Search results for: fracture classification

Authors: Robert Poenaru, Mihail Pleşa

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In this work, a solution that addresses the protection of pre-trained neural networks is developed: Singularization. This method involves applying permutations to the weight matrices of a pre-trained model, introducing a form of structured noise that obscures the original model’s architecture. These permutations make it difficult for an attacker to reconstruct the original model, even if the permuted weights are obtained. Experimental benchmarks indicate that the application of singularization has a profound impact on model performance, often degrading it to the point where retraining from scratch becomes necessary to recover functionality, which is particularly effective for securing intellectual property in neural networks. Moreover, unlike other approaches, singularization is lightweight and computationally efficient, which makes it well suited for resource-constrained environments. Our experiments also demonstrate that this technique performs efficiently in various image classification tasks, highlighting its broad applicability and practicality in real-world scenarios.

Keywords: machine learning, ANE, CNN, security

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Authors: Yunxiang Fang

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With the aging trend continuing to accelerate, open space is important for the daily life of the elderly, and its old-age friendliness is worthy of attention. Based on behavioral observation and literature research, this paper studies the behavior of the elderly in urban open space. Through the investigation, classification and quantitative analysis of the activity types, time characteristics and spatial behavior order of the elderly in Xicheng Square in Hangzhou, it summarizes the square space suitable for the psychological needs, physiology and activity needs of the elderly, combined with the basis of literature research. Finally, the suggestions for the improvement of the old-age friendship of Xicheng Square are put forward, from the aspects of microclimate, safety and accessibility, space richness and service facility quality.

Keywords: behavior characteristics, old-age friendliness, open space, square

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Authors: Bouhafara Djaber, Menail Younes, Mesrafet Farouk, Aissaoui Mohammed Islem

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This article presents results of experimental investigations of the durability of (GFRP) composite exposed to typical environments of marine industries applications,The use of fiber-glass reinforced polyester composites in marine applications such as Hulls of voyage boats and hulls of small vessels for the military navy , this type of composite is becoming attractive because of their reduced weight and improved corrosion resistance. However,a deep understating of oil ageing effect on composite structures is essential to ensure long-term performance and durability. in this work evaluate the effect of oil ageing on absorptıon behavıor and ımpact properties of glass/polyester composites manufactured with two types of fiber fabrics (fibreglass mat and fiberglass woven roving) and isophthalic polyester resin. The specimens obtained from commercial (GFRP) profiles made of unsaturated polyester resin were subjected to immersion in (i) marine oil for boats and (ii) salt water at ambient temperature for up to 1 month. The effects of such exposure conditions on this types of profile we analysed in what concerns their (i) mass change,(ii) mechanical response in impact, namely on the mechanical response – oil immersion caused a higher level of degradation, compared with salt water immersion;fracture surface examination by scanning electron microscopy revealed delamination, fiber debonding and resin crumbling due to oil effect.

Keywords: Marine Engine Oil, Absorption, Polyester, Glass Fibre

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Authors: J. K. Alhassan, B. Attah, S. Misra

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Human beings have the ability to make logical decisions. Although human decision - making is often optimal, it is insufficient when huge amount of data is to be classified. medical dataset is a vital ingredient used in predicting patients health condition. In other to have the best prediction, there calls for most suitable machine learning algorithms. This work compared the performance of Artificial Neural Network (ANN) and Decision Tree Algorithms (DTA) as regards to some performance metrics using diabetes data. The evaluations was done using weka software and found out that DTA performed better than ANN. Multilayer Perceptron (MLP) and Radial Basis Function (RBF) were the two algorithms used for ANN, while RegTree and LADTree algorithms were the DTA models used. The Root Mean Squared Error (RMSE) of MLP is 0.3913,that of RBF is 0.3625, that of RepTree is 0.3174 and that of LADTree is 0.3206 respectively.

Keywords: artificial neural network, classification, decision tree algorithms, diabetes mellitus

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Authors: Shrabani Ghosh

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Automatic offensive language detection in social media has become a stirring task in today's NLP. Manual Offensive language detection is tedious and laborious work where automatic methods based on machine learning are only alternatives. Previous works have done sentiment analysis over social media in different ways such as supervised, semi-supervised, and unsupervised manner. Domain adaptation in a semi-supervised way has also been explored in NLP, where the source domain and the target domain are different. In domain adaptation, the source domain usually has a large amount of labeled data, while only a limited amount of labeled data is available in the target domain. Pretrained transformers like BERT, RoBERTa models are fine-tuned to perform text classification in an unsupervised manner to perform further pre-train masked language modeling (MLM) tasks. In previous work, hate speech detection has been explored in Gab.ai, which is a free speech platform described as a platform of extremist in varying degrees in online social media. In domain adaptation process, Twitter data is used as the source domain, and Gab data is used as the target domain. The performance of domain adaptation also depends on the cross-domain similarity. Different distance measure methods such as L2 distance, cosine distance, Maximum Mean Discrepancy (MMD), Fisher Linear Discriminant (FLD), and CORAL have been used to estimate domain similarity. Certainly, in-domain distances are small, and between-domain distances are expected to be large. The previous work finding shows that pretrain masked language model (MLM) fine-tuned with a mixture of posts of source and target domain gives higher accuracy. However, in-domain performance of the hate classifier on Twitter data accuracy is 71.78%, and out-of-domain performance of the hate classifier on Gab data goes down to 56.53%. Recently self-supervised learning got a lot of attention as it is more applicable when labeled data are scarce. Few works have already been explored to apply self-supervised learning on NLP tasks such as sentiment classification. Self-supervised language representation model ALBERTA focuses on modeling inter-sentence coherence and helps downstream tasks with multi-sentence inputs. Self-supervised attention learning approach shows better performance as it exploits extracted context word in the training process. In this work, a self-supervised attention mechanism has been proposed to detect hate speech on Gab.ai. This framework initially classifies the Gab dataset in an attention-based self-supervised manner. On the next step, a semi-supervised classifier trained on the combination of labeled data from the first step and unlabeled data. The performance of the proposed framework will be compared with the results described earlier and also with optimized outcomes obtained from different optimization techniques.

Keywords: attention learning, language model, offensive language detection, self-supervised learning

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Authors: Amanpreet Kaur

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Wavelet transform (WT) is a powerful statistical tool used in applied mathematics for signal and image processing. The different mother, wavelet basis function, has been compared to select the optimal wavelet function that represents the electromyogram signal characteristics of upper limb amputees. Four different EMG electrode has placed on different location of shoulder muscles. Twenty one wavelet functions from different wavelet families were investigated. These functions included Daubechies (db1-db10), Symlets (sym1-sym5), Coiflets (coif1-coif5) and Discrete Meyer. Using mean square error value, the significance of the mother wavelet functions has been determined for teres, pectorals, and infraspinatus around shoulder muscles. The results show that the best mother wavelet is the db3 from the Daubechies family for efficient classification of the signal.

Keywords: Daubechies, upper limb amputation, shoulder muscles, Symlets, Coiflets

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Authors: Kapil Pandey, Vishnu Goyal

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Spatial and temporal data analysis is very well known in the field of satellite image processing. When spatial data are correlated with time, series analysis it gives the significant results in change detection studies. In this paper the GIS and Remote sensing techniques has been used to find the change detection using time series satellite imagery of Uttarakhand state during the years of 1990-2010. Natural vegetation, urban area, forest cover etc. were chosen as main landuse classes to study. Landuse/ landcover classes within several years were prepared using satellite images. Maximum likelihood supervised classification technique was adopted in this work and finally landuse change index has been generated and graphical models were used to present the changes.

Keywords: GIS, landuse/landcover, spatial and temporal data, remote sensing

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Authors: Niloofar Bahramian, Mohammad Atai, Mohammad Reza Naimi-Jamal

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Rule of mixtures is the simple analytical model is used to predict various properties of composites before design. The aim of this study was to demonstrate the benefits and limitations of the Rule-of-Mixtures (ROM) for predicting bending modulus of a continuous and unidirectional fiber reinforced composites using in dental applications. The Composites were fabricated from light curing resin (with and without silica nanoparticles) and modified and non-modified fibers. Composite samples were divided into eight groups with ten specimens for each group. The bending modulus (flexural modulus) of samples was determined from the slope of the initial linear region of stress-strain curve on 2mm×2mm×25mm specimens with different designs: fibers corona treatment time (0s, 5s, 7s), fibers silane treatment (0%wt, 2%wt), fibers volume fraction (41%, 33%, 25%) and nanoparticles incorporation in resin (0%wt, 10%wt, 15%wt). To study the fiber and matrix interface after fracture, single edge notch beam (SENB) method and scanning electron microscope (SEM) were used. SEM also was used to show the nanoparticles dispersion in resin. Experimental results of bending modulus for composites made of both physical (corona) and chemical (silane) treated fibers were in reasonable agreement with linear ROM estimates, but untreated fibers or non-optimized treated fibers and poor nanoparticles dispersion did not correlate as well with ROM results. This study shows that the ROM is useful to predict the mechanical behavior of unidirectional dental composites but fiber-resin interface and quality of nanoparticles dispersion play important role in ROM accurate predictions.

Keywords: bending modulus, fiber reinforced composite, fiber treatment, rule-of-mixtures

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Authors: Mahmoud M. S. Albattah

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Urbanization results in the expansion of administrative boundaries, mainly at the periphery, ultimately leading to changes in landcover. Agricultural land, naturally vegetated land, and other land types are converted into residential areas with a high density of constructs, such as transportation systems and housing. In urban regions of rapid growth and change, urban planners need regular information on up to date ground change. Amman (the capital of Jordan) is growing at unprecedented rates, creating extensive urban landscapes. Planners interact with these changes without having a global view of their impact. The use of aerial photographs and satellite images data combined with topographic information and field survey could provide effective information to develop urban change and growth inventory which could be explored towards producing a very important signature for the built-up area changes.

Keywords: highway design, satellite technologies, remote sensing, GIS, image segmentation, classification

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Authors: Carl J. Caspersen, Kenneth E. Powell, Gregory M. Christenson, Kirupa V. Patel

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‘Physical activity’, ‘exercise’, and ‘physical fitness’ are terms that describe different concepts. However, they are often confused with one another, and the terms are sometimes used interchangeably. This paper proposes definitions to distinguish them. Physical activity is defined as any bodily movement produced by skeletal muscles that result in energy expenditure. The energy expenditure can be measured in kilocalories. Physical activity in daily life can be categorized into occupational, sports, Conditioning, household, or other activities. Exercise is a subset of physical activity that is planned, structured, and repetitive and has as a final or an intermediate objective the improvement or maintenance of physical fitness. Physical fitness is a set of attributes that are either health- or skill-related. The degree to which people have these attributes can be measured with specific tests. These definitions are offered as an interpretational framework for comparing studies that relate physical activity, exercise, and physical fitness to health. Physical activity is defined as any bodily movement produced by skeletal muscles that require energy expenditure. Physical inactivity has been identified as the fourth leading risk factor for global mortality causing an estimated 3.2 million deaths globally. Regular moderate intensity physical activity – such as walking, cycling, or participating in sports – has significant benefits for health. For instance, it can reduce the risk of cardiovascular diseases, diabetes, colon and breast cancer, and depression. Moreover, adequate levels of physical activity will decrease the risk of a hip or vertebral fracture and help control weight. Any bodily movement produced by the contraction of skeletal muscle that increases energy expenditure above a basal level. In these guidelines, physical activity generally refers to the subset of physical activity that enhances health.

Keywords: physical activity, exercise, physical fitness, sports

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Authors: Piyush Samant, Ravinder Agarwal

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Complementary and Alternative Medicine (CAM) techniques are quite popular and effective for chronic diseases. Iridology is more than 150 years old CAM technique which analyzes the patterns, tissue weakness, color, shape, structure, etc. for disease diagnosis. The objective of this paper is to validate the use of iridology for the diagnosis of the diabetes. The suggested model was applied in a systemic disease with ocular effects. 200 subject data of 100 each diabetic and non-diabetic were evaluated. Complete procedure was kept very simple and free from the involvement of any iridologist. From the normalized iris, the region of interest was cropped. All 63 features were extracted using statistical, texture analysis, and two-dimensional discrete wavelet transformation. A comparison of accuracies of six different classifiers has been presented. The result shows 89.66% accuracy by the random forest classifier.

Keywords: complementary and alternative medicine, classification, iridology, iris, feature extraction, disease prediction

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Authors: Tharini N. de Silva, Xiao Zhibo, Zhao Rui, Mao Kezhi

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Causal relation identification is a crucial task in information extraction and knowledge discovery. In this work, we present two approaches to causal relation identification. The first is a classification model trained on a set of knowledge-based features. The second is a deep learning based approach training a model using convolutional neural networks to classify causal relations. We experiment with several different convolutional neural networks (CNN) models based on previous work on relation extraction as well as our own research. Our models are able to identify both explicit and implicit causal relations as well as the direction of the causal relation. The results of our experiments show a higher accuracy than previously achieved for causal relation identification tasks.

Keywords: causal realtion extraction, relation extracton, convolutional neural network, text representation

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Authors: A. B. Chehreh, M. Grätzel, M. Klein, J. P. Bergmann, F. Walther

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Friction stir welding (FSW) is an advantageous method in the thermal joining processes, featuring the welding of various dissimilar and similar material combinations, joining temperatures below the melting point which prevents irregularities such as pores and hot cracks as well as high strengths mechanical joints near the base material. The FSW process consists of a rotating tool which is made of a shoulder and a probe. The welding process is based on a rotating tool which plunges in the workpiece under axial pressure. As a result, the material is plasticized by frictional heat which leads to a decrease in the flow stress. During the welding procedure, the material is continuously displaced by the tool, creating a firmly bonded weld seam behind the tool. However, the mechanical properties of the weld seam are affected by the design and geometry of the tool. These include in particular microstructural and surface properties which can favor crack initiation. Following investigation compares the dynamic properties of FSW weld seams with conventional and stationary shoulder geometry based on load increase test (LIT). Compared to classical Woehler tests, it is possible to determine the fatigue strength of the specimens after a short amount of time. The investigations were carried out on a robotized welding setup on 2 mm thick EN AW 5754 aluminum alloy sheets. It was shown that an increased tensile and fatigue strength can be achieved by using the stationary shoulder concept. Furthermore, it could be demonstrated that the LIT is a valid method to describe the fatigue behavior of FSW weld seams.

Keywords: aluminum alloy, fatigue performance, fracture, friction stir welding

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Authors: Zhao Haoyue

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Due to the combined effects of climate change, urban expansion, and population growth, various environmental issues, such as urban heat islands and pollution, arise. Therefore, reliable information on urban environmental climate is needed to address and mitigate the negative effects. The emergence of urban climate maps provides a practical basis for urban climate regulation and improvement. This article takes Shapingba District, Chongqing City, as an example to study the construction method of urban thermal environment climate maps based on GIS spatial analysis technology. The thermal load, ventilation potential analysis map, and thermal environment comprehensive analysis map were obtained. Based on the classification criteria obtained from the climate map, corresponding protection and planning mitigation measures have been proposed.

Keywords: urban climate, GIS, heat island analysis, urban thermal environment

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Authors: Loai Abdallah, Malik Yousef

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A domain space for the data should reflect the actual similarity between objects. Since objects belonging to the same cluster usually share some common traits even though their geometric distance might be relatively large. In general, the Euclidean distance of data points that represented by large number of features is not capturing the actual relation between those points. In this study, we propose a new method to construct a different space that is based on clustering to form a new distance metric. The new distance space is based on ensemble clustering (EC). The EC distance space is defined by tracking the membership of the points over multiple runs of clustering algorithm metric. Over this distance, we train the decision trees classifier (DT-EC). The results obtained by applying DT-EC on 10 datasets confirm our hypotheses that embedding the EC space as a distance metric would improve the performance.

Keywords: ensemble clustering, decision trees, classification, K nearest neighbors

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Authors: Jae-Yong Park, Gwancheol Seo, Young-Ho Kim

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Microvoids, sometimes called Kikendall voids, generally form in the interfaces between Sn-based solders and Cu and degrade the mechanical and electrical properties of the solder joints. The microvoid formation is known as the rapid interdiffusion between Sn and Cu and impurity content in the Cu. Cu electroplating from the acid solutions has been widely used by microelectronic packaging industry for both printed circuit board (PCB) and integrated circuit (IC) applications. The quality of electroplated Cu that can be optimized by the electroplating conditions is critical for the solder joint reliability. In this paper, the influence of electroplating conditions on the microvoid growth in the interfaces between Sn-3.0Ag-0.5Cu (SAC) solder and Cu layer was investigated during isothermal aging. The Cu layers were electroplated by controlling the additive of electroplating bath and current density to induce various microvoid densities. The electroplating bath consisted of sulfate, sulfuric acid, and additives and the current density of 5-15 mA/cm2 for each bath was used. After aging at 180 °C for up to 250 h, typical bi-layer of Cu6Sn5 and Cu3Sn intermetallic compounds (IMCs) was gradually growth at the SAC/Cu interface and microvoid density in the Cu3Sn showed disparities in the electroplating conditions. As the current density increased, the microvoid formation was accelerated in all electroplating baths. The higher current density induced, the higher impurity content in the electroplated Cu. When the polyethylene glycol (PEG) and Cl- ion were mixed in an electroplating bath, the microvoid formation was the highest compared to other electroplating baths. On the other hand, the overall IMC thickness was similar in all samples irrespective of the electroplating conditions. Impurity content in electroplated Cu influenced the microvoid growth, but the IMC growth was not affected by the impurity content. In conclusion, the electroplated conditions are properly optimized to avoid the excessive microvoid formation that results in brittle fracture of solder joint under high strain rate loading.

Keywords: electroplating, additive, microvoid, intermetallic compound

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Authors: Shuai Zhao, Shalendra D. Sharma, Jin Xu

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This research uses advanced machine learning models to learn a number of hypotheses regarding political executives. Specifically, it analyses the impact these powerful leaders have on economic growth by using leaders’ data from the Archigos database from 1835 to the end of 2015. The data is processed by the AutoGluon, which was developed by Amazon. Automated Machine Learning (AutoML) and AutoGluon can automatically extract features from the data and then use multiple classifiers to train the data. Use a linear regression model and classification model to establish the relationship between leaders and economic growth (GDP per capita growth), and to clarify the relationship between their characteristics and economic growth from a machine learning perspective. Our work may show as a model or signal for collaboration between the fields of statistics and artificial intelligence (AI) that can light up the way for political researchers and economists.

Keywords: comparative politics, political executives, leaders’ characteristics, artificial intelligence

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Authors: Ilirian Laçi, Alketa Spahiu

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Modality of treatment in hepatocellular carcinoma (HCC) patients depends on the stage of the disease. The Barcelona Clinic Liver Cancer Classification (BCLC) is the preferred staging system. There are many patients initially present with intermediate-stage disease. For these patients, transarterial chemoembolization (TACE) is the treatment of choice. The differences in individual factors that are not captured by the BCLC framework, such as the tumor growth pattern, degree of hypervascularity, and vascular supply, complicate further evaluation of these patients. Because of these differences, not all patients benefit equally from TACE. Several tools have been devised to aid the decision-making process, which have shown promising initial results but have failed external evaluation and have not been translated to the clinic aspects. Criteria for treatment decisions in daily clinical practice are needed in all stages of the disease.

Keywords: hepatocellular carcinoma, transarterial chemoembolization, TACE, liver

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Authors: Rahul Patel, S. P. Dave, M. V Shah

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Ahmedabad is a rapidly growing city in western India that is experiencing significant urbanization and industrialization. With projections indicating that it will become a metropolitan city in the near future, various construction activities are taking place, making soil testing a crucial requirement before construction can commence. To achieve this, construction companies and contractors need to periodically conduct soil testing. This study focuses on the process of creating a spatial database that is digitally formatted and integrated with geotechnical data and a Geographic Information System (GIS). Building a comprehensive geotechnical Geo-database involves three essential steps. Firstly, borehole data is collected from reputable sources. Secondly, the accuracy and redundancy of the data are verified. Finally, the geotechnical information is standardized and organized for integration into the database. Once the Geo-database is complete, it is integrated with GIS. This integration allows users to visualize, analyze, and interpret geotechnical information spatially. Using a Topographic to Raster interpolation process in GIS, estimated values are assigned to all locations based on sampled geotechnical data values. The study area was contoured for SPT N-Values, Soil Classification, Φ-Values, and Bearing Capacity (T/m2). Various interpolation techniques were cross-validated to ensure information accuracy. The GIS map generated by this study enables the calculation of SPT N-Values, Φ-Values, and bearing capacities for different footing widths and various depths. This approach highlights the potential of GIS in providing an efficient solution to complex phenomena that would otherwise be tedious to achieve through other means. Not only does GIS offer greater accuracy, but it also generates valuable information that can be used as input for correlation analysis. Furthermore, this system serves as a decision support tool for geotechnical engineers. The information generated by this study can be utilized by engineers to make informed decisions during construction activities. For instance, they can use the data to optimize foundation designs and improve site selection. In conclusion, the rapid growth experienced by Ahmedabad requires extensive construction activities, necessitating soil testing. This study focused on the process of creating a comprehensive geotechnical database integrated with GIS. The database was developed by collecting borehole data from reputable sources, verifying its accuracy and redundancy, and organizing the information for integration. The GIS map generated by this study is an efficient solution that offers greater accuracy and generates valuable information that can be used as input for correlation analysis. It also serves as a decision support tool for geotechnical engineers, allowing them to make informed decisions during construction activities.

Keywords: arcGIS, borehole data, geographic information system (GIS), geo-database, interpolation, SPT N-value, soil classification, φ-value, bearing capacity

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Authors: Alfonsina Buoniconto

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The aim of this study is to investigate the degree of cross-linguistic and intra-linguistic variation in the encoding of motion events (MEs) in English and Italian, these being typologically different languages both showing signs of disobedience to their respective types. As a matter of fact, the traditional typological classification of MEs encoding distributes languages into two macro-types, based on the preferred locus for the expression of Path, the main ME component (other components being Figure, Ground and Manner) characterized by conceptual and structural prominence. According to this model, Satellite-framed (SF) languages typically express Path information in verb-dependent items called satellites (e.g. preverbs and verb particles) with main verbs encoding Manner of motion; whereas Verb-framed languages (VF) tend to include Path information within the verbal locus, leaving Manner to adjuncts. Although this dichotomy is valid altogether, languages do not always behave according to their typical classification patterns. English, for example, is usually ascribed to the SF type due to the rich inventory of postverbal particles and phrasal verbs used to express spatial relations (i.e. the cat climbed down the tree); nevertheless, it is not uncommon to find constructions such as the fog descended slowly, which is typical of the VF type. Conversely, Italian is usually described as being VF (cf. Paolo uscì di corsa ‘Paolo went out running’), yet SF constructions like corse via in lacrime ‘She ran away in tears’ are also frequent. This paper will try to demonstrate that such a typological overlapping is due to the fact that the semantic units making up MEs are distributed within several loci of the sentence –not only verbs and satellites– thus determining a number of different constructions stemming from convergent factors. Indeed, the linguistic expression of motion events depends not only on the typological nature of languages in a traditional sense, but also on a series morphological, lexical, and syntactic resources, as well as on inferential, discursive, usage-related, and cultural factors that make semantic information more or less accessible, frequent, and easy to process. Hence, rather than describe English and Italian in dichotomic terms, this study focuses on the investigation of cross-linguistic and intra-linguistic variation in the use of all the strategies made available by each linguistic system to express motion. Evidence for these assumptions is provided by parallel corpora analysis. The sample texts are taken from two contemporary Italian novels and their respective English translations. The 400 motion occurrences selected (200 in English and 200 in Italian) were scanned according to the MODEG (an acronym for Motion Decoding Grid) methodology, which grants data comparability through the indexation and retrieval of combined morphosyntactic and semantic information at different levels of detail.

Keywords: construction typology, motion event encoding, parallel corpora, satellite-framed vs. verb-framed type

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Authors: Marina Yurievna Aleksandrova

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Research of the Holocaust retains value not only for history but also for sociology and psychology. One of the most important fields of study is how people were coping during and after this traumatic event. The aim of this paper is to identify the main contexts of the topic of love and to determine which contexts are more characteristic for different groups of victims of the Holocaust (gender, nationality, age). In this research, transcripts of interviews with Holocaust victims that were collected during 1946 for the "Voices of the Holocaust" project were used as data. Main contexts were analyzed with methods of network analysis and latent semantic analysis and classified by gender, age, and nationality with random forest. The results show that love is articulated and described significantly differently for male and female informants, nationality is shown results with lower values of quality metrics, as well as the age.

Keywords: Holocaust, latent semantic analysis, network analysis, text-mining, random forest

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Authors: Omair Ghori, Anton Stadler, Stefan Wilk, Wolfgang Effelsberg

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Fire-related incidents account for extensive loss of life and material damage. Quick and reliable detection of occurring fires has high real world implications. Whereas a major research focus lies on the detection of outdoor fires, indoor camera-based fire detection is still an open issue. Cameras in combination with computer vision helps to detect flames and smoke more quickly than conventional fire detectors. In this work, we present a computer vision-based smoke detection algorithm based on contrast changes and a multi-step classification. This work accelerates computer vision-based fire detection considerably in comparison with classical indoor-fire detection.

Keywords: contrast analysis, early fire detection, video smoke detection, video surveillance

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Authors: Wei-Chia Huang, Jane Wang

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Damage to nerves is considered one of the most irreversible injuries. The regeneration of nerves has always been an important topic in regenerative medicine. In general, damage to human tissue will naturally repair overtime. However, when the nerves are damaged, healed flesh wound cannot guarantee full restoration to its original function, as truncated nerves are often irreversible. Therefore, the development of treatment methods to successfully guide and accelerate the regeneration of nerves has been highly sought after. In order to induce nerve tissue growth, nerve conduits are commonly used to help reconnect broken nerve bundles to provide protection to the location of the fracture while guiding the growth of the nerve bundles. To prevent the protected tissue from becoming necrotic and to ensure the growth rate, the conduits used are often modified with microstructures or blended with neuron growth factors that may facilitate nerve regeneration. Electrical stimulation is another attempted treatment for medical rehabilitation. With appropriate range of voltages and stimulation frequencies, it has been demonstrated to promote cell proliferation and migration. Biodegradability are critical for medical devices like nerve conduits, while conductive polymers pose great potential toward the differentiation and growth of nerve cells. In this work, biodegradability and conductivity were combined into a novel biodegradable, photocurable, conductive polymer composite materials by embedding conductive nanoparticles in poly(glycerol sebacate) acrylate (PGSA) and 3D-printed into nerve conduits. Rat pheochromocytoma cells and rat neuronal Schwann cells were chosen for the in vitro tests of the conduits and had demonstrate selective growth upon culture in the conductive conduits with built-in microchannels and electrical stimulation.

Keywords: biodegradable polymer, 3d printing, neural regeneration, electrical stimulation

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Authors: Jayant Kumar, Aarcha Jayachandran Sasikala, Barry Adrian Shepherd

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Public Distribution System is a flagship welfare programme of the Government of India with both historical and political significance. Targeted at lower sections of society,it is one of the largest supply chain networks in the world. There has been several studies by academics and planning commission about the effectiveness of the system. Our study focuses on applying predictive analytics to aid the central body to keep track of the problem of breach of service level agreement between the two echelons of food supply chain. Each shop breach is leading to a potential additional inventory carrying cost. Thus, through this study, we aim to show that aided with such analytics, the network can be made more cost effective. The methods we illustrate in this study are applicable to other commercial supply chains as well.

Keywords: PDS, analytics, cost effectiveness, Karnataka, inventory cost, service level JEL classification: C53

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Authors: Paata Sitchinava

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It has been studied phenomena subjected on the water physical (hydrophysical, mineralogy containing, specific hydrophysical) class of heavy clay soils of the Colchis lowland, according to various categories and forms of the porous water, which will be the base of the distributed used methods of the engineering practice and reclamation effectiveness evaluation. According to of clay grounds data, it has been chosen three research bases section in the central part of lowland, where has implemented investigation works by using a special program. It has been established, that three of cuts are somewhat identical, and by morphological grounds separated layers are the difference by Gallic quality. It has been implemented suitable laboratory experimental research at the samples taken from the cuts, at the base of these created classification mark of physical-technical characteristic, which is the base of suitable calculation of hydrophysical researches.

Keywords: Colchis lowland, drainage, water, soil-ground

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Authors: M. I. Equbal, R. K. Ohdar, B. Singh, P. Talukdar

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Micro-alloyed steel components are used in automotive industry for the necessity to make the manufacturing process cycles shorter when compared to conventional steel by eliminating heat treatment cycles, so an important saving of costs and energy can be reached by reducing the number of operations. Micro-alloying elements like vanadium, niobium or titanium have been added to medium carbon steels to achieve grain refinement with or without precipitation strengthening along with uniform microstructure throughout the matrix. Present study reports the applicability of medium carbon vanadium micro-alloyed steel in hot forging. Forgeability has been determined with respect to different cooling rates, after forging in a hydraulic press at 50% diameter reduction in temperature range of 900-11000C. Final microstructures, hardness, tensile strength, and impact strength have been evaluated. The friction coefficients of different lubricating conditions, viz., graphite in hydraulic oil, graphite in furnace oil, DF 150 (Graphite, Water-Based) die lubricant and dry or without any lubrication were obtained from the ring compression test for the above micro-alloyed steel. Results of ring compression tests indicate that graphite in hydraulic oil lubricant is preferred for free forging and dry lubricant is preferred for die forging operation. Exceptionally good forgeability and high resistance to fracture, especially for faster cooling rate has been observed for fine equiaxed ferrite-pearlite grains, some amount of bainite and fine precipitates of vanadium carbides and carbonitrides. The results indicated that the cooling rate has a remarkable effect on the microstructure and mechanical properties at room temperature.

Keywords: cooling rate, hot forging, micro-alloyed, ring compression

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Authors: Nebi Gedik

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One of the significant and continual public health problems in the world is breast cancer. Early detection is very important to fight the disease, and mammography has been one of the most common and reliable methods to detect the disease in the early stages. However, it is a difficult task, and computer-aided diagnosis (CAD) systems are needed to assist radiologists in providing both accurate and uniform evaluation for mass in mammograms. In this study, a multiresolution statistical method to classify mammograms as normal and abnormal in digitized mammograms is used to construct a CAD system. The mammogram images are represented by wave atom transform, and this representation is made by certain groups of coefficients, independently. The CAD system is designed by calculating some statistical features using each group of coefficients. The classification is performed by using support vector machine (SVM).

Keywords: wave atom transform, statistical features, multi-resolution representation, mammogram

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Authors: Adrien Marque, Daniel Delahaye, Pierre Maréchal, Isabelle Berry

Abstract:

Wind direction and uncertainty are crucial in aircraft or unmanned aerial vehicle trajectories. By computing wind covariance matrices on each spatial grid point, these spatial grids can be defined as images with symmetric positive definite matrix elements. A data pre-processing step, a specific convolution, a specific max-pooling, and a specific flatten layers are implemented to process such images. Then, the neural network is applied to spatial grids, whose elements are wind covariance matrices, to solve classification problems related to the feasibility of unmanned aerial vehicles based on wind direction and wind uncertainty.

Keywords: wind direction, uncertainty level, unmanned aerial vehicle, convolution neural network, SPD matrices

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Authors: M. Ben Slimene, M.-S. Ouali

Abstract:

Asset management of urban infrastructures faces a multitude of challenges that need to be overcome to obtain a reliable measurement of performances. Predicting the performance of slowly aging systems is one of those challenges, which helps the asset manager to investigate specific failure modes and to undertake the appropriate maintenance and rehabilitation interventions to avoid catastrophic failures as well as to optimize the maintenance costs. This article presents a methodology for modeling the deterioration of slowly degrading assets based on an operating history. It consists of extracting degradation profiles by grouping together assets that exhibit similar degradation sequences using an unsupervised classification technique derived from artificial intelligence. The obtained clusters are used to build the performance prediction models. This methodology is applied to a sample of a stormwater drainage culvert dataset.

Keywords: artificial Intelligence, clustering, culvert, regression model, slow degradation

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Authors: Hyun-Koo Kim, Yonghun Kim, Yong-Hoon Kim, Ju Hee Lee, Myungho Song

Abstract:

In this paper, we have described an efficient stereo matching method and pedestrian detection method using stereo types LWIR camera. We compared with three types stereo camera algorithm as block matching, ELAS, and SGM. For pedestrian detection using stereo LWIR camera, we used that SGM stereo matching method, free space detection method using u/v-disparity, and HOG feature based pedestrian detection. According to testing result, SGM method has better performance than block matching and ELAS algorithm. Combination of SGM, free space detection, and pedestrian detection using HOG features and SVM classification can detect pedestrian of 30m distance and has a distance error about 30 cm.

Keywords: advanced driver assistance system, pedestrian detection, stereo matching method, stereo long-wave IR camera

Procedia PDF Downloads 415