Search results for: MFCC feature warping

Authors: I. A Tijani, Y. F Wu, C.W. Lim

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Concrete Damaged Plasticity Model (CDPM) is capable of modeling the stress-strain behavior of confined concrete. Nevertheless, the accuracy of the model largely depends on its parameters. To date, most research works mainly focus on the identification and modification of the parameters for fiber reinforced polymer (FRP) confined concrete prior to damage. And, it has been established that the FRP-strengthened concrete behaves differently to FRP-repaired concrete. This paper presents a modified plastic damage model within the context of the CDPM in ABAQUS for modelling of a uniformly FRP-confined repaired concrete under monotonic loading. The proposed model includes infliction damage, elastic stiffness, yield criterion and strain hardening rule. The distinct feature of damaged concrete is elastic stiffness reduction; this is included in the model. Meanwhile, the test results were obtained from a physical testing of repaired concrete. The dilation model is expressed as a function of the lateral stiffness of the FRP-jacket. The finite element predictions are shown to be in close agreement with the obtained test results of the repaired concrete. It was observed from the study that with necessary modifications, finite element method is capable of modeling FRP-repaired concrete structures.

Keywords: Concrete, FRP, Damage, Repairing, Plasticity, and Finite element method

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Authors: Geetanjali Singh, R. J. Choudhary, Anjana Dogra

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Here we report the effects of anti-site disorder, present in the sample, on the magnetic properties of Sm₂NiMnO₆ (SNMO) thin films. To our best knowledge, there are no studies available on the thin films of SNMO. Thin films were grown using pulsed laser deposition technique on SrTiO₃ (STO) substrate under oxygen pressure of 800 mTorr. X-ray diffraction (XRD) profiles show that the film grown is epitaxial. Field cooled (FC) and zero field cooled (ZFC) magnetization curve increase as we decrease the temperature till ~135K. A broad dip was observed in both the curves below this temperature which is more dominating in ZFC curve. An additional sharp cusplike shape was observed at low temperature (~20 K) which is due to the re-entrant spin-glass like properties present in the sample. Super-exchange interaction between Ni²⁺-O-Mn⁴⁺ is attributed to the FM ordering in these samples. The spin-glass feature is due to anti-site disorder within the homogeneous sample which was stated to be due to the mixed valence states Ni³⁺ and Mn³⁺ present in the sample. Anti-site disorder was found to play very crucial role in different magnetic phases of the sample.

Keywords: double perovskite, pulsed laser deposition, spin-glass, magnetization

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Authors: Daniel M. Wolff, Patricia Gray, Rafael de la Parra Venegas

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An unsupervised machine listening system is constructed and applied to a dataset of 17,195 30-second marine hydrophone recordings. The system is then heuristically supplemented with anecdotal listening, contextual recording information, and supervised learning techniques to reduce the number of false positives. Features for classification are assembled by extracting the following data from each of the audio files: the spectral centroid, root-mean-squared values for each frequency band of a 10-octave filter bank, and mel-frequency cepstral coefficients in 5-second frames. In this way both time- and frequency-domain information are contained in the features to be passed to a clustering algorithm. Classification is performed using the k-means algorithm and then a k-nearest neighbors search. Different values of k are experimented with, in addition to different combinations of the available feature sets. Hypothesized class labels are 'primarily anthrophony' and 'primarily biophony', where the best class result conforming to the former label has 104 members after heuristic pruning. This demonstrates how a large audio dataset has been made more tractable with machine learning techniques, forming the foundation of a framework designed to acoustically monitor and gauge biological and anthropogenic activity in a marine environment.

Keywords: anthrophony, hydrophone, k-means, machine learning

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Authors: Faraj Ahmed E. Alhegagi, Anagia M. Khamkam Mohamed, Bassam F. Alhajaji

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Duplex stainless steels (DSS) are widely employed in industry for apparatus working with sea water in petroleum, refineries and in chemical plants. Fracture of DSS takes place by cleavage of the ferrite phase and the austenite phase ductile tear off. Pop-in is an important feature takes place during fracture of DSS. The procedure of Pop-ins assessment plays an important role in fracture toughness studies. In present work, Zeron100 DSS specimens were heat treated at different temperatures, cooled and pulled to failure to assess the pop-ins criterion in crack initiation prediction. The outcome results were compared to the British Standard (BS 7448) and the ASTEM standard (E1290) for Crack-Tip Opening Displacement (CTOD) fracture toughness measurement. Pop-in took place during specimens loading specially for those specimens heat treated at higher temperatures. The standard BS7448 was followed to check specimen validity for fractured toughness assessment by direct determination of KIC. In most cases, specimens were invalid for KIC measurement. The two procedures were equivalent only when single pop-ins were assessed. A considerable contrast in fracture toughness value between was observed where multiple pop-ins were assessed.

Keywords: fracture toughness, stainless steels, pop ins, crack assessment

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Authors: Konstantinos Perifanos, Eirini Florou, Dionysis Goutsos

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This paper presents and benchmarks a number of end-to-end Deep Learning based models for metaphor detection in Greek. We combine Convolutional Neural Networks and Recurrent Neural Networks with representation learning to bear on the metaphor detection problem for the Greek language. The models presented achieve exceptional accuracy scores, significantly improving the previous state-of-the-art results, which had already achieved accuracy 0.82. Furthermore, no special preprocessing, feature engineering or linguistic knowledge is used in this work. The methods presented achieve accuracy of 0.92 and F-score 0.92 with Convolutional Neural Networks (CNNs) and bidirectional Long Short Term Memory networks (LSTMs). Comparable results of 0.91 accuracy and 0.91 F-score are also achieved with bidirectional Gated Recurrent Units (GRUs) and Convolutional Recurrent Neural Nets (CRNNs). The models are trained and evaluated only on the basis of training tuples, the related sentences and their labels. The outcome is a state-of-the-art collection of metaphor detection models, trained on limited labelled resources, which can be extended to other languages and similar tasks.

Keywords: metaphor detection, deep learning, representation learning, embeddings

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Authors: Denila Jinny Arulraj, Thillai Rajan Annamalai

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This paper aims to study the relationship between funding sources and firm performance of Indian private limited SMEs using cross-sectional data obtained from a nation-wide census. A unique feature of the study is that it analyses firms that use only one form of external funding. Employing Propensity Score Matching, we find that obtaining any form of external finance has a negative influence on equivalents of profit margin and return on assets and a negative influence on asset turnover of small firms. But, the impact of institutional sources of funding on small enterprises is found to be lesser than that of non-institutional sources of funding. External/institutional sources of funding have a less negative impact on the profit margin for medium enterprises and have no significant influence on other measures of performance. The contribution of this research is the discovery of institutional sources wielding a lesser influence on performance measures considered. It is also found that institutional sources can benefit small enterprises more than medium enterprises.

Keywords: external finance, institutional finance, non-institutional finance, performance, India, SME

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Authors: Roshani Gusain, Deep Sinha, Karan Nayal, Anmol Kumar Mishra, Manav Singh

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In this research paper, we introduce "Mind Care Assistant - Companion App", which is a Flutter and Firebase-based mental health monitor. The app wants to improve and monitor the mental health of its users, it uses noninvasive ways to check for a change in their emotional state. By responding to questions, the app will provide individualized suggestions ᅳ tasks and mindfulness exercises ᅳ for users who are depressed or anxious. The app features a chat-bot that incorporates cognitive behavioural therapy (CBT) principles and combines natural language processing with machine learning to develop personalised responses. The feature of the app that makes it easy for us to choose between iOS and Android is cross-platform, which allows users from both mobile systems to experience almost no changes in their interfaces. With Firebase integration synchronized and real-time data storage, security is easily possible. The paper covers the architecture of the app, how it was developed and some important features. The primary research result presents the promise of a "Mind Care Assistant" in mental health care using new wait-for-health technology, proposing a full stack application to be able to manage depression/anxiety and overall Mental well-being very effectively.

Keywords: mental health, mobile application, flutter, firebase, Depression, Anxiety

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Authors: Zineb Ouahdi

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Spirocyclic compound derivatives, with their unique heterocyclic motifs, serve as a continual source of inspiration in the pursuit of developing potential therapeutic agents. These compounds are diverse in their chemical structures; some have fully saturated skeletons, while others are partially unsaturated. Nevertheless, these compounds share a characteristic feature with natural products - the presence of at least one heteroatom in one of their rings. The inclusion of a C = O dipolarophile in pyridazinones imparts an exciting aspect for 1,3-dipolar cycloaddition reactions, the focal point of our study. Our research has involved a detailed theoretical investigation of the reaction between ethyl (Z)-2-bromo-2-(2-(p-tolyl)hydrazono)acetate and 6-methyl-4,5-dihydropyridazine-3(2H)-one. This has been accomplished using the DFT/B3LYP/6-31g(d,p) method, intending to illuminate the chemical pathway of this reaction. The chemical reactivity theories we used for this purpose included FMO, TS, and local and global indices derived from conceptual DFT. The theoretical framework outlined in this study allowed us to propose a reaction mechanism for cycloaddition reactions. It also enabled the identification of the potential activities of the analyzed compounds (P1, P2, P3, P4, P5, and P6) against the major protease of the coronavirus disease (COVID-19). This was achieved using various computational tools, including AutoDock Tools, Autodock Vina, Autodock 4, and PYRX.

Keywords: MEDT, pyridazin, cycloaddition, FMO, DFT, docking

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Authors: Adrienne Kline, Jaydip Desai

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Electroencephalogram (EEG) is a noninvasive technique that registers signals originating from the firing of neurons in the brain. The Emotiv EEG Neuroheadset is a consumer product comprised of 14 EEG channels and was used to record the reactions of the neurons within the brain to two forms of stimuli in 10 participants. These stimuli consisted of auditory and visual formats that provided directions of ‘right’ or ‘left.’ Participants were instructed to raise their right or left arm in accordance with the instruction given. A scenario in OpenViBE was generated to both stimulate the participants while recording their data. In OpenViBE, the Graz Motor BCI Stimulator algorithm was configured to govern the duration and number of visual stimuli. Utilizing EEGLAB under the cross platform MATLAB®, the electrodes most stimulated during the study were defined. Data outputs from EEGLAB were analyzed using IBM SPSS Statistics® Version 20. This aided in determining the electrodes to use in the development of a brain-machine interface (BMI) using real-time EEG signals from the Emotiv EEG Neuroheadset. Signal processing and feature extraction were accomplished via the Simulink® signal processing toolbox. An Arduino™ Duemilanove microcontroller was used to link the Emotiv EEG Neuroheadset and the right and left Mecha TE™ Hands.

Keywords: brain-machine interface, EEGLAB, emotiv EEG neuroheadset, OpenViBE, simulink

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Authors: Chayanika Hazarika Bordoloi

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This paper is an attempt to work on a highly inflectional language called Assamese. This is also one of the national languages of India and very little has been achieved in terms of computational research. Building a language processing tool for a natural language is not very smooth as the standard and language representation change at various levels. This paper presents inflectional suffixes of Assamese verbs and how the statistical tools, along with linguistic features, can improve the tagging accuracy. Conditional random fields (CRF tool) was used to automatically tag and train the text data; however, accuracy was improved after linguistic featured were fed into the training data. Assamese is a highly inflectional language; hence, it is challenging to standardizing its morphology. Inflectional suffixes are used as a feature of the text data. In order to analyze the inflections of Assamese word forms, a list of suffixes is prepared. This list comprises suffixes, comprising of all possible suffixes that various categories can take is prepared. Assamese words can be classified into inflected classes (noun, pronoun, adjective and verb) and un-inflected classes (adverb and particle). The corpus used for this morphological analysis has huge tokens. The corpus is a mixed corpus and it has given satisfactory accuracy. The accuracy rate of the tagger has gradually improved with the modified training data.

Keywords: CRF, morphology, tagging, tagset

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Authors: Jim Shi, Jasmine Chang, Nesreen El-Rayes

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The world has been experiencing a steadily increasing trend in both the production and consumption of plastic. The global consumer revolution should not have been possible without plastic, thanks to its salient feature of inexpensiveness and durability. But, as a two-edged sword, its durable quality has returned to haunt and even jeopardized us. That exacerbating the plastic crisis has attracted various global initiatives and actions. Simultaneously, firms are eager to adopt new technology as they witness and perceive more potential and merit of Industry 4.0 technologies. For example, Blockchain technology (BCT) is drawing the attention of numerous stakeholders because of its wide range of outstanding features that promise to enhance supply chain operations. However, from a research perspective, most of the literature addresses the plastic crisis from either environmental or social perspectives. In contrast, analysis from the data science perspective and technology is relatively scarce. To this end, this study aims to fill this gap and cover the plastic crisis from a holistic view of environmental, social, technological, and business perspectives. In particular, we propose a mathematical model to examine the inclusion of BCT to enhance and improve the efficiency on the upstream and the downstream sides of the plastic value, where the whole value chain is coordinated systematically, and its interoperability can be optimized. Consequently, the Environmental, Social, and Governance (ESG) goal and Circular Economics (CE) sustainability can be maximized.

Keywords: blockchain technology, plastic, circular economy, sustainability

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Authors: Nazmul Shahadat, Anthony S. Maida

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In recent years, hypercomplex inspired neural networks improved deep CNN architectures due to their ability to share weights across input channels and thus improve cohesiveness of representations within the layers. The work described herein studies the effect of replacing existing layers in an Axial Attention ResNet with their quaternion variants that use cross-channel weight sharing to assess the effect on image classification. We expect the quaternion enhancements to produce improved feature maps with more interlinked representations. We experiment with the stem of the network, the bottleneck layer, and the fully connected backend by replacing them with quaternion versions. These modifications lead to novel architectures which yield improved accuracy performance on the ImageNet300k classification dataset. Our baseline networks for comparison were the original real-valued ResNet, the original quaternion-valued ResNet, and the Axial Attention ResNet. Since improvement was observed regardless of which part of the network was modified, there is a promise that this technique may be generally useful in improving classification accuracy for a large class of networks.

Keywords: axial attention, representational networks, weight sharing, cross-channel correlations, quaternion-enhanced axial attention, deep networks

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Authors: Y. Bai, M. Bodlak, V. Frolov, S. Huber, V. Jary, I. Konorov, D. Levit, J. Novy, D. Steffen, O. Subrt, M. Virius

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Modern experiments in high energy physics impose great demands on the reliability, the efficiency, and the data rate of Data Acquisition Systems (DAQ). This contribution focuses on the development and deployment of the new communication library DIALOG for the intelligent, FPGA-based Data Acquisition System (iFDAQ) of the COMPASS experiment at CERN. The iFDAQ utilizing a hardware event builder is designed to be able to readout data at the maximum rate of the experiment. The DIALOG library is a communication system both for distributed and mixed environments, it provides a network transparent inter-process communication layer. Using the high-performance and modern C++ framework Qt and its Qt Network API, the DIALOG library presents an alternative to the previously used DIM library. The DIALOG library was fully incorporated to all processes in the iFDAQ during the run 2016. From the software point of view, it might be considered as a significant improvement of iFDAQ in comparison with the previous run. To extend the possibilities of debugging, the online monitoring of communication among processes via DIALOG GUI is a desirable feature. In the paper, we present the DIALOG library from several insights and discuss it in a detailed way. Moreover, the efficiency measurement and comparison with the DIM library with respect to the iFDAQ requirements is provided.

Keywords: data acquisition system, DIALOG library, DIM library, FPGA, Qt framework, TCP/IP

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Authors: Jörg Lenhardt, Wolfram Schiffmann, Jörg Keller

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While the feature sizes of recent Complementary Metal Oxid Semiconductor (CMOS) devices decrease the influence of static power prevails their energy consumption. Thus, power savings that benefit from Dynamic Frequency and Voltage Scaling (DVFS) are diminishing and temporal shutdown of cores or other microchip components become more worthwhile. A consequence of powering off unused parts of a chip is that the relative difference between idle and fully loaded power consumption is increased. That means, future chips and whole server systems gain more power saving potential through power-aware load balancing, whereas in former times this power saving approach had only limited effect, and thus, was not widely adopted. While powering off complete servers was used to save energy, it will be superfluous in many cases when cores can be powered down. An important advantage that comes with that is a largely reduced time to respond to increased computational demand. We include the above developments in a server power model and quantify the advantage. Our conclusion is that strategies from datacenters when to power off server systems might be used in the future on core level, while load balancing mechanisms previously used at core level might be used in the future at server level.

Keywords: power efficiency, static power consumption, dynamic power consumption, CMOS

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Authors: Serap Keskin Tunç, Cennet Neslihan Eroğlu, Sevinç Şahin, Selda Seçkin

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It has been suggested that various viruses may play a role in the pathogenesis of cancerous oral lesions in the literature. The aim of this study was to investigate the presence of both possible precancerous viral markers (HPV, HHV8, HSV1, HSV2, and EBV), and p53 and Ki-67 in the dental follicles of asymptomatic impacted teeth. A hundred healthy volunteers, older than 18 years old, included in the study. Dental follicles of extracted impacted teeth were excised and fixated in 10% formaldehyde. Histopathological and immunohistochemical examinations using HPV (containing HPV 8 and HPV 11), p16 (containing HPV 16), HHV8, HSV1, HSV2, EBV, p53 and Ki-67 antibodies were carried out. Also, the immunohistochemical results were correlated with the clinicopathological feature by Chi-square test statistically No dysplasia or neoplasm was observed. 62% of the cases were positive for p16, 32% were positive for EBV, 26% were positive for HSV1, immunohistochemically. All cases were immunonegative for HPV, HSV2, and HHV8. There was statistically significant correlation between overexpression of p53 with both EBV and p16 positivity (p<0.05). Direct correlation between higher expression of Ki-67 between EBV immunopositivity was detected (p<0.05). Thus, these viruses may be suggested to show trophism to the dental follicles acting as a reservoir. In conclusion, all dental follicles of extracted impacted teeth should be examined histopathologically in order to detect and prevent possible viral oncogenesis.

Keywords: dental follicles, Ki67, p53, precancerous markers viral markers

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Authors: Muhammad Zubair Khan, Yugyung Lee

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Deep learning has recently achieved enormous response in semantic image segmentation. The previously developed U-Net inspired architectures operate with continuous stride and pooling operations, leading to spatial data loss. Also, the methods lack establishing long-term pixels connection to preserve context knowledge and reduce spatial loss in prediction. This article developed encoder-decoder architecture with bi-directional LSTM embedded in long skip-connections and densely connected convolution blocks. The network non-linearly combines the feature maps across encoder-decoder paths for finding dependency and correlation between image pixels. Additionally, the densely connected convolutional blocks are kept in the final encoding layer to reuse features and prevent redundant data sharing. The method applied batch-normalization for reducing internal covariate shift in data distributions. The empirical evidence shows a promising response to our method compared with other semantic segmentation techniques.

Keywords: deep learning, semantic segmentation, image analysis, pixels connection, convolution neural network

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Authors: Zahra Salehi Shahrbabaki, Zahra Chavoshzadeh, Fahimeh Abdollahimajd, Samin Sharafian, Tolue Mahdavi, Mahnaz Jamee

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Background: Inborn errors of immunity (IEIs) are monogenic diseases of the immune the system with broad clinical manifestations. Despite the increasing genetic advancements, the diagnosis of IEIs still leans on clinical diagnosis. Dermatologic manifestations are observed in a large number of IEI patients and can lead to proper approach, prompt intervention and improved prognosis. Methods: This cross-sectional study was carried out between 2018 and 2020 on IEIs at a Children's tertiary care center in Tehran, Iran. Demographic details (including age, sex, and parental consanguinity), age at onset of symptoms and family history of IEI with were recorded. Results :212 patients were included. Cutaneous findings were reported in (95 ,44.8%) patients. and 61 of 95 (64.2%) reported skin lesions as the first clinical presentation. Skin infection (69, 72.6%) was the most frequent cutaneous manifestation, followed by an eczematous rash (24, 25 %). Conclusions: Skin manifestations are common feature in IEI patients and can be readily recognizable by healthcare providers. This study tried to provide information on prognostic consequences.

Keywords: primary immuno deficiency, inborn errror of metabolism, skin manifestation, skin infection

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Authors: Rana Tajammal Rashid

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Federalism is a political system in which government power and responsibility are divided between a federal legislature and units of the state or provincial legislatures. This system provides the structure for the states having large territory and through that can manage the state affairs and administration easily. Many of the largest countries in the world are federations, like; The United States, Canada, India, Pakistan South Africa, Argentina, and Australia. Every large democratic nation has a federal system of government. This study will explore the feature and good governance of two developed countries Canada and Australia. This study will be helpful to the developing countries like Pakistan, India which have a federal form of structure to run the affairs of the state. In the federal system of Pakistan there are lot of issues and conflicts with the provinces with a comparative study of these two developed countries, i.e., Australia and Canada, our policy and decision maker political actors will understand in which way a state will successfully manage the issues related to federalism. This study will also provide the help to the students of comparative politics that how to analysis the different political system of the developed countries of the world.

Keywords: federalism, features of federalism, types of federalism, history of federalism, Australian federalism, Canadian federalism, federalism developments, executives, federal and provincial autonomy legislative, judicial

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Authors: Shimaa Holail, Tamer Saleh, Xiongwu Xiao

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Building change detection (BCD) from satellite imagery is an essential topic in urbanization monitoring, agricultural land management, and updating geospatial databases. Recently, methods for detecting changes based on deep learning have made significant progress and impressive results. However, it has the problem of being insensitive to changes in buildings with complex spectral differences, and the features being extracted are not discriminatory enough, resulting in incomplete buildings and irregular boundaries. To overcome these problems, we propose a dual Siamese network based on the Unet model with the addition of a deep supervision strategy (DS) in this paper. This network consists of a backbone (encoder) based on ImageNet pre-training, a fusion block, and feature pyramid networks (FPN) to enhance the step-by-step information of the changing regions and obtain a more accurate BCD map. To train the proposed method, we created a new dataset (EGY-BCD) of high-resolution and multi-temporal aerial images captured over New Cairo in Egypt to detect building changes for this purpose. The experimental results showed that the proposed method is effective and performs well with the EGY-BCD dataset regarding the overall accuracy, F1-score, and mIoU, which were 91.6 %, 80.1 %, and 73.5 %, respectively.

Keywords: building change detection, deep supervision, semantic segmentation, EGY-BCD dataset

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Authors: Ni Komang Desy Arya Pinatih

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Transnational crime is a crime with 'unique' feature because the activities benefit the lack of state monitoring on the borders so dealing with it cannot be based on conventional engagement but also need joint operation with other countries. On the other hand with the flow of globalization and the growth of information technology and transportation, states become more vulnerable to transnational crime threats especially human trafficking. This paper would examine transnational crime activities, especially human trafficking in Indonesia. With the case study on the mapping of human trafficking crime in East Java province, Indonesia, this paper would try to analyze how the difference in human trafficking crime trends at the national and sub-national levels. The findings of this research were first, there is difference in human trafficking crime trends whereas at the national level the trend is rising, while at sub-national (province) level the trend is declining. Second, regarding the decline of human trafficking number, it’s interesting to see how the method to decrease human trafficking crime in East Jawa Province in order to reduce transnational crime accounts in the region. These things are hopefully becoming a model for transnational crimes engagement in other regions to reduce human trafficking numbers as much as possible.

Keywords: transnational crime, human trafficking, southeast Asia, anticipation model on transnational crimes

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Authors: Tanushree Jaitly, Shailendra Gupta, Leila Taher, Gerold Schuler, Julio Vera

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Genomics-based personalized medicine is a promising approach to fight aggressive tumors based on patient's specific tumor mutation and expression profiles. A remarkable case is, dendritic cell-based immunotherapy, in which tumor epitopes targeting patient's specific mutations are used to design a vaccine that helps in stimulating cytotoxic T cell mediated anticancer immunity. Here we present a computational pipeline for epitope-based personalized cancer vaccines using patient-specific haplotype and cancer mutation profiles. In the workflow proposed, we analyze Whole Exome Sequencing and RNA Sequencing patient data to detect patient-specific mutations and their expression level. Epitopes including the tumor mutations are computationally predicted using patient's haplotype and filtered based on their expression level, binding affinity, and immunogenicity. We calculate binding energy for each filtered major histocompatibility complex (MHC)-peptide complex using docking studies, and use this feature to select good epitope candidates further.

Keywords: cancer immunotherapy, epitope prediction, NGS data, personalized medicine

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Authors: T. Heikkinen, J. Oksman, T. Bragge, A. Nurmi, O. Kontkanen, T. Ahtoniemi

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Motor impairment is an inherent phenotypic feature of several chronic neurodegenerative diseases, and pharmacological therapies aimed to counterbalance the motor disability have a great market potential. Animal models of chronic neurodegenerative diseases display a number deteriorating motor phenotype during the disease progression. There is a wide array of behavioral tools to evaluate motor functions in rodents. However, currently existing methods to study motor functions in rodents are often limited to evaluate gross motor functions only at advanced stages of the disease phenotype. The most commonly applied traditional motor assays used in CNS rodent models, lack the sensitivity to capture fine motor impairments or improvements. Fine motor skill characterization in rodents provides a more sensitive tool to capture more subtle motor dysfunctions and therapeutic effects. Importantly, similar approach, kinematic movement analysis, is also used in clinic, and applied both in diagnosis and determination of therapeutic response to pharmacological interventions. The aim of this study was to apply kinematic gait analysis, a novel and automated high precision movement analysis system, to characterize phenotypic deficits in three different chronic neurodegenerative animal models, a transgenic mouse model (SOD1 G93A) for amyotrophic lateral sclerosis (ALS), and R6/2 and Q175KI mouse models for Huntington’s disease (HD). The readouts from walking behavior included gait properties with kinematic data, and body movement trajectories including analysis of various points of interest such as movement and position of landmarks in the torso, tail and joints. Mice (transgenic and wild-type) from each model were analyzed for the fine motor kinematic properties at young ages, prior to the age when gross motor deficits are clearly pronounced. Fine motor kinematic Evaluation was continued in the same animals until clear motor dysfunction with conventional motor assays was evident. Time course analysis revealed clear fine motor skill impairments in each transgenic model earlier than what is seen with conventional gross motor tests. Motor changes were quantitatively analyzed for up to ~80 parameters, and the largest data sets of HD models were further processed with principal component analysis (PCA) to transform the pool of individual parameters into a smaller and focused set of mutually uncorrelated gait parameters showing strong genotype difference. Kinematic fine motor analysis of transgenic animal models described in this presentation show that this method isa sensitive, objective and fully automated tool that allows earlier and more sensitive detection of progressive neuromuscular and CNS disease phenotypes. As a result of the analysis a comprehensive set of fine motor parameters for each model is created, and these parameters provide better understanding of the disease progression and enhanced sensitivity of this assay for therapeutic testing compared to classical motor behavior tests. In SOD1 G93A, R6/2, and Q175KI mice, the alterations in gait were evident already several weeks earlier than with traditional gross motor assays. Kinematic testing can be applied to a wider set of motor readouts beyond gait in order to study whole body movement patterns such as with relation to joints and various body parts longitudinally, providing a sophisticated and translatable method for disseminating motor components in rodent disease models and evaluating therapeutic interventions.

Keywords: Gait analysis, kinematic, motor impairment, inherent feature

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Authors: Abe Degale D., Cheng Jian

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When creating automated video surveillance systems, violent action recognition is crucial. In recent years, hand-crafted feature detectors have been the primary method for achieving violence detection, such as the recognition of fighting activity. Researchers have also looked into learning-based representational models. On benchmark datasets created especially for the detection of violent sequences in sports and movies, these methods produced good accuracy results. The Hockey dataset's videos with surveillance camera motion present challenges for these algorithms for learning discriminating features. Image recognition and human activity detection challenges have shown success with deep representation-based methods. For the purpose of detecting violent images and identifying aggressive human behaviours, this research suggested a deep representation-based model using the transfer learning idea. The results show that the suggested approach outperforms state-of-the-art accuracy levels by learning the most discriminating features, attaining 99.34% and 99.98% accuracy levels on the Hockey and Movies datasets, respectively.

Keywords: violence detection, faster RCNN, transfer learning and, surveillance video

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Authors: L. Oufni, M. Amrane, R. Rabi

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Radon (and thoron) is a naturally occurring radioactive noble gas, having variable distribution in the geological environment. The exposure of human beings to ionizing radiation from natural sources is a continuing and inescapable feature of life on earth. Radon, thoron and their short-lived decay products in the atmosphere are the most important contributors to human exposure from natural sources. The aim of this study is to determine alpha-and beta-activities per unit volume of air due to radon (222Rn), thoron (220Rn) and their progenies in the air of ancient mine of Aouli in which there is no working activity is situated at approximately 25 km north of the city of Midelt (Morocco), by using LR-115 type II and CR-39 solid state nuclear track detectors (SSNTDs). Equilibrium factors between radon and its daughters and between thoron and its progeny were evaluated in the studied atmospheres. The committed equivalent doses due to the 218Po and 214Po radon short-lived progeny were evaluated in different tissues of the respiratory tract of the visitors of the considered ancient mine. The visitors in these mines spent a good amount of time. It was essential to let the staff know about these values and take the needed steps to prevent any health complications.

Keywords: radon, thoron, concentration, exposure dose, SSNTD, mine

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Authors: Fei Gao, Rodolfo C. Raga Jr.

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This research proposal will ascertain the major risk factors for diabetes and to design a predictive model for risk assessment. The project aims to improve diabetes early detection and management by utilizing data science techniques, which may improve patient outcomes and healthcare efficiency. The phase relation values of each attribute were used to analyze and choose the attributes that might influence the examiner's survival probability using Diabetes Health Indicators Dataset from Kaggle’s data as the research data. We compare and evaluate eight machine learning algorithms. Our investigation begins with comprehensive data preprocessing, including feature engineering and dimensionality reduction, aimed at enhancing data quality. The dataset, comprising health indicators and medical data, serves as a foundation for training and testing these algorithms. A rigorous cross-validation process is applied, and we assess their performance using five key metrics like accuracy, precision, recall, F1-score, and area under the receiver operating characteristic curve (AUC-ROC). After analyzing the data characteristics, investigate their impact on the likelihood of diabetes and develop corresponding risk indicators.

Keywords: diabetes, risk factors, predictive model, risk assessment, data science techniques, early detection, data analysis, Kaggle

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Authors: Ankur Chaudhuri, Sibani Sen Chakraborty

Abstract:

In human, glutaminyl cyclase activity is highly abundant in neuronal and secretory tissues and is preferentially restricted to hypothalamus and pituitary. The N-terminal modification of β-amyloids (Aβs) peptides by the generation of a pyro-glutamyl (pGlu) modified Aβs (pE-Aβs) is an important process in the initiation of the formation of neurotoxic plaques in Alzheimer’s disease (AD). This process is catalyzed by glutaminyl cyclase (QC). The expression of QC is characteristically up-regulated in the early stage of AD, and the hallmark of the inhibition of QC is the prevention of the formation of pE-Aβs and plaques. A computer-aided drug design (CADD) process was employed to give an idea for the designing of potentially active compounds to understand the inhibitory potency against human glutaminyl cyclase (QC). This work elaborates the ligand-based and structure-based pharmacophore exploration of glutaminyl cyclase (QC) by using the known inhibitors. Three dimensional (3D) quantitative structure-activity relationship (QSAR) methods were applied to 154 compounds with known IC50 values. All the inhibitors were divided into two sets, training-set, and test-sets. Generally, training-set was used to build the quantitative pharmacophore model based on the principle of structural diversity, whereas the test-set was employed to evaluate the predictive ability of the pharmacophore hypotheses. A chemical feature-based pharmacophore model was generated from the known 92 training-set compounds by HypoGen module implemented in Discovery Studio 2017 R2 software package. The best hypothesis was selected (Hypo1) based upon the highest correlation coefficient (0.8906), lowest total cost (463.72), and the lowest root mean square deviation (2.24Å) values. The highest correlation coefficient value indicates greater predictive activity of the hypothesis, whereas the lower root mean square deviation signifies a small deviation of experimental activity from the predicted one. The best pharmacophore model (Hypo1) of the candidate inhibitors predicted comprised four features: two hydrogen bond acceptor, one hydrogen bond donor, and one hydrophobic feature. The Hypo1 was validated by several parameters such as test set activity prediction, cost analysis, Fischer's randomization test, leave-one-out method, and heat map of ligand profiler. The predicted features were then used for virtual screening of potential compounds from NCI, ASINEX, Maybridge and Chembridge databases. More than seven million compounds were used for this purpose. The hit compounds were filtered by drug-likeness and pharmacokinetics properties. The selective hits were docked to the high-resolution three-dimensional structure of the target protein glutaminyl cyclase (PDB ID: 2AFU/2AFW) to filter these hits further. To validate the molecular docking results, the most active compound from the dataset was selected as a reference molecule. From the density functional theory (DFT) study, ten molecules were selected based on their highest HOMO (highest occupied molecular orbitals) energy and the lowest bandgap values. Molecular dynamics simulations with explicit solvation systems of the final ten hit compounds revealed that a large number of non-covalent interactions were formed with the binding site of the human glutaminyl cyclase. It was suggested that the hit compounds reported in this study could help in future designing of potent inhibitors as leads against human glutaminyl cyclase.

Keywords: glutaminyl cyclase, hit lead, pharmacophore model, simulation

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Authors: Clifford Irikefe Gbeyonron

Abstract:

Applied linguists concur that there is low-level achievement in English language use among Nigerian secondary school students. One of the factors that exacerbate this is classroom feature of which large class size is obvious. This study investigated the impact of large classes on learning English as a second language (ESL) at junior secondary school (JSS) in Yobe State. To achieve this, Solomon four-group experimental design was used. 382 subjects were divided into four groups and taught ESL for thirteen weeks. 356 subjects wrote the post-test. Data from the systematic observation and post-test were analyzed via chi square and ANOVA. Results indicated that learners in large classes (LLC) attain lower linguistic progress than learners in small classes (LSC). Furthermore, LSC have more chances to access teacher evaluation and participate actively in classroom discourse than LLC. In consequence, large classes have adverse effects on learning ESL in Yobe State. This is inimical to English language education given that each learner of ESL has their individual peculiarity within each class. It is recommended that strategies that prioritize individualization, grouping, use of language teaching aides, and theorization of innovative models in respect of large classes be considered.

Keywords: large classes, achievement, classroom discourse

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Authors: Zuoji Huang, Haiming Qian, Chunlin Wang, Jinyan Sun, Nan Xu

Abstract:

In this paper, the strategy to extract double road edge lines from acquired road stripe image was explored. The workflow is as follows: the road stripes are acquired by probabilistic boosting tree algorithm and morphological algorithm immediately, and road centerlines are detected by thinning algorithm, so the initial road edge lines can be acquired along the road centerlines. Then we refine the results with big variation of local curvature of centerlines. Specifically, the energy function of edge line is constructed by gradient feature and spectral information, and Dijkstra algorithm is used to optimize the initial road edge lines. The Snake model is constructed to solve the fracture problem of intersection, and the discrete dynamic programming algorithm is used to solve the model. After that, we could get the final road network. Experiment results show that the strategy proposed in this paper can be used to extract the continuous and smooth road edge lines from high-resolution remote sensing images with an accuracy of 88% in our study area.

Keywords: road edge lines extraction, energy function, intersection fracture, Snake model

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Authors: Lianzhong Zhang, Chao Huang

Abstract:

Sea-land segmentation is a basic step in many tasks such as sea surface monitoring and ship detection. The existing sea-land segmentation algorithms have poor segmentation accuracy, and the parameter adjustments are cumbersome and difficult to meet actual needs. Also, the current sea-land segmentation adopts traditional deep learning models that use Convolutional Neural Networks (CNN). At present, the transformer architecture has achieved great success in the field of natural images, but its application in the field of radar images is less studied. Therefore, this paper proposes a sea-land segmentation method based on the transformer architecture to strengthen edge supervision. It uses a self-attention mechanism with a gating strategy to better learn relative position bias. Meanwhile, an additional edge supervision branch is introduced. The decoder stage allows the feature information of the two branches to interact, thereby improving the edge precision of the sea-land segmentation. Based on the Gaofen-3 satellite image dataset, the experimental results show that the method proposed in this paper can effectively improve the accuracy of sea-land segmentation, especially the accuracy of sea-land edges. The mean IoU (Intersection over Union), edge precision, overall precision, and F1 scores respectively reach 96.36%, 84.54%, 99.74%, and 98.05%, which are superior to those of the mainstream segmentation models and have high practical application values.

Keywords: SAR, sea-land segmentation, deep learning, transformer

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Authors: Wanhyun Cho, Soonja Kang, Sangkyoon Kim, Soonyoung Park

Abstract:

In this paper, we present the human action recognition method using the variational Bayesian HMM with the Dirichlet process mixture (DPM) of the Gaussian-Wishart emission model (GWEM). First, we define the Bayesian HMM based on the Dirichlet process, which allows an infinite number of Gaussian-Wishart components to support continuous emission observations. Second, we have considered an efficient variational Bayesian inference method that can be applied to drive the posterior distribution of hidden variables and model parameters for the proposed model based on training data. And then we have derived the predictive distribution that may be used to classify new action. Third, the paper proposes a process of extracting appropriate spatial-temporal feature vectors that can be used to recognize a wide range of human behaviors from input video image. Finally, we have conducted experiments that can evaluate the performance of the proposed method. The experimental results show that the method presented is more efficient with human action recognition than existing methods.

Keywords: human action recognition, Bayesian HMM, Dirichlet process mixture model, Gaussian-Wishart emission model, Variational Bayesian inference, prior distribution and approximate posterior distribution, KTH dataset

Procedia PDF Downloads 351