Search results for: convolutional long short-term memory

Authors: Nirmal Sugathan, Santosh Maruthy

Abstract:

Background: A considerable number of studies have evidenced that phonological encoding (PE) and working memory (WM) skills operate differently in adults who stutter (AWS). In order to tap these skills, several paradigms have been employed such as phonological priming, phoneme monitoring, and nonword repetition tasks. This study, however, utilizes a word jumble paradigm to assess both PE and WM using different modalities and this may give a better understanding of phonological processing deficits in AWS. Aim: The present study investigated PE and WM abilities in conjunction with lexical access in AWS using jumbled words. The study also aimed at investigating the effect of increase in cognitive load on phonological processing in AWS by comparing the speech reaction time (SRT) and accuracy scores across various syllable lengths. Method: Participants were 11 AWS (Age range=19-26) and 11 adults who do not stutter (AWNS) (Age range=19-26) matched for age, gender and handedness. Stimuli: Ninety 3-, 4-, and 5-syllable jumbled words (JWs) (n=30 per syllable length category) constructed from Kannada words served as stimuli for jumbled word paradigm. In order to generate jumbled words (JWs), the syllables in the real words were randomly transpositioned. Procedures: To assess PE, the JWs were presently visually using DMDX software and for WM task, JWs were presented through auditory mode through headphones. The participants were asked to silently manipulate the jumbled words to form a Kannada real word and verbally respond once. The responses for both tasks were audio recorded using record function in DMDX software and the recorded responses were analyzed using PRAAT software to calculate the SRT. Results: SRT: Mann-Whitney test results demonstrated that AWS performed significantly slower on both tasks (p < 0.001) as indicated by increased SRT. Also, AWS presented with increased SRT on both the tasks in all syllable length conditions (p < 0.001). Effect of syllable length: Wilcoxon signed rank test was carried out revealed that, on task assessing PE, the SRT of 4syllable JWs were significantly higher in both AWS (Z= -2.93, p=.003) and AWNS (Z= -2.41, p=.003) when compared to 3-syllable words. However, the findings for 4- and 5-syllable words were not significant. Task Accuracy: The accuracy scores were calculated for three syllable length conditions for both PE and PM tasks and were compared across the groups using Mann-Whitney test. The results indicated that the accuracy scores of AWS were significantly below that of AWNS in all the three syllable conditions for both the tasks (p < 0.001). Conclusion: The above findings suggest that PE and WM skills are compromised in AWS as indicated by increased SRT. Also, AWS were progressively less accurate in descrambling JWs of increasing syllable length and this may be interpreted as, rather than existing as a uniform deficiency, PE and WM deficits emerge when the cognitive load is increased. AWNS exhibited increased SRT and increased accuracy for JWs of longer syllable length whereas AWS was not benefited from increasing the reaction time, thus AWS had to compromise for both SRT and accuracy while solving JWs of longer syllable length.

Keywords: adults who stutter, phonological ability, working memory, encoding, jumbled words

Procedia PDF Downloads 240

Authors: Chen-Yuan Hsu

Abstract:

Introduction Elderly is growing and results to live in the long-term care (LTC) and then due to the routine of the facilities in Taiwan, also resulted to losing of those people with environment interaction, so, the self-reliance support (SRS) for those people to experience environment interaction is an essential. Methods This study was recruited samples of a LTC in the central of Taiwan. There was a following research on the SRS group with 20 samples collected and routine care group with 20 samples. A structured questionnaire as the Environment Interaction Dimension, as data collection included demographic information and the dimensions of environment interaction. Data analysis used SPSS 22.0 for Window 2000 to report the finding. Results The Environment Interaction Dimension for Taiwanese is a Chinese version of the containing 8 items. The result of t-test analysis found that environment interaction showed a significant difference between groups (p＜.05), the result recommended that there was a higher score of environment interaction dimension on the SRS group (29.90±5.56) comparing with the routine care group (22.1±5.53). Conclusion This study showed that the SRS group was higher than the routine care group on the environment interaction dimension for Taiwanese elderly living in the LTC. The results can also provide the reference for LTC, to encourage those people to participate in SRS in LTC, and therefore also improving their environment interaction.

Keywords: self-reliance support, environment interaction, long-term care, elderly

Procedia PDF Downloads 104

Authors: Svitov David, Alyamkin Sergey

Abstract:

The usage of convolutional neural networks (CNNs) in conjunction with the margin-based softmax approach demonstrates the state-of-the-art performance for the face recognition problem. Recently, lightweight neural network models trained with the margin-based softmax have been introduced for the face identification task for edge devices. In this paper, we propose a distillation method for lightweight neural network architectures that outperforms other known methods for the face recognition task on LFW, AgeDB-30 and Megaface datasets. The idea of the proposed method is to use class centers from the teacher network for the student network. Then the student network is trained to get the same angles between the class centers and face embeddings predicted by the teacher network.

Keywords: ArcFace, distillation, face recognition, margin-based softmax

Procedia PDF Downloads 146

Authors: Othman Maklouf, Abdunnaser Tresh

Abstract:

GPS and inertial navigation system (INS) have complementary qualities that make them ideal use for sensor fusion. The limitations of GPS include occasional high noise content, outages when satellite signals are blocked, interference and low bandwidth. The strengths of GPS include its long-term stability and its capacity to function as a stand-alone navigation system. In contrast, INS is not subject to interference or outages, have high bandwidth and good short-term noise characteristics, but have long-term drift errors and require external information for initialization. A combined system of GPS and INS subsystems can exhibit the robustness, higher bandwidth and better noise characteristics of the inertial system with the long-term stability of GPS. The most common estimation algorithm used in integrated INS/GPS is the Kalman Filter (KF). KF is able to take advantages of these characteristics to provide a common integrated navigation implementation with performance superior to that of either subsystem (GPS or INS). This paper presents a simplified KF algorithm for land vehicle navigation application. In this integration scheme, the GPS derived positions and velocities are used as the update measurements for the INS derived PVA. The KF error state vector in this case includes the navigation parameters as well as the accelerometer and gyroscope error states.

Keywords: GPS, INS, Kalman filter, inertial navigation system

Procedia PDF Downloads 471

Authors: Ebenezer Joseph

Abstract:

Chess is a useful tool to enhance general and specific cognitive functioning in children. The present study aims to assess the impact of chess on cognitive in children and to measure the differential impact of socio-demographic factors like age and gender of the child on the effectiveness of the chess intervention.This research study used an experimental design to study the impact of the Training in Chess on the intelligence of children. The Pre-test Post-test Control Group Design was utilized. The research design involved two groups of children: an experimental group and a control group. The experimental group consisted of children who participated in the one-year Chess Training Intervention, while the control group participated in extra-curricular activities in school. The main independent variable was training in chess. Other independent variables were gender and age of the child. The dependent variable was the cognitive functioning of the child (as measured by IQ, working memory index, processing speed index, perceptual reasoning index, verbal comprehension index, numerical reasoning, verbal reasoning, non-verbal reasoning, social intelligence, language, conceptual thinking, memory, visual motor and creativity). The sample consisted of 200 children studying in Government and Private schools. Random sampling was utilized. The sample included both boys and girls falling in the age range 6 to 16 years. The experimental group consisted of 100 children (50 from Government schools and 50 from Private schools) with an equal representation of boys and girls. The control group similarly consisted of 100 children. The dependent variables were assessed using Binet-Kamat Test of Intelligence, Wechsler Intelligence Scale for Children - IV (India) and Wallach Kogan Creativity Test. The training methodology comprised Winning Moves Chess Learning Program - Episodes 1–22, lectures with the demonstration board, on-the-board playing and training, chess exercise through workbooks (Chess school 1A, Chess school 2, and tactics) and working with chess software. Further students games were mapped using chess software and the brain patterns of the child were understood. They were taught the ideas behind chess openings and exposure to classical games were also given. The children participated in mock as well as regular tournaments. Preliminary analysis carried out using independent t tests with 50 children indicates that chess training has led to significant increases in the intelligent quotient. Children in the experimental group have shown significant increases in composite scores like working memory and perceptual reasoning. Chess training has significantly enhanced the total creativity scores, line drawing and pattern meaning subscale scores. Systematically learning chess as part of school activities appears to have a broad spectrum of positive outcomes.

Keywords: chess, intelligence, creativity, children

Procedia PDF Downloads 257

Authors: Stephan Printz, Kristina Lahl, René Vossen, Sabina Jeschke

Abstract:

Strategic investment decisions are characterized by high innovation potential and long-term effects on the competitiveness of enterprises. Due to the uncertainty and risks involved in this complex decision making process, the need arises for well-structured support activities. A method that considers cost and the long-term added value is the cost-benefit effectiveness estimation. One of those methods is the “profitability estimation focused on benefits – PEFB”-method developed at the Institute of Management Cybernetics at RWTH Aachen University. The method copes with the challenges associated with strategic investment decisions by integrating long-term non-monetary aspects whilst also mapping the chronological sequence of an investment within the organization’s target system. Thus, this method is characterized as a holistic approach for the evaluation of costs and benefits of an investment. This participation-oriented method was applied to business environments in many workshops. The results of the workshops are a library of more than 96 cost aspects, as well as 122 benefit aspects. These aspects are preprocessed and comparatively analyzed with regards to their alignment to a series of risk levels. For the first time, an accumulation and a distribution of cost and benefit aspects regarding their impact and probability of occurrence are given. The results give evidence that the PEFB-method combines precise measures of financial accounting with the incorporation of benefits. Finally, the results constitute the basics for using information technology and data science for decision support when applying within the PEFB-method.

Keywords: cost-benefit analysis, multi-criteria decision, profitability estimation focused on benefits, risk and uncertainty analysis

Procedia PDF Downloads 445

Authors: Praveen S. Muthukumarana, Achala C. Aponso

Abstract:

A large percentage of global crop harvest is lost due to crop diseases. Timely identification and treatment of crop diseases is difficult in many developing nations due to insufficient trained professionals in the field of agriculture. Many crop diseases can be accurately diagnosed by visual symptoms. In the past decade, deep learning has been successfully utilized in domains such as healthcare but adoption in agriculture for plant disease detection is rare. The literature shows that models trained with popular datasets such as PlantVillage does not generalize well on real world images. This paper attempts to find out how to make plant disease identification models that generalize well with real world images.

Keywords: agriculture, convolutional neural network, deep learning, plant disease classification, plant disease detection, plant disease diagnosis

Procedia PDF Downloads 145

Authors: Ayyaz Hussain, Tariq Sadad

Abstract:

Breast cancer (BC) is one of the widespread ailments among females globally. The early prognosis of BC can decrease the mortality rate. Exact findings of benign tumors can avoid unnecessary biopsies and further treatments of patients under investigation. However, due to variations in images, it is a tough job to isolate cancerous cases from normal and benign ones. The machine learning technique is widely employed in the classification of BC pattern and prognosis. In this research, a deep convolution neural network (DCNN) called AlexNet architecture is employed to get more discriminative features from breast tissues. To achieve higher accuracy, K-nearest neighbor (KNN) classifiers are employed as a substitute for the softmax layer in deep learning. The proposed model is tested on a widely used breast image database called MIAS dataset for experimental purposes and achieved 99% accuracy.

Keywords: breast cancer, DCNN, KNN, mammography

Procedia PDF Downloads 136

Authors: Samia Ait Ali Yahia

Abstract:

In Algeria, the most impressive and most known prehistoric art is the painted or engraved rock art which is present with abundance in several regions. The existence of rock art in Great Kabylia region has been known for over sixty years. The main purpose of this research is to show the dangers facing these rock paintings and engravings and what are the arrangements for their protection and recovery. As every vestige destroyed is a part of the world's memory which disappears, some steps have to be taken in order to protect these historical and archaeological heritages.

Keywords: rock paintings and engravings, preservation, valorization, Kabylia

Procedia PDF Downloads 456

Authors: Mehrnaz Alibeikloo, Hadi Khabbaz, Behzad Fatahi

Abstract:

The spectral random field method is one of the widely used methods to obtain more reliable and accurate results in geotechnical problems involving material variability. Karhunen-Loeve (K-L) expansion method was applied to perform random field discretization of cross-correlated creep parameters. Karhunen-Loeve expansion method is based on eigenfunctions and eigenvalues of covariance function adopting Kernel integral solution. In this paper, the accuracy of Karhunen-Loeve expansion was investigated to predict long-term settlement of soft soils adopting elastic visco-plastic creep model. For this purpose, a parametric study was carried to evaluate the effect of K-L expansion terms and spatial correlation length on the reliability of results. The results indicate that small values of spatial correlation length require more K-L expansion terms. Moreover, by increasing spatial correlation length, the coefficient of variation (COV) of creep settlement increases, confirming more conservative and safer prediction.

Keywords: Karhunen-Loeve expansion, long-term settlement, reliability analysis, spatial correlation length

Procedia PDF Downloads 159

Authors: Faisal A. Salih

Abstract:

Vinpocetine (Vin) is an ethyl ester of apovincamic acid and is a semisynthetic derivative of vincamine, an alkaloid from plants of the genus Periwinkle (plant) vinca minor. It was found that this compound stimulates cerebral metabolism: it increases the uptake of glucose and oxygen, as well as the consumption of these substances by the brain tissue. Vinpocetine enhances the flow of blood in the brain and has a vasodilating, antihypertensive, and antiplatelet effect. Vinpocetine seems to improve the human ability to acquire new memories and restore memories that have been lost. This drug has been clinically used for the treatment of cerebrovascular disorders such as stroke and dementia memory disorders, as well as in ophthalmology and otorhinolaryngology. It has no side effects, and no toxicity has been reported when using vinpocetine for a long time. For the quantitative determination of Vin in dosage forms, the HPLC methods are generally used. A promising alternative is potentiometry with Vin- selective electrode, which does not require expensive equipment and materials. Another advantage of the potentiometric method is that the pills and solutions for injections can be used directly without separation from matrix components, which reduces both analysis time and cost. In this study, it was found that the choice of a good plasticizer an electrode with the following membrane composition: PVC (32.8 wt.%), ortho-nitrophenyl octyl ether (66.6 wt.%), tetrakis-4-chlorophenyl borate (0.6 wt.%) exhibits excellent analytical performance: lower detection limit (LDL) 1.2•10⁻⁷ M, linear response range (LRR) 1∙10⁻³–3.9∙10⁻⁶ M, the slope of the electrode function 56.2±0.2 mV/decade). Vin masses per average tablet weight determined by direct potentiometry (DP) and potentiometric titration (PT) methods for the two different sets of 10 tablets were (100.35±0.2–100.36±0.1) mg for two sets of blister packs. The mass fraction of Vin in individual tablets, determined using DP, was (9.87 ± 0.02–10.16 ±0.02) mg, while the RSD was (0.13–0.35%). The procedure has very good reproducibility, and excellent compliance with the declared amounts was observed.

Keywords: vinpocetine, potentiometry, ion selective electrode, pharmaceutical analysis

Procedia PDF Downloads 73

Authors: Taimoor Hussain Alvi, Syed Toqueer Akhter

Abstract:

The focus of this research is to identify Pak-India bilateral trade spillover effects upon Pakistan’s Growth rate. Cross-country spillover growth Effects have been linked with openness and access to markets. In this research, we intend to see the short run and long run effects of Pak-India Bilateral Trade Openness upon economic growth in Pakistan. Trade Openness has been measured as the sum of bilateral exports and imports between the two countries. Increased emphasis on the condition and environment of financial markets is laid in light of globalization and trade liberalization. This research paper makes use of the Univariate Autoregressive Distributed Lagged Model to analyze the effects of bilateral trade variables upon the growth pattern of Pakistan in the short run and long run. Key findings of the study empirically support the notion that increased bilateral trade will be beneficial for Pakistan in the short run because of cost advantage and knowledge spillover in terms of increased technical and managerial ability from multinational firms. However, contrary to extensive literature, increased bilateral trade measures will affect Pakistan’s growth rate negatively in the long run because of the industrial size differential and increased integration of Indian economy with the world.

Keywords: bilateral trade openness, spillover, comparative advantage, univariate

Procedia PDF Downloads 481

Authors: Michael Luc Andre, Célia Maintenant

Abstract:

Theory of mind is a milestone in child development which allows children to understand that others could have different mental states than theirs. Understanding the developmental stages of theory of mind in children leaded researchers on two Connected research problems. In one hand, the link between executive function and theory of mind, and on the other hand, the relationship of theory of mind and syntax processing. These two lines of research involved a great literature, full of important results, despite certain level of disagreement between researchers. For a long time, these two research perspectives continue to grow up separately despite research conclusion suggesting that the three variables should implicate same developmental period. Indeed, our goal was to study the relation between theory of mind, executive function, and language via a unique research question. It supposed that between executive function and language, one of the two variables could play a critical role in the relationship between theory of mind and the other variable. Thus, 112 children aged between three and six years old were recruited for completing a receptive and an expressive vocabulary task, a syntax understanding task, a theory of mind task, and three executive function tasks (inhibition, cognitive flexibility and working memory). The results showed significant correlations between performance on theory of mind task and performance on executive function domain tasks, except for cognitive flexibility task. We also found significant correlations between success on theory of mind task and performance in all language tasks. Multiple regression analysis justified only syntax and general abilities of language as possible predictors of theory of mind performance in our preschool age children sample. The results were discussed in the perspective of a great role of language abilities in theory of mind development. We also discussed possible reasons that could explain the non-significance of executive domains in predicting theory of mind performance, and the meaning of our results for the literature.

Keywords: child development, executive function, general language, syntax, theory of mind

Procedia PDF Downloads 64

Authors: Emiliya Tsankova, Genoveva Zlateva, Violeta Kostadinova

Abstract:

The challenges modern education faces undoubtedly require reforms and innovations aimed at the reconceptualization of existing educational strategies, the introduction of new concepts and novel techniques and technologies related to the recasting of the aims of education and the remodeling of the content and methodology of education which would guarantee the streamlining of our education with basic European values. Aim: The aim of the current research is the development of a didactic technology for the assessment of the applicability and efficacy of game techniques in pedagogic practice calibrated to specific content and the age specificity of learners, as well as for evaluating the efficacy of such approaches for the facilitation of the acquisition of biological knowledge at a higher theoretical level. Results: In this research, we examine the objectivity, reliability and validity of two newly introduced diagnostic parameters for assessing the durability of the acquired knowledge. A pedagogic experiment has been carried out targeting the verification of the hypothesis that the introduction of game techniques in biological education leads to an increase in the quantity, quality and durability of the knowledge acquired by students. For the purposes of monitoring the effect of the application of the pedagogical technique employing game methodology on the durability of the acquired knowledge a test-base examination has been applied to students from a control group (CG) and students form an experimental group on the same content after a six-month period. The analysis is based on: 1.A study of the statistical significance of the differences of the tests for the CG and the EG, applied after a six-month period, which however is not indicative of the presence or absence of a marked effect from the applied pedagogic technique in cases when the entry levels of the two groups are different. 2.For a more reliable comparison, independently from the entry level of each group, another “indicator of efficacy of game techniques for the durability of knowledge” which has been used for the assessment of the achievement results and durability of this methodology of education. The monitoring of the studied parameters in their dynamic unfolding in different age groups of learners unquestionably reveals a positive effect of the introduction of game techniques in education in respect of durability and permanence of acquired knowledge. Methods: In the current research the following battery of methods and techniques of research for diagnostics has been employed: theoretical analysis and synthesis; an actual pedagogical experiment; questionnaire; didactic testing and mathematical and statistical methods. The data obtained have been used for the qualitative and quantitative of the results which reflect the efficacy of the applied methodology. Conclusion: The didactic model of the parameters researched in the framework of a specific study of pedagogic diagnostics is based on a general, interdisciplinary approach. Enhanced durability of the acquired knowledge proves the transition of that knowledge from short-term memory storage into long-term memory of pupils and students, which justifies the conclusion that didactic plays have beneficial effects for the betterment of learners’ cognitive skills. The innovations in teaching enhance the motivation, creativity and independent cognitive activity in the process of acquiring the material thought. The innovative methods allow for untraditional means for assessing the level of knowledge acquisition. This makes possible the timely discovery of knowledge gaps and the introduction of compensatory techniques, which in turn leads to deeper and more durable acquisition of knowledge.

Keywords: objectivity, reliability and validity of pedagogical diagnostic parameters introduced in the framework of a specific research

Procedia PDF Downloads 392

Authors: Ashan Wijekoon, Abi Beane, Subashini Jayawardana

Abstract:

Background: Civil war in Sri Lanka ended a decade ago, leaving thousands of army veterans permanently disabled following lower-limb amputations. Quantifying long-term functional health and psychological wellbeing will inform the development of tailored home-based rehabilitation intervention. Objectives: To assess the long-term health and quality of life of Sri Lankan soldiers with traumatic lower-limb amputation.Methods and Materials: A comparative cross-sectional study was conducted in five districts of Sri Lanka. Using stratified random sample technique, two groups of 85 participants were selected; group 1, community re-integrated male army veterans with unilateral lower-limb amputation, and group 2, age and sex matched normal healthy individuals. Long-term health and quality of life (QoL) outcomes were assessed and compared between the two groups using self-administered Short-Form Health Survey-36 questionnaire (SF-36) previously validated for use in Sri Lanka. Results: Group 1 were active prosthetic users who had undergone amputation > ten years ago (Mean±SD: 21.7±5.9). The most prevalent comorbidities for group 1 and 2 were hypertension and diabetes (22.4% and 30.6% and 9.4% and 9.8%, respectively). In group 1, injury-associated long-term health outcomes included knee osteoarthritis (18.8%), knee pain (20.0%), and back pain (69.4%). Scores of physical health and psychological wellbeing were 53.1 (IQR 64.4- 43.8) and 63.5 (IQR 73.3- 51.4) for each group, respectively. Scores revealed the highest QoL related to social functioning (75 (IQR 87.5- 62.5)) and the poorest aspects of QoL related to general health (40 (IQR 50- 35)). Prevalence of comorbidities was significantly higher, and QoL outcomes were significantly lower among soldiers compared to normal healthy individuals (p<0.05).Conclusion: Higher prevalence of comorbidities, poor physical health, and lower QoL outcomes were more prevalent in soldiers with lower-limb amputation when compared to healthy counterparts.

Keywords: community-based, disability, health outcomes, quality of life, soldiers

Procedia PDF Downloads 129

Authors: Merrylord Harb-Azar

Abstract:

Traditionally, PTSD treatment has involved trauma-focused cognitive behaviour therapy (TF CBT) to consolidate traumatic memories. A piloted integrated treatment of TF CBT and eye movement desensitisation reprocessing therapy (EMDR) of eight phases will fasten the rate memory is being consolidated and enhance cognitive functioning in patients with PTSD. Patients spend a considerable amount of time in treatment managing their traumas experienced firsthand, or from aversive details ranging from war, assaults, accidents, abuse, hostage related, riots, or natural disasters. The time spent in treatment or as inpatient affects overall quality of life, relationships, cognitive functioning, and overall sense of identity. EMDR is being offered twice a week in conjunction with the standard prolonged exposure as an inpatient in a private hospital. Prolonged exposure for up to 5 hours per day elicits the affect response required for EMDR sessions in the afternoon to unlock unprocessed memories and facilitate consolidation in the amygdala and hippocampus. Results are indicating faster consolidation of memories, reduction in symptoms in a shorter period of time, reduction in admission time, which is enhancing the quality of life and relationships, and improved cognition. The impact of events scale (IES) results demonstrate a significant reduction in symptoms, trauma symptoms inventory (TSI), and posttraumatic stressor disorder check list (PCL) that demonstrates large effect sizes to date. An integrated treatment approach for PTSD achieves a faster resolution of memories, improves cognition, and reduces the amount of time spent in therapy.

Keywords: EMDR enhances cognitive functioning, faster consolidation of trauma memory, integrated treatment of TF CBT and EMDR, reduction in inpatient admission time

Procedia PDF Downloads 145

Authors: Chen Xi, Liu Xuebing, Lao Xueru, Kuan Sinman, Jiang Yike, Wang Hanwei, Yang Xiaolang, Zhou Junjie, Xie Jinpeng

Abstract:

To further promote the development of smart cities, the microscopic "nerve endings" of the City Intelligent Model (CIM) are extended to be more sensitive. In this paper, we develop a pedestrian trajectory twin terminal based on the CIM and CNN technology. It also uses 5G networks, architectural and geoinformatics technologies, convolutional neural networks, combined with deep learning networks for human behavior recognition models, to provide empirical data such as 'pedestrian flow data and human behavioral characteristics data', and ultimately form spatial performance evaluation criteria and spatial performance warning systems, to make the empirical data accurate and intelligent for prediction and decision making.

Keywords: urban planning, urban governance, CIM, artificial intelligence, sustainable development

Procedia PDF Downloads 418

Authors: R. Deju, M. Mincu, D. Gurau

Abstract:

During the interim storage or final disposal of low level waste, migration/diffusion of radionuclides can occur when the waste comes in contact with water. The long-term leaching behaviour into surrounding fluid (demineralized water) of ¹³⁷Cs, ⁶⁰Co and ¹⁵²Eu radionuclides, artificially incorporated in mortar matrices made from natural aggregates (river sand) and recycled radioactive concrete was studied. Results presented in this work are obtained in two years of mortar testing and will be used for the safety increasing in the storage of low level radioactive waste. The study involved the influence of curing time, type and size distribution of the aggregates on leaching behaviour. The mortar samples were immersed in distilled water for 30 days. The leached activity of the mortar samples was measured on samples from the immersing water and analyzed through a gamma-ray spectrometry method using an HPGe detector with a GESPECOR code for efficiency evaluation. The long-term leaching behaviour of the radionuclides was evaluated from the leaching data calculating the apparent diffusion coefficient.

Keywords: gamma spectrometry, leaching behavior, reuse and recycling of radioactive concrete, waste management

Procedia PDF Downloads 247

Authors: Chen Xi, Kuan Sinman, LI Haofeng, Huang Hongming, Zeng Chengyu, Tong Zhiyuan

Abstract:

Stray animals are on the rise in mainland China's cities. There are legal reasons for this, namely the lack of protection for domestic pets in mainland China, where only wildlife protection laws exist. At a social level, the ease with which families adopt pets and the lack of a social view of animal nature has led to the frequent abandonment and loss of stray animals. If left unmanaged, conflicts between humans and stray animals can also increase. This project provides an inexpensive and widely applicable management tool for urban management by collecting videos and pictures of stray animals captured by surveillance or transmitted by humans and using artificial intelligence technology (mainly using YOLOv5 recognition technology) and recording and managing them in a database.

Keywords: urban planning, urban governance, artificial intelligence, convolutional neural network

Procedia PDF Downloads 104

Authors: Dalia Feiziene, Virginijus Feiza, Agne Putramentaite, Jonas Volungevicius, Kristina Amaleviciute, Sarunas Antanaitis

Abstract:

The investigations on soil conservation are one of the most important topics in modern agronomy. Soil management practices have great influence on soil physico-chemical quality and GHG emission. Research objective: To reveal the sensitivity and vitality of soils with different texture to long-term antropogenisation on Cambisol in Central Lithuania and to compare them with not antropogenised soil resources. Methods: Two long-term field experiments (loam on loam; sandy loam on loam) with different management intensity were estimated. Disturbed and undisturbed soil samples were collected from 5-10, 15-20 and 30-35 cm depths. Soil available P and K contents were determined by ammonium lactate extraction, total N by the dry combustion method, SOC content by Tyurin titrimetric (classical) method, texture by pipette method. In undisturbed core samples soil pore volume distribution, plant available water (PAW) content were determined. A closed chamber method was applied to quantify soil respiration (SR). Results: Long-term resources management changed soil quality. In soil with loam texture, within 0-10, 10-20 and 30-35 cm soil layers, significantly higher PAW, SOC and mesoporosity (MsP) were under no-tillage (NT) than under conventional tillage (CT). However, total porosity (TP) under NT was significantly higher only in 0-10 cm layer. MsP acted as dominant factor for N, P and K accumulation in adequate layers. P content in all soil layers was higher under NT than in CT. N and K contents were significantly higher than under CT only in 0-10 cm layer. In soil with sandy loam texture, significant increase in SOC, PAW, MsP, N, P and K under NT was only in 0-10 cm layer. TP under NT was significantly lower in all layers. PAW acted as strong dominant factor for N, P, K accumulation. The higher PAW the higher NPK contents were determined. NT did not secure chemical quality within deeper layers than CT. Long-term application of mineral fertilisers significantly increased SOC and soil NPK contents primarily in top-soil. Enlarged fertilization determined the significantly higher leaching of nutrients to deeper soil layers (CT) and increased hazards of top-soil pollution. Straw returning significantly increased SOC and NPK accumulation in top-soil. The SR on sandy loam was significantly higher than on loam. At dry weather conditions, on loam SR was higher in NT than in CT, on sandy loam SR was higher in CT than in NT. NPK fertilizers promoted significantly higher SR in both dry and wet year, but suppressed SR on sandy loam during usual year. Not antropogenised soil had similar SOC and NPK distribution within 0-35 cm layer and depended on genesis of soil profile horizons.

Keywords: fertilizers, long-term experiments, soil texture, soil tillage, straw

Procedia PDF Downloads 299

Authors: Jinhoa Lee

Abstract:

The relationships between environmental quality, energy use and economic output have created growing attention over the past decades among researchers and policy makers. Focusing on the empirical aspects of the role of CO2 emissions and energy use in affecting the economic output, this paper is an effort to fulfill the gap in a comprehensive case study at a country level using modern econometric techniques. To achieve the goal, this country-specific study examines the short-run and long-run relationships among energy consumption (using disaggregated energy sources: crude oil, coal, natural gas, electricity), carbon dioxide (CO2) emissions and gross domestic product (GDP) for Turkey using time series analysis from the year 1980-2010. To investigate the relationships between the variables, this paper employs the Phillips–Perron (PP) test for stationarity, Johansen maximum likelihood method for cointegration and a Vector Error Correction Model (VECM) for both short- and long-run causality among the research variables for the sample. All the variables in this study show very strong significant effects on GDP in the country for the long term. The long-run equilibrium in the VECM suggests negative long-run causalities from consumption of petroleum products and the direct combustion of crude oil, coal and natural gas to GDP. Conversely, positive impacts of CO2 emissions and electricity consumption on GDP are found to be significant in Turkey during the period. There exists a short-run bidirectional relationship between electricity consumption and natural gas consumption. There exists a positive unidirectional causality running from electricity consumption to natural gas consumption, while there exists a negative unidirectional causality running from natural gas consumption to electricity consumption. Moreover, GDP has a negative effect on electricity consumption in Turkey in the short run. Overall, the results support arguments that there are relationships among environmental quality, energy use and economic output but the associations can to be differed by the sources of energy in the case of Turkey over of period 1980-2010.

Keywords: CO2 emissions, energy consumption, GDP, Turkey, time series analysis

Procedia PDF Downloads 508

Authors: Amir Shahab Shahabi, Mohsen Hasirian

Abstract:

Modern societies, marked by expansive Internet connectivity and the rise of e-commerce, are now integrated with digital platforms at an unprecedented level. The efficiency, speed, and accessibility of e-commerce have garnered a substantial consumer base. Against this backdrop, electronic banking has undergone rapid proliferation within the realm of online activities. However, this growth has inadvertently given rise to an environment conducive to illicit activities, notably electronic payment fraud, posing a formidable challenge to the domain of electronic banking. A pivotal role in upholding the integrity of electronic commerce and business transactions is played by electronic fraud detection, particularly in the context of credit cards which underscores the imperative of comprehensive research in this field. To this end, our study introduces an Attentional Bi-Stream Sequence Learner (AttBiSeL) framework that leverages attention mechanisms and recurrent networks. By incorporating bidirectional recurrent layers, specifically bidirectional Long Short-Term Memory (LSTM) and Gated Recurrent Unit (GRU) layers, the proposed model adeptly extracts past and future transaction sequences while accounting for the temporal flow of information in both directions. Moreover, the integration of an attention mechanism accentuates specific transactions to varying degrees, as manifested in the output of the recurrent networks. The effectiveness of the proposed approach in automatic credit card fraud classification is evaluated on the European Cardholders' Fraud Dataset. Empirical results validate that the hybrid architectural paradigm presented in this study yields enhanced accuracy compared to previous studies.

Keywords: credit card fraud, deep learning, attention mechanism, recurrent neural networks

Procedia PDF Downloads 13

Authors: Xuewen Yu, Danhui Dan

Abstract:

The identification of impact load and some hard-to-obtain system parameters is crucial for the activities of analysis, validation, and evaluation in the engineering field. This paper proposes a method that utilizes neural networks based on 1D-CNN to identify the impact load and partial system parameters from measured responses. To this end, forward computations are conducted to provide datasets consisting of the triples (parameter θ, input u, output y). Then neural networks are trained to learn the mapping from input to output, fu|{θ} : y → u, as well as from input and output to parameter, fθ : (u, y) → θ. Afterward, feeding the trained neural networks the measured output response, the input impact load and system parameter can be calculated, respectively. The method is tested on two simulated examples and shows sound accuracy in estimating the impact load (waveform and location) and system parameters.

Keywords: convolutional neural network, impact load identification, system parameter identification, inverse problem

Procedia PDF Downloads 123

Authors: Eman Hegazy, Stefan Anderberg, Joakim Krook

Abstract:

Urban water infrastructure systems are central to functioning cities. For securing a continuous and efficient supply of the systems services, continuous investment, maintenance, and renewal are needed. Neglecting maintenance and renewal can lead to recurrent breakdown problems as systems age, which makes it more and more difficult to secure efficient long-term supply. Globally, many cities struggle with aging water infrastructure, often due to competing funding priorities. Investment in maintenance and renewal is not prioritized. The problem primarily stems from the challenge of reaping the benefits of investments promptly. The long-term benefits gained from investing in the renewal of water infrastructure may be achievable in the long run, resulting in the oversight of such investments. This leads to a build-up of "renewal debt" for future generations to inherit. Addressing this issue is difficult due to various contributing factors and the complex nature of the systems. The study aims to contribute to an increased understanding of the long-term management challenges of urban water infrastructure, the development of improved maintenance and renewal strategies through the examination of water infrastructure management, and the assessment of the adequacy of the maintenance and renewal in a case study, the city of Linköping, Sweden. Employing a multi-methods approach, this study utilized both qualitative and quantitative methods, including interviews, workshops, and data analysis. The findings of the study provided insights into the current status of the water and sewerage networks in Linkoping, highlighting the risks to ensuring reliable and sustainable water supply and discussing strategies for improving maintenance and renewal.

Keywords: case study, infrastructure management, renewal needs, Sweden, urban water infrastructure

Procedia PDF Downloads 68

Authors: Ksheeraj Sai Vepuri, Nada Attar

Abstract:

We as humans use words with accompanying visual and facial cues to communicate effectively. Classifying facial emotion using computer vision methodologies has been an active research area in the computer vision field. In this paper, we propose a simple method for facial expression recognition that enhances accuracy. We tested our method on the FER-2013 dataset that contains static images. Instead of using Histogram equalization to preprocess the dataset, we used Unsharp Mask to emphasize texture and details and sharpened the edges. We also used ImageDataGenerator from Keras library for data augmentation. Then we used Convolutional Neural Networks (CNN) model to classify the images into 7 different facial expressions, yielding an accuracy of 69.46% on the test set. Our results show that using image preprocessing such as the sharpening technique for a CNN model can improve the performance, even when the CNN model is relatively simple.

Keywords: facial expression recognittion, image preprocessing, deep learning, CNN

Procedia PDF Downloads 143

Authors: Tansa Nisan Gunerhan

Abstract:

Dementia comes in different forms, including Alzheimer's disease. The most common dementia diagnosis among elderly individuals is Alzheimer's disease. On average, for patients with Alzheimer’s, life expectancy is around 4-8 years after the diagnosis; however, expectancy can go as high as twenty years or more, depending on the shrinkage of the brain. Normally, along with aging, the brain shrinks at some level but doesn’t lose a vast amount of neurons. However, Alzheimer's patients' neurons are destroyed rapidly; hence problems with loss of memory, communication, and other metabolic activities begin. The toxic changes in the brain affect the stability of the neurons. Beta-amyloid and tau are two proteins that are believed to play a role in the development of Alzheimer's disease through their toxic changes. Beta-amyloid is a protein that is produced in the brain and is normally broken down and removed from the body. However, in people with Alzheimer's disease, the production of beta-amyloid increases, and it begins to accumulate in the brain. These plaques are thought to disrupt communication between nerve cells and may contribute to the death of brain cells. Tau is a protein that helps to stabilize microtubules, which are essential for the transportation of nutrients and other substances within brain cells. In people with Alzheimer's disease, tau becomes abnormal and begins to accumulate inside brain cells, forming neurofibrillary tangles. These tangles disrupt the normal functioning of brain cells and may contribute to their death, forming amyloid plaques which are deposits of a protein called amyloid-beta that build up between nerve cells in the brain. The accumulation of amyloid plaques and neurofibrillary tangles in the brain is thought to contribute to the shrinkage of brain tissue. As the brain shrinks, the size of the brain may decrease, leading to a reduction in brain volume. Brain atrophy in Alzheimer's disease is often accompanied by changes in the structure and function of brain cells and the connections between them, leading to a decline in brain function. These toxic changes that accumulate can cause symptoms such as memory loss, difficulty with thinking and problem-solving, and changes in behavior and personality.

Keywords: Alzheimer, amyloid-beta, brain atrophy, neuron, shrinkage

Procedia PDF Downloads 95

Authors: Muhammed Ashiq Villanthenkodath, Shreya Pal

Abstract:

Purpose: This study scrutinizes the impact of economic globalization on ecological footprint while endogenizing economic growth and energy consumption from 1990 to 2018 in India. Design/methodology/approach: The standard unit root test has been employed for time series analysis to unveil the integration order. Then, the cointegration was confirmed using autoregressive distributed lag (ARDL) analysis. Further, the study executed the dynamic ARDL simulation model to estimate long-run and short-run results along with simulation and robotic prediction. Findings: The cointegration analysis confirms the existence of a long-run association among variables. Further, economic globalization reduces the ecological footprint in the long run. Similarly, energy consumption decreases the ecological footprint. In contrast, economic growth spurs the ecological footprint in India. Originality/value: This study contributes to the literature in many ways. First, unlike studies that employ CO2 emissions and globalization nexus, this study employs ecological footprint for measuring environmental quality; since it is the broader measure of environmental quality, it can offer a wide range of climate change mitigation policies for India. Second, the study executes a multivariate framework with updated series from 1990 to 2018 in India to explore the link between EF, economic globalization, energy consumption, and economic growth. Third, the dynamic autoregressive distributed lag (ARDL) model has been used to explore the short and long-run association between the series. Finally, to our limited knowledge, this is the first study that uses economic globalization in the EF function of India amid facing a trade-off between sustainable economic growth and the environment in the era of globalization.

Keywords: economic globalization, ecological footprint, India, dynamic ARDL simulation model

Procedia PDF Downloads 124

Authors: Okwan Frank

Abstract:

Maternal health outcome is one of the major population development challenges in Sub-Saharan Africa. The region has the highest maternal mortality ratio, despite the progressive economic growth in the region during the global economic crisis. It has been hypothesized that increase in economic growth will reduce the level of maternal mortality. The purpose of this study is to investigate the existence of the negative relationship between health outcome proxy by maternal mortality ratio and economic growth in Sub-Saharan Africa. The study used the Pooled Mean Group estimator of ARDL Autoregressive Distributed Lag (ARDL) and the Kao test for cointegration to examine the short-run and long-run relationship between maternal mortality and economic growth. The results of the cointegration test showed the existence of a long-run relationship between the variables considered for the study. The long-run result of the Pooled Mean group estimates confirmed the hypothesis of an inverse relationship between maternal health outcome proxy by maternal mortality ratio and economic growth proxy by Gross Domestic Product (GDP) per capita. Thus increasing economic growth by investing in the health care systems to reduce pregnancy and childbirth complications will help reduce maternal mortality in the sub-region.

Keywords: economic growth, maternal mortality, pool mean group, Sub-Saharan Africa

Procedia PDF Downloads 157

Authors: Bikis Muhammed, Sehra Sedigh Sarvestani, Ali R. Hurson, Lasanthi Gamage

Abstract:

Vehicular traffic events have overly complex spatial correlations and temporal interdependencies and are also influenced by environmental events such as weather conditions. To capture these spatial and temporal interdependencies and make more realistic vehicular traffic predictions, graph neural networks (GNN) based traffic prediction models have been extensively utilized due to their capability of capturing non-Euclidean spatial correlation very effectively. However, most of the already existing GNN-based traffic prediction models have some limitations during learning complex and dynamic spatial and temporal patterns due to the following missing factors. First, most GNN-based traffic prediction models have used static distance or sometimes haversine distance mechanisms between spatially separated traffic observations to estimate spatial correlation. Secondly, most GNN-based traffic prediction models have not incorporated environmental events that have a major impact on the normal traffic states. Finally, most of the GNN-based models did not use an attention mechanism to focus on only important traffic observations. The objective of this paper is to study and make real-time vehicular traffic predictions while incorporating the effect of weather conditions. To fill the previously mentioned gaps, our prediction model uses a real-time driving distance between sensors to build a distance matrix or spatial adjacency matrix and capture spatial correlation. In addition, our prediction model considers the effect of six types of weather conditions and has an attention mechanism in both spatial and temporal data aggregation. Our prediction model efficiently captures the spatial and temporal correlation between traffic events, and it relies on the graph attention network (GAT) and Bidirectional bidirectional long short-term memory (Bi-LSTM) plus attention layers and is called GAT-BILSTMA.

Keywords: deep learning, real time prediction, GAT, Bi-LSTM, attention

Procedia PDF Downloads 71

Authors: I. Martinez-Conesa, E. Garcia-Meca, M. Barradas-Quiroz

Abstract:

The objective of this study is to assess the impact on earnings management of latest two Hofstede cultural dimensions: long-term orientation and indulgence. Long-term orientation represents the alignment of a society towards the future and indulgence expresses the extent to which a society exhibits willingness, or restrain, to realise their impulses. Additionally, this paper tests if there are relevant differences by testing the moderating role of the legal tradition, Continental versus Anglo-Saxon. Our sample comprises 15 countries: Belgium, Canada, Germany, Spain, France, Great Britain, Hong Kong, India, Japan, Korea, Netherlands, Philippines, Portugal, Sweden, and Thailand, with a total of 12,936 observations from 2003 to 2013. Our results show that managers in countries with high levels of long-term orientation reduce their levels of discretionary accruals. The findings do not confirm the effect of indulgence on earnings management. In addition, our results confirm previous literature regarding the effect of individualism, noting that firms in countries with high levels of collectivism might be more inclined to use earnings discretion to protect the welfare of the collective group of firm stakeholders. Uncertainty avoidance results in downwards earnings management as well as high disclosure, suggesting that less manipulation takes place when transparency is higher. Indulgence is the cultural dimension that confronts wellbeing versus survival; dimension is formulated including happiness, the perception of live control and the importance of leisure. Indulgence shows a weak negative correlation with power distance indicating a slight tendency for more hierarchical societies to be less indulgent. Anglo-Saxon countries are a positive effect of individualism and a negative effect of masculinity, uncertainty avoidance, and disclosure. With respect to continental countries, we can see a significant and positive effect of individualism and a significant and negative effect of masculinity, long-term orientation, and indulgence. Therefore, we observe the negative effect on earnings management provoked by higher disclosure and uncertainty avoidance only happens in Anglo-Saxon countries. Meanwhile, the improvement in reporting quality motivated by higher long-term orientation and higher indulgence is dominant in Continental countries. Our results confirm that there is a moderating effect of the legal system in the association between culture and earnings management. This effect is especially relevant in the dimensions related to uncertainty avoidance, long term orientation, indulgence, and disclosure. The negative effect of long-term orientation on earnings management only happens in those countries set in continental legal systems because of the Anglo-Saxon legal systems is supported by the decisions of the courts and the traditions, so it already has long-term orientation. That does not occur in continental systems, depending mainly of contend of the law. Sensitivity analysis used with Jones modified CP model, Jones Standard model and Jones Standard CP model confirm the robustness of these results. This paper collaborates towards a better understanding on how earnings management, culture and legal systems relate to each other, and contribute to previous literature by examining the influence of the two latest Hofstede’s dimensions not previously studied in papers.

Keywords: Hofstede, long-term-orientation, earnings management, indulgence

Procedia PDF Downloads 239