s business environments, frequent change of customer requirements is a tough challenge to manufacturing company. To cope with these challenges, a production planning and scheduling (PP&S) function might be established to provide accountability for both customer service and operational efficiency. Nowadays, many manufacturing firms have utilized PP&S software solutions to generate a realistic production plan and schedule to adapt to external changes efficiently. However, companies which consider the introduction of PP&S software solution, still have difficulties for selecting adequate solution to meet their specific needs. Since the task of PP&S is the one of major building blocks of SCM (Supply Chain Management) architecture, which deals with short term decision making in the production process of SCM, it is needed that the functionalities of PP&S should be analysed within the whole SCM process. The aim of this paper is to analyse the PP&S functionalities and its system architecture from the SCM perspective by using the criteria of level of planning hierarchy, major 4 SCM processes and problem-solving approaches, and finally propose a classification framework of PP&S solutions to facilitate the comparison among various commercial software solutions. By using proposed framework, several major PP&S solutions are classified and positioned according to their functional characteristics in this paper. By using this framework, practitioners who consider the introduction of computerized PP&S solutions in manufacturing firms can prepare evaluation and benchmarking sheets for selecting the most suitable solution with ease and in less time.

Keywords: production planning, production scheduling, supply chain management, the advanced planning system

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Authors: Ilma Robo, Saimir Heta, Vera Ostreni, Elsaida Agrushi, Eduart Kapaj

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Introduction: Cyclic hormonal fluctuations are known from literature to have a clinically visible effects on gingival tissue reactions, to the diagnosed processes of gingival inflammation. Materials and methods: At a total of 47 female patients, ad-hock presented at the University Clinic, were recorded data on effect of hormonal oscillations at periodontal treatment protocol. Oral examination was performed on soft tissue of gingiva and the oral mucous membrane, always respecting the air-drying procedure and then checking with free eye differences in oral mucosal relief. After the patients were informed about the study protocol, the purpose of the study and the ongoing procedure, verbal consensus was required. Results: The study was conducted in a total of 47 patients, out of which 13 patients were under the gingivitis classification, and 24 patients under the periodontal classification. Patients included in the study are divided by age, cycle week respectively 1,2,3 and 4.The younger age of female patients is more prone to the appearance of gingivitis, which is further aggravated by the effects of sexual hormones and the effect of the controlled or non-regulated fluctuations of the latter. Conclusions: The healing process is more fuel-intensive in the absence of high hormone levels, as they are these pro-inflammatory hormones, both in or near the ho Younger women are more open to volunteering in studies that record individual and study data that may last in time.

Keywords: gingiva, hormonal oscillations, female patients, mucosa, periodontal non-surgical treatment

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Authors: Fernanda A. P. Peres, Thiago N. Peres, Flavio S. Fogliatto, Michel J. Anzanello

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Batch processes are widely used in food industry and have an important role in the production of high added value products, such as chocolate. Process performance is usually described by variables that are monitored as the batch progresses. Data arising from these processes are likely to display a strong correlation-autocorrelation structure, and are usually monitored using control charts based on multiway principal components analysis (MPCA). Process control of a new batch is carried out comparing the trajectories of its relevant process variables with those in a reference set of batches that yielded products within specifications; it is clear that proper determination of the reference set is key for the success of a correct signalization of non-conforming batches in such quality control schemes. In chocolate manufacturing, misclassifications of non-conforming batches in the conching phase may lead to significant financial losses. In such context, the accuracy of process control grows in relevance. In addition to that, the main assumption in MPCA-based monitoring strategies is that all batches are synchronized in duration, both the new batch being monitored and those in the reference set. Such assumption is often not satisfied in chocolate manufacturing process. As a consequence, traditional techniques as MPCA-based charts are not suitable for process control and monitoring. To address that issue, the objective of this work is to compare the performance of three dynamic time warping (DTW) methods in the alignment and synchronization of chocolate conching process variables’ trajectories, aimed at properly determining the reference distribution for multivariate statistical process control. The power of classification of batches in two categories (conforming and non-conforming) was evaluated using the k-nearest neighbor (KNN) algorithm. Real data from a milk chocolate conching process was collected and the following variables were monitored over time: frequency of soybean lecithin dosage, rotation speed of the shovels, current of the main motor of the conche, and chocolate temperature. A set of 62 batches with durations between 495 and 1,170 minutes was considered; 53% of the batches were known to be conforming based on lab test results and experts’ evaluations. Results showed that all three DTW methods tested were able to align and synchronize the conching dataset. However, synchronized datasets obtained from these methods performed differently when inputted in the KNN classification algorithm. Kassidas, MacGregor and Taylor’s (named KMT) method was deemed the best DTW method for aligning and synchronizing a milk chocolate conching dataset, presenting 93.7% accuracy, 97.2% sensitivity and 90.3% specificity in batch classification, being considered the best option to determine the reference set for the milk chocolate dataset. Such method was recommended due to the lowest number of iterations required to achieve convergence and highest average accuracy in the testing portion using the KNN classification technique.

Keywords: batch process monitoring, chocolate conching, dynamic time warping, reference set distribution, variable duration

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Authors: Vladimir A. Todiras

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Spatio-temporal models provide new possibilities for real-time action in pest risk analysis. It should be noted that estimation of the possibility and probability of introduction of a pest and of its economic consequences involves many uncertainties. We present a new mapping technique that assesses pest invasion risk using online BioClass software. BioClass is a GIS tool designed to solve multiple-criteria classification and optimization problems based on fuzzy logic and level set methods. This research describes a method for predicting the potential establishment and spread of a plant pest into new areas using a case study: corn rootworm (Diabrotica spp.), tomato leaf miner (Tuta absoluta) and plum fruit moth (Grapholita funebrana). Our study demonstrated that in BioClass we can combine fuzzy logic and geographic information systems with knowledge of pest biology and environmental data to derive new information for decision making. Pests are sensitive to a warming climate, as temperature greatly affects their survival and reproductive rate and capacity. Changes have been observed in the distribution, frequency and severity of outbreaks of Helicoverpa armigera on tomato. BioClass has demonstrated to be a powerful tool for applying dynamic models and map the potential future distribution of a species, enable resource to make decisions about dangerous and invasive species management and control.

Keywords: classification, model, pest, risk

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Authors: Paul Licina, Emma M. Johnston, David Lisle, Mark Young, Chris Brady

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Background: Lumbar pars pathology is a common cause of pain in the growing spine. It can be seen in young athletes participating in at-risk sports and can affect sporting performance and long-term health due to its resistance to traditional management. There is a current lack of consensus of classification and treatment for pars injuries. Previous systems used CT to stage pars defects but could not assess early stress reactions. A modified classification is proposed that considers findings on MRI, significantly improving early treatment guidance. The treatment protocol is designed for patients aged 5 to 19 years. Method: Clinical screening identifies patients with a low, medium, or high index of suspicion for lumbar pars injury using patient age, sport participation and pain characteristics. MRI of the at-risk cohort enables augmentation of existing CT-based classification while avoiding ionising radiation. Patients are classified into five categories based on MRI findings. A type 0 lesion (stress reaction) is present when CT is normal and MRI shows high signal change (HSC) in the pars/pedicle on T2 images. A type 1 lesion represents the ‘early defect’ CT classification. The group previously referred to as a 'progressive stage' defect on CT can be split into 2A and 2B categories. 2As have HSC on MRI, whereas 2Bs do not. This distinction is important with regard to healing potential. Type 3 lesions are terminal stage defects on CT, characterised by pseudarthrosis. MRI shows no HSC. Results: Stress reactions (type 0) and acute fractures (1 and 2a) can heal and are treated in a custom-made hard brace for 12 weeks. It is initially worn 23 hours per day. At three weeks, patients commence basic core rehabilitation. At six weeks, in the absence of pain, the brace is removed for sleeping. Exercises are progressed to positions of daily living. Patients with continued pain remain braced 23 hours per day without exercise progression until becoming symptom-free. At nine weeks, patients commence supervised exercises out of the brace for 30 minutes each day. This allows them to re-learn muscular control without rigid support of the brace. At 12 weeks, bracing ceases and MRI is repeated. For patients with near or complete resolution of bony oedema and healing of any cortical defect, rehabilitation is focused on strength and conditioning and sport-specific exercise for the full return to activity. The length of this final stage is approximately nine weeks but depends on factors such as development and level of sports participation. If significant HSC remains on MRI, CT scan is considered to definitively assess cortical defect healing. For these patients, return to high-risk sports is delayed for up to three months. Chronic defects (2b and 3) cannot heal and are not braced, and rehabilitation follows traditional protocols. Conclusion: Appropriate clinical screening and imaging with MRI can identify pars pathology early. In those with potential for healing, we propose hard bracing and appropriate rehabilitation as part of a multidisciplinary management protocol. The validity of this protocol will be tested in future studies.

Keywords: adolescents, MRI classification, pars interticularis, treatment protocol

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Authors: Narjes Abbasabadi

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As the world continues to urbanize and suburbanize, where suburbanization associated with mass sprawl has been the dominant form of this expansion, sustainable development challenges will be more concerned. Sprawling, characterized by low density and automobile dependency, presents significant environmental issues regarding energy consumption and Co2 emissions. This paper examines the vertical expansion of suburbs integrated into mass transit nodes as a planning strategy for boosting density, intensification of land use, conversion of single family homes to multifamily dwellings or mixed use buildings and development of viable alternative transportation choices. It analyzes the spatial patterns of tall building transit-oriented development (TB-TOD) of suburban regions in Sydney (Australia), Toronto (Canada), and Washington D.C. (United States). The main objectives of this research seek to understand the effect of the new morphology of suburban tall, the physical dimensions of individual buildings and their arrangement at a larger scale with energy efficiency. This study aims to answer these questions: 1) why and how can the potential phenomenon of vertical expansion or high-rise development be integrated into suburb settings? 2) How can this phenomenon contribute to an overall denser development of suburbs? 3) Which spatial pattern or typologies/ sub-typologies of the TB-TOD model do have the greatest energy efficiency? It addresses these questions by focusing on 1) energy, heat energy demand (excluding cooling and lighting) related to design issues at two levels: macro, urban scale and micro, individual buildings—physical dimension, height, morphology, spatial pattern of tall buildings and their relationship with each other and transport infrastructure; 2) Examining TB-TOD to provide more evidence of how the model works regarding ridership. The findings of the research show that the TB-TOD model can be identified as the most appropriate spatial patterns of tall buildings in suburban settings. And among the TB-TOD typologies/ sub-typologies, compact tall building blocks can be the most energy efficient one. This model is associated with much lower energy demands in buildings at the neighborhood level as well as lower transport needs in an urban scale while detached suburban high rise or low rise suburban housing will have the lowest energy efficiency. The research methodology is based on quantitative study through applying the available literature and static data as well as mapping and visual documentations of urban regions such as Google Earth, Microsoft Bing Bird View and Streetview. It will examine each suburb within each city through the satellite imagery and explore the typologies/ sub-typologies which are morphologically distinct. The study quantifies heat energy efficiency of different spatial patterns through simulation via GIS software.

Keywords: energy efficiency, spatial pattern, suburb, tall building transit-oriented development (TB-TOD)

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Authors: Rafael Santos, Clara Pimenta Do Vale, Barbara Bogoni, Poul Henning Kirkegaard

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In the last decades, the relevance of hand drawing has decreased in the scope of architectural education. However, some schools continue to recognize its decisive role, not only in the architectural design teaching, but in the whole of architectural training. With this paper it is intended to present the results of a research developed on the following problem: the practise of hand drawing as a premier form of representation in architectural design teaching. The research had as its object the educational model of the Faculty of Architecture of the University of Porto (FAUP) and was led by three main objectives: to identify the circumstance that promoted hand drawing as a form of representation in FAUP's model; to characterize the types of hand drawing and their role in that model; to determine the particularities of hand drawing as a premier form of representation in architectural design teaching. Methodologically, the research was conducted according to a qualitative embedded single-case study design. The object – i.e., the educational model – was approached in FAUP case considering its Context and three embedded unities of analysis: the educational Purposes, Principles and Practices. In order to guide the procedures of data collection and analysis, a Matrix for the Characterization (MCC) was developed. As a methodological tool, the MCC allowed to relate the three embedded unities of analysis with the three main sources of evidence where the object manifests itself: the professors, expressing how the model is Assumed; the architectural design classes, expressing how the model is Achieved; and the students, expressing how the model is Acquired. The main research methods used were the naturalistic and participatory observation, in-person-interview and documentary and bibliographic review. The results reveal that the educational model of FAUP – following the model of the former Porto School – was largely due to the methodological foundations created with the hand drawing teaching-learning processes. In the absence of a culture of explicit theoretical elaboration or systematic research, hand drawing was the support for the continuity of the school, an expression of a unified thought about what should be the reflection and practice of architecture. As a form of representation, hand drawing plays a transversal role in the entire educational model, since its purposes are not limited to the conception of architectural design – it is also a means for perception, analysis and synthesis. Regarding the architectural design teaching, there seems to be an understanding of three complementary dimensions of didactics: the instrumental, methodological and propositional dimension. At FAUP, hand drawing is recognized as the common denominator among these dimensions, according to the idea of "globality of drawing". It is expected that the knowledge base developed in this research may have three main contributions: to contribute to the maintenance and valorisation of FAUP’s model; through the precise description of the methodological procedures, to contribute by transferability to similar studies; through the critical and objective framework of the problem underlying the hand drawing in architectural design teaching, to contribute to the broader discussion concerning the contemporary challenges on architectural education.

Keywords: architectural design teaching, architectural education, forms of representation, hand drawing

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Authors: Walid Moudani

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The healthcare environment is generally perceived as being information rich yet knowledge poor. However, there is a lack of effective analysis tools to discover hidden relationships and trends in data. In fact, valuable knowledge can be discovered from application of data mining techniques in healthcare system. In this study, a proficient methodology for the extraction of significant patterns from the coronary heart disease warehouses for heart attack prediction, which unfortunately continues to be a leading cause of mortality in the whole world, has been presented. For this purpose, we propose to enumerate dynamically the optimal subsets of the reduced features of high interest by using rough sets technique associated to dynamic programming. Therefore, we propose to validate the classification using Random Forest (RF) decision tree to identify the risky heart disease cases. This work is based on a large amount of data collected from several clinical institutions based on the medical profile of patient. Moreover, the experts’ knowledge in this field has been taken into consideration in order to define the disease, its risk factors, and to establish significant knowledge relationships among the medical factors. A computer-aided system is developed for this purpose based on a population of 525 adults. The performance of the proposed model is analyzed and evaluated based on set of benchmark techniques applied in this classification problem.

Keywords: multi-classifier decisions tree, features reduction, dynamic programming, rough sets

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Authors: I. Stathopoulos, V. Syrgiamiotis, E. Karavasilis, A. Ploussi, I. Nikas, C. Hatzigiorgi, K. Platoni, E. P. Efstathopoulos

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Introduction: Brain and central nervous system (CNS) tumors form the second most common group of cancer in children, accounting for 30% of all childhood cancers. MRI is the key imaging technique used for the visualization and management of pediatric brain tumors. Initial characterization of tumors from MRI scans is usually performed via a radiologist’s visual assessment. However, different brain tumor types do not always demonstrate clear differences in visual appearance. Using only conventional MRI to provide a definite diagnosis could potentially lead to inaccurate results, and so histopathological examination of biopsy samples is currently considered to be the gold standard for obtaining definite diagnoses. Machine learning is defined as the study of computational algorithms that can use, complex or not, mathematical relationships and patterns from empirical and scientific data to make reliable decisions. Concerning the above, machine learning techniques could provide effective and accurate ways to automate and speed up the analysis and diagnosis for medical images. Machine learning applications in radiology are or could potentially be useful in practice for medical image segmentation and registration, computer-aided detection and diagnosis systems for CT, MR or radiography images and functional MR (fMRI) images for brain activity analysis and neurological disease diagnosis. Purpose: The objective of this study is to provide an automated tool, which may assist in the imaging evaluation and classification of brain neoplasms in pediatric patients by determining the glioma type, grade and differentiating between different brain tissue types. Moreover, a future purpose is to present an alternative way of quick and accurate diagnosis in order to save time and resources in the daily medical workflow. Materials and Methods: A cohort, of 80 pediatric patients with a diagnosis of posterior fossa tumor, was used: 20 ependymomas, 20 astrocytomas, 20 medulloblastomas and 20 healthy children. The MR sequences used, for every single patient, were the following: axial T1-weighted (T1), axial T2-weighted (T2), FluidAttenuated Inversion Recovery (FLAIR), axial diffusion weighted images (DWI), axial contrast-enhanced T1-weighted (T1ce). From every sequence only a principal slice was used that manually traced by two expert radiologists. Image acquisition was carried out on a GE HDxt 1.5-T scanner. The images were preprocessed following a number of steps including noise reduction, bias-field correction, thresholding, coregistration of all sequences (T1, T2, T1ce, FLAIR, DWI), skull stripping, and histogram matching. A large number of features for investigation were chosen, which included age, tumor shape characteristics, image intensity characteristics and texture features. After selecting the features for achieving the highest accuracy using the least number of variables, four machine learning classification algorithms were used: k-Nearest Neighbour, Support-Vector Machines, C4.5 Decision Tree and Convolutional Neural Network. The machine learning schemes and the image analysis are implemented in the WEKA platform and MatLab platform respectively. Results-Conclusions: The results and the accuracy of images classification for each type of glioma by the four different algorithms are still on process.

Keywords: image classification, machine learning algorithms, pediatric MRI, pediatric oncology

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Authors: Piotr Prewysz-Kwinto, Grazyna Voss

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In June 2013, the European Parliament and the Council adopted a directive on financial reporting (Directive 2013/34/EU). The main objective was to simplify the principles of the preparation of financial statements, including the principles of the presentation and disclosures of financial information by adapting reporting burdens to the type and size of an undertaking. Therefore, the Directive introduced a classification of all undertakings into five groups, i.e. micro, small, medium-sized, large and public-interest entities, and defined in detail the classification criteria. The principles of the preparation of financial statements and the presentation of financial information as well as applicable simplifications were defined for each group. The EU Member States had to implement the provisions of Directive 34 relating to accounting and financial reporting into domestic norms until January 1, 2016. In Poland, the provisions of Directive 34 were implemented into domestic accounting norms specified in the Polish Accounting Act on a gradual basis. On July 11, 2014, the Polish Parliament adopted an amendment to the Act, introducing the Directive's solutions for micro-undertakings and on July 23, 2015, for the remaining undertakings. The aim of this paper is to present Polish solutions relating to financial reporting after the implementation of Directive 34 and the results of the survey conducted among accountants regarding the evaluation of the implemented simplifications for micro and small undertakings.

Keywords: accounting standards, financial reporting, financial statement, simplification

Procedia PDF Downloads 275