Search results for: temporal neural sequences

Authors: Wanchalerm Patanacharoenwong, Panaya Sudta, Prachya Bumrungkun

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The paper focuses on the development of a system that combines Internet of Things (IoT) technologies and deep learning algorithms for anomaly detection in residential Electric Vehicle (EV) charging in smart cities. With the increasing number of EVs, ensuring efficient and reliable charging systems has become crucial. The aim of this research is to develop an integrated IoT and deep learning system for detecting anomalies in residential EV charging and enhancing EV load profiling and event detection in smart cities. This approach utilizes IoT devices equipped with infrared cameras to collect thermal images and household EV charging profiles from the database of Thailand utility, subsequently transmitting this data to a cloud database for comprehensive analysis. The methodology includes the use of advanced deep learning techniques such as Recurrent Neural Networks (RNN) and Long Short-Term Memory (LSTM) algorithms. IoT devices equipped with infrared cameras are used to collect thermal images and EV charging profiles. The data is transmitted to a cloud database for comprehensive analysis. The researchers also utilize feature-based Gaussian mixture models for EV load profiling and event detection. Moreover, the research findings demonstrate the effectiveness of the developed system in detecting anomalies and critical profiles in EV charging behavior. The system provides timely alarms to users regarding potential issues and categorizes the severity of detected problems based on a health index for each charging device. The system also outperforms existing models in event detection accuracy. This research contributes to the field by showcasing the potential of integrating IoT and deep learning techniques in managing residential EV charging in smart cities. The system ensures operational safety and efficiency while also promoting sustainable energy management. The data is collected using IoT devices equipped with infrared cameras and is stored in a cloud database for analysis. The collected data is then analyzed using RNN, LSTM, and feature-based Gaussian mixture models. The approach includes both EV load profiling and event detection, utilizing a feature-based Gaussian mixture model. This comprehensive method aids in identifying unique power consumption patterns among EV owners and outperforms existing models in event detection accuracy. In summary, the research concludes that integrating IoT and deep learning techniques can effectively detect anomalies in residential EV charging and enhance EV load profiling and event detection accuracy. The developed system ensures operational safety and efficiency, contributing to sustainable energy management in smart cities.

Keywords: cloud computing framework, recurrent neural networks, long short-term memory, Iot, EV charging, smart grids

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Authors: Ankit Shah, R. K. Shrivastava

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Flood and drought are two main features of hydrology which affect the human life. Floods are natural disasters which cause millions of rupees’ worth of damage each year in India and the whole world. Flood causes destruction in form of life and property. An accurate estimate of the flood damage potential is a key element to an effective, nationwide flood damage abatement program. Also, the increase in demand of water due to increase in population, industrial and agricultural growth, has let us know that though being a renewable resource it cannot be taken for granted. We have to optimize the use of water according to circumstances and conditions and need to harness it which can be done by construction of hydraulic structures. For their safe and proper functioning of hydraulic structures, we need to predict the flood magnitude and its impact. Hydraulic structures play a key role in harnessing and optimization of flood water which in turn results in safe and maximum use of water available. Mainly hydraulic structures are constructed on ungauged sites. There are two methods by which we can estimate flood viz. generation of Unit Hydrographs and Flood Frequency Analysis. In this study, Regional Flood Frequency Analysis has been employed. There are many methods for estimating the ‘Regional Flood Frequency Analysis’ viz. Index Flood Method. National Environmental and Research Council (NERC Methods), Multiple Regression Method, etc. However, none of the methods can be considered universal for every situation and location. The Narmada basin is located in Central India. It is drained by most of the tributaries, most of which are ungauged. Therefore it is very difficult to estimate flood on these tributaries and in the main river. As mentioned above Artificial Neural Network (ANN)s and Multiple Regression Method is used for determination of Regional flood Frequency. The annual peak flood data of 20 sites gauging sites of Narmada Basin is used in the present study to determine the Regional Flood relationships. Homogeneity of the considered sites is determined by using the Index Flood Method. Flood relationships obtained by both the methods are compared with each other, and it is found that ANN is more reliable than Multiple Regression Method for the present study area.

Keywords: artificial neural network, index flood method, multi layer perceptrons, multiple regression, Narmada basin, regional flood frequency

Procedia PDF Downloads 390

Authors: Daniel Suarez Alonso

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This study has tried to focus on the alternatives offered to police institutions by the implementation of Geographic Information systems. Providing operational police commanders with effective and efficient tools, providing analytical capacity to reduce criminal opportunities, must be a priority. Given the intimate connection of crimes and infractions to the environment, law enforcement institutions must respond proactively to changing circumstances of anti-norm behaviors. To this end, it has been intended to analyze the antisocial spatial distribution of the city of Móstoles, trying to identify those spatiotemporal patterns that occur to anticipate their commission through the planning of dynamic preventive strategies. The application of GIS offers alternative analytical approaches to the different problems that underlie the development of life in society, focusing resources on those places with the highest concentration of incidents.

Keywords: data analysis, police organizations, police prevention, geographic information systems

Procedia PDF Downloads 23

Authors: Abdulmalek Ahmed

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Formation pressure is the main function that affects drilling operation economically and efficiently. Knowing the pore pressure and the parameters that affect it will help to reduce the cost of drilling process. Many empirical models reported in the literature were used to calculate the formation pressure based on different parameters. Some of these models used only drilling parameters to estimate pore pressure. Other models predicted the formation pressure based on log data. All of these models required different trends such as normal or abnormal to predict the pore pressure. Few researchers applied artificial intelligence (AI) techniques to predict the formation pressure by only one method or a maximum of two methods of AI. The objective of this research is to predict the pore pressure based on both drilling parameters and log data namely; weight on bit, rotary speed, rate of penetration, mud weight, bulk density, porosity and delta sonic time. A real field data is used to predict the formation pressure using five different artificial intelligence (AI) methods such as; artificial neural networks (ANN), radial basis function (RBF), fuzzy logic (FL), support vector machine (SVM) and functional networks (FN). All AI tools were compared with different empirical models. AI methods estimated the formation pressure by a high accuracy (high correlation coefficient and low average absolute percentage error) and outperformed all previous. The advantage of the new technique is its simplicity, which represented from its estimation of pore pressure without the need of different trends as compared to other models which require a two different trend (normal or abnormal pressure). Moreover, by comparing the AI tools with each other, the results indicate that SVM has the advantage of pore pressure prediction by its fast processing speed and high performance (a high correlation coefficient of 0.997 and a low average absolute percentage error of 0.14%). In the end, a new empirical correlation for formation pressure was developed using ANN method that can estimate pore pressure with a high precision (correlation coefficient of 0.998 and average absolute percentage error of 0.17%).

Keywords: Artificial Intelligence (AI), Formation pressure, Artificial Neural Networks (ANN), Fuzzy Logic (FL), Support Vector Machine (SVM), Functional Networks (FN), Radial Basis Function (RBF)

Procedia PDF Downloads 128

Authors: Ali A. Aldosari

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Change detection of Earth surface features is extremely important for better understanding of our environment in order to promote better decision making. Al-Jawf is remarkable for its abundant agricultural water where there is fertile agricultural land due largely to underground water. As result, this region has large areas of cultivation of dates, olives and fruits trees as well as other agricultural products such as Alfa Alfa and wheat. However this agricultural area was declined due to the reduction of government supports in the last decade. This reduction was not officially recorded or measured in this region at large scale or governorate level. Remote sensing data are primary sources extensively used for change detection in agriculture applications. This study is applied the technology of GIS and used the Normalized Difference Vegetation Index (NDVI) which can be used to measure and analyze the spatial and temporal changes in the agriculture areas in the Aljouf region.

Keywords: spatial analysis, geographical information system, change detection

Procedia PDF Downloads 377

Authors: Ozhan Simsek, Dicle Donmez, Burhanettin Imrak, Ahsen Isik Ozguven, Yildiz Aka Kacar

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Pomegranate (Punica granatum L.) is known to be one of the oldest edible fruit tree species, with a wide geographical global distribution. Fruits from the two defined varieties (Hicaznar and 33N26) were taken at intervals after pollination and fertilization at different sizes. Seed samples were used for transcriptome sequencing. Primary sequencing was produced by Illumina Hi-Seq™ 2000. Firstly, we had raw reads, and it was subjected to quality control (QC). Raw reads were filtered into clean reads and aligned to the reference sequences. De novo analysis was performed to detect genes expressed in seeds of pomegranate varieties. We performed downstream analysis to determine differentially expressed genes. We generated about 27.09 gb bases in total after Illumina Hi-Seq sequencing. All samples were assembled together, we got 59,264 Unigenes, the total length, average length, N50, and GC content of Unigenes are 84.547.276 bp, 1.426 bp, 2,137 bp, and 46.20 %, respectively. Unigenes were annotated with 7 functional databases, finally, 42.681(NR: 72.02%), 39.660 (NT: 66.92%), 30.790 (Swissprot: 51.95%), 20.212 (COG: 34.11%), 27.689 (KEGG: 46.72%), 12.328 (GO: 20.80%), and 33,833 (Interpro: 57.09%) Unigenes were annotated. With functional annotation results, we detected 42.376 CDS, and 4.999 SSR distribute on 16.143 Unigenes.

Keywords: next generation sequencing, SSR, RNA-Seq, Illumina

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Authors: Rachid Essamoud, Abdelkrim Afenzar, Ahmed Belqadi

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During the Early Mesozoic, the northwestern part of the African continent was affected by initial fracturing associated with the early stages of the opening of the Central Atlantic (Atlantic Rift). During this rifting phase, the Moroccan Meseta experienced an extensive tectonic regime. This extension favored the formation of a set of rift-type basins, including the Mohammedia-Benslimane-ElGara-Berrechid basin. Thus, it is essential to know the nature of the deposits in this basin and their evolution over time as well as their relationship with the basaltic effusion of the Central Atlantic Magmatic Province (CAMP). These deposits are subdivided into two large series: The Lower clay-salt series attributed to the Triassic and the Upper clay-salt series attributed to the Liassic. The two series are separated by the Upper Triassic-Lower Liassic basaltic complex. The detailed sedimentological analysis made it possible to characterize four mega-sequences, fifteen types of facies and eight architectural elements and facies associations in the Triassic series. A progressive decrease observed in paleo-slope over time led to the evolution of the paleoenvironment from a proximal system of alluvial fans to a braided fluvial style, then to an anastomosed system. These environments eventually evolved into an alluvial plain associated with a coastal plain where playa lakes, mudflats and lagoons had developed. The pure and massive halitic facies at the top of the series probably indicate an evolution of the depositional environment towards a shallow subtidal environment. The presence of these evaporites indicates a climate that favored their precipitation, in this case, a fairly hot and humid climate. The sedimentological analysis of the supra-basaltic part shows that during the Lower Liassic, the paleopente after basaltic effusion remained weak with distal environments. The faciological analysis revealed the presence of four major sandstone, silty, clayey and evaporitic lithofacies organized in two mega-sequences: the sedimentation of the first rock-salt mega-sequence took place in a brine depression system free, followed by saline mudflats under continental influences. The upper clay mega-sequence displays facies documenting sea level fluctuations from the final transgression of the Tethys or the opening Atlantic. Saliferous sedimentation is therefore favored from the Upper Triassic, but experienced a sudden rupture by the emission of basaltic flows which are interstratified in the azoic salt clays of very shallow seas. This basaltic emission which belongs to the CAMP would come from a fissural volcanism probably carried out through transfer faults located in the NW and SE of the basin. Their emplacement is probably subaquatic to subaerial. From a chronological and paleogeographic point of view, this main volcanism, dated between the Upper Triassic and the Lower Liassic (180-200 MA), is linked to the fragmentation of Pangea and managed by a progressive expansion triggered in the West in close relation with the initial phases of Central Atlantic rifting and seems to coincide with the major mass extinction at the Triassic-Jurassic boundary.

Keywords: Basalt, CAMP, Liassic, sedimentology, Triassic, Morocco

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Authors: Carolina Zambrana, Grover Zurita

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The food loss production that occurs in deficient agricultural production is one of the major problems worldwide. This puts the population's food security and the efficiency of farming investments at risk. It is to be expected that this food security will be achieved with the own and efficient production of each country. It will have an impact on the well-being of its population and, thus, also on food sovereignty. The production losses in quantity and quality occur due to the lack of efficient detection of diseases at an early stage. It is very difficult to solve the agriculture efficiency using traditional methods since it takes a long time to be carried out due to detection imprecision of the main diseases, especially when the production areas are extensive. Therefore, the main objective of this research study is to perform a systematic literature review, of the latest five years, of Precision Agriculture (PA) to be able to understand the state of the art of the set of new technologies, procedures, and optimization processes with Artificial Intelligence (AI). This study will focus on Corns, Potatoes, and Rice diagnostic diseases. The extensive literature review will be performed on Elsevier, Scopus, and IEEE databases. In addition, this research will focus on advanced digital imaging processing and the development of software and hardware for PA. The convolution neural network will be handling special attention due to its outstanding diagnostic results. Moreover, the studied data will be incorporated with artificial intelligence algorithms for the automatic diagnosis of crop quality. Finally, precision agriculture with technology applied to the agricultural sector allows the land to be exploited efficiently. This system requires sensors, drones, data acquisition cards, and global positioning systems. This research seeks to merge different areas of science, control engineering, electronics, digital image processing, and artificial intelligence for the development, in the near future, of a low-cost image measurement system that allows the optimization of crops with AI.

Keywords: precision agriculture, convolutional neural network, deep learning, artificial intelligence

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Authors: Tasnim Kallel, Rim Taktak

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In this experimental work, fiber reinforced polymer (FRP) composite laminates were manufactured using hand lay-up technique. The unsaturated polyester (UP) and vinylester (VE) were considered as resins reinforced with different woven fabrics (bidirectional and quadriaxial rovings). The mechanical behaviour of the resulting composites was studied and then compared. A focus was essentially done on the evaluation of the effect of E-Glass fiber and ply orientation on the mechanical properties such as tensile strength, flexural strength, and hardness of the studied composite laminates. Also, crack paths and fracture surfaces were examined, and failure mechanisms were analyzed. From the main results, it was found that the quadriaxial composite laminates (QA/VE and QA/UP) with stacking sequences of [0°, +45°, 90°, -45°] present a very ductile tensile behaviour. The other laminate samples (R500/VE, RM/VE, R500/UP and RM/UP) show a very brittle behaviour whatever the used resin. The intrinsic toughness KIC of QA/VE laminate, obtained in fracture tests, are found more important than that of RM/VE composite. Thus, the QA/VE samples, as multidirectional laminate, presents the highest interlaminar fracture resistance.

Keywords: crack growth, fiber orientation, fracture behavior, e-glass fiber fabric, laminate composite, mechanical behavior

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Authors: Taekyun Doo, Cheongmin Ji, Manpyo Hong

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Like a closed-circuit television (CCTV), video surveillance system is widely placed for gathering video from unspecified people to prevent crime, surveillance, or many other purposes. However, abuse of CCTV brings about concerns of personal privacy invasions. In this paper, we propose an encryption method to protect personal privacy system in H.264 compressed video bitstream with encrypting only regions of interest (ROI). There is no need to change the existing video surveillance system. In addition, encrypting ROI in compressed video bitstream is a challenging work due to spatial and temporal drift errors. For this reason, we propose a novel drift mitigation method when ROI is encrypted. The proposed method was implemented by using JM reference software based on the H.264 compressed videos, and experimental results show the verification of our proposed methods and its effectiveness.

Keywords: H.264/AVC, video encryption, privacy protection, post compression, region of interest

Procedia PDF Downloads 319

Authors: Mahmoud Mabrok, Channarong Rodkhum

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Flavobacterium columanre is one of the devastating pathogen that causes noticeable economic losses in freshwater cultured fish. Like other filamentous bacteria, F. columanre tends to aggregate and fluctuate to all kind of media, thus revealing obstacles in recognition of its colonies. Since the molecular typing is the only fundamental tool for rapid and precise detection of this pathgen. The present study developed a species-specific PCR assay based on cslA unique gene of F. columnare. The cslA gene sequences of 13 F. columnare, strains retrieved from gene bank database, were aligned to identify a conserved homologous segment prior to primers design. The new primers yielded amplicons of 287 bp from F. columnare strains but not from relevant or other pathogens, unlike to other published set that showed no specificity and cross-reactivity with F. indicum. The primers were sensitive and detected as few as 7 CFUs of bacteria and 3 pg of gDNA template. The sensitivity was reduced ten times when using tissue samples. These primers precisely defined all field isolates in a double-blind study, proposing their applicable use for field detection.

Keywords: Columnaris infection, cslA gene, Flavobacterium columnare, PCR

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Authors: Takahiro Nishigaki, Katsumi Nitta, Takashi Onoda

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In this paper, we present a method of applying Independent Topic Analysis (ITA) to increasing the number of document data. The number of document data has been increasing since the spread of the Internet. ITA was presented as one method to analyze the document data. ITA is a method for extracting the independent topics from the document data by using the Independent Component Analysis (ICA). ICA is a technique in the signal processing; however, it is difficult to apply the ITA to increasing number of document data. Because ITA must use the all document data so temporal and spatial cost is very high. Therefore, we present Incremental ITA which extracts the independent topics from increasing number of document data. Incremental ITA is a method of updating the independent topics when the document data is added after extracted the independent topics from a just previous the data. In addition, Incremental ITA updates the independent topics when the document data is added. And we show the result applied Incremental ITA to benchmark datasets.

Keywords: text mining, topic extraction, independent, incremental, independent component analysis

Procedia PDF Downloads 281

Authors: Ayesha Sohail, Khadija Maqbool, Anila Asif, Haroon Ahmad

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The field of tissue engineering is an active area of research. Bone tissue engineering helps to resolve the clinical problems of critical size and non-healing defects by the creation of man-made bone tissue. We will design and validate an efficient numerical model, which will simulate the effective diffusivity in bone tissue engineering. Our numerical model will be based on the finite element analysis of the diffusion-reaction equations. It will have the ability to optimize the diffusivity, even at multi-scale, with the variation of time. It will also have a special feature, with which we will not only be able to predict the oxygen, glucose and cell density dynamics, more accurately, but will also sort the issues arising due to anisotropy. We will fix these problems with the help of modifying the governing equations, by selecting appropriate spatio-temporal finite element schemes, by adaptive grid refinement strategy and by transient analysis.

Keywords: scaffolds, porosity, diffusion, transient analysis

Procedia PDF Downloads 512

Authors: Zhenfeng Xie

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Acute gastroenteritis (AGE) is a common cause of morbidity and mortality in infants and young children worldwide, especially in developing countries. Enterovirus(EVs) have been identified in patients with AGE in many countries around the world, and some studies have revealed that EV infection is associated with gastrointestinal symptoms and plays a role in AGE. As a potential causative pathogen of AGE in humans, continuous detection and identification of EVs in pediatric patients with AGE is needed. In this study, we aimed to investigate the prevalence, seasonal distribution, and molecular characteristics of EVs circulating in pediatric patients with AGE in Thailand from 2019 to 2022. A total of 1422 stool specimens were collected for this study. RT-PCR amplification of the 5'UTR was used to screen for EV positive samples. EV genotyping was determined based on nucleotide sequence and phylogenetic analysis of the VP1 sequences. EV prevalence in pediatric AGE patients was 8.3% (118 out of 1,422). Among these, 35.6% of EV infection cases were caused by species A, followed by species C and B (33.1% and 30.5%, respectively). A total of 26 EV genotypes were identified in this study. Poliovirus 3 and coxsackievirus A2 were the predominant genotypes detected(14% and 13%, respectively). EV was detected all year round with higher prevalence between July and December. In summary, this study reports EV's prevalence and genotype diversity in pediatric patients with AGE in Thailand during 2019-2022.

Keywords: enterovirus, epidemiology, acute gastroenteritis, genotype

Procedia PDF Downloads 49

Authors: Sabina-Adriana Floria, Marius Gavrilescu, Florin Leon, Silvia Curteanu, Costel Anton

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Neuroevolution is a subfield of artificial intelligence used to solve various problems in different application areas. Specifically, neuroevolution is a technique that applies biologically inspired methods to generate neural network architectures and optimize their parameters automatically. In this paper, we use different biologically inspired optimization algorithms in an ensemble strategy with the aim of training multilayer perceptron neural networks, resulting in regression models used to simulate the industrial chemical process of obtaining bricks from silicone-based materials. Installations in the raw ceramics industry, i.e., bricks, are characterized by significant energy consumption and large quantities of emissions. In addition, the initial conditions that were taken into account during the design and commissioning of the installation can change over time, which leads to the need to add new mixes to adjust the operating conditions for the desired purpose, e.g., material properties and energy saving. The present approach follows the study by simulation of a process of obtaining bricks from silicone-based materials, i.e., the modeling and optimization of the process. Optimization aims to determine the working conditions that minimize the emissions represented by nitrogen monoxide. We first use a search procedure to find the best values for the parameters of various biologically inspired optimization algorithms. Then, we propose an adaptive ensemble strategy that uses only a subset of the best algorithms identified in the search stage. The adaptive ensemble strategy combines the results of selected algorithms and automatically assigns more processing capacity to the more efficient algorithms. Their efficiency may also vary at different stages of the optimization process. In a given ensemble iteration, the most efficient algorithms aim to maintain good convergence, while the less efficient algorithms can improve population diversity. The proposed adaptive ensemble strategy outperforms the individual optimizers and the non-adaptive ensemble strategy in convergence speed, and the obtained results provide lower error values.

Keywords: optimization, biologically inspired algorithm, neuroevolution, ensembles, bricks, emission minimization

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Authors: Ibrahim Ilker Ozyigit, Ertugrul Filiz, Ilhan Dogan

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An in silico analysis of Brachypodium distachyon, Triticum aestivum, Festuca arundinacea, Lolium perenne, Hordeum vulgare subsp. vulgare of the Pooideaea was performed based on complete chloroplast genomes including rbcL coding and trnH-psbA intergenic spacer regions alone to compare phylogenetic resolving power. Neighbor-joining, Minimum Evolution, and Unweighted Pair Group Method with arithmetic mean methods were used to reconstruct phylogenies with the highest bootstrap supported the obtained data from whole chloroplast genome sequence. The highest and lowest values from nucleotide diversity (π) analysis were found to be 0.315813 and 0.043495 in rbcL coding region in chloroplast genome and complete chloroplast genome, respectively. The highest transition/transversion bias (R) value was recorded as 1.384 in complete chloroplast genomes. F. arudinacea-L. perenne clade was uncovered in all phylogenies. Sequences of rbcL and trnH-psbA regions were not able to resolve the Pooideae phylogenies due to lack of genetic variation.

Keywords: chloroplast DNA, Pooideae, phylogenetic analysis, rbcL, trnH-psbA

Procedia PDF Downloads 353

Authors: Syed Romat Ali Shah, Bilal Shoaib, Saleem Akhtar, Munib Ahmad, Shahan Sadiqui

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Classification is a data mining technique, which is recognizedon Machine Learning (ML) algorithm. It is used to classifythe individual articlein a knownofinformation into a set of predefinemodules or group. Web mining is also a portion of that sympathetic of data mining methods. The main purpose of this paper to analysis and compare the performance of Naïve Bayse Algorithm, Decision Tree, K-Nearest Neighbor (KNN), Artificial Neural Network (ANN)and Support Vector Machine (SVM). This paper consists of different ML algorithm and their advantages and disadvantages and also define research issues.

Keywords: Data Mining, Web Mining, classification, ML Algorithms

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Authors: Houria Hamitouche, Abdellah Khelifa

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Quaternary ammonium salts (QACs) are the most common cationic surfactants of natural or synthetic origin usually. They possess one or more hydrophobic hydrocarbon chains and hydrophilic cationic group. In fact, the hydrophobic groups are derived from three main sources: petrochemicals, vegetable oils, and animal fats. These QACs have attracted the attention of chemists for a long time, due to their general simple synthesis and their broad application in several fields. They are important as ingredients of cosmetic products and are also used as corrosion inhibitors, in emulsion polymerization and textile processing. Within biological applications, QACs show a good antimicrobial activity and can be used as medicines, gene delivery agents or in DNA extraction methods. The 2004 worldwide annual consumption of QACs was reported as 500,000 tons. The petroleum product is considered a true reservoir of a variety of chemical species, which can be used in the synthesis of quaternary ammonium salts. The purpose of the present contribution is to synthesize the quaternary ammonium salts by Menschutkin reaction, via chloromethylation/quaternization sequences, from Algerian petroleum products namely: reformate, light naphtha and kerosene and characterize.

Keywords: quaternary ammonium salts, reformate, light naphtha, kerosene

Procedia PDF Downloads 310

Authors: Akm Badrul Alam, Yoshiaki Fujii, Nahid Hasan Dipu, Shakil Ahmed Razo

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Shale rockmasses often include silt layers, impacting slope stability in construction and mining. Analyzing their interaction is crucial for long-term stability. A study used an elastoplastic model, incorporating the stress transfer method and Coulomb's criterion, to assess a shale rock mass with silt layers. It computed stress distribution, assessed failure potential, and identified vulnerable regions where nodal forces were calculated for a comprehensive analysis. A shale rock mass ranging from 14.75 to 16.75 meters thick, with silt layers varying from 0.36 to 0.5 meters, was considered in the model. It examined four silt layer conditions: horizontal (SiHL), vertical (SiVL), inclined against slope (SiIincAGS), and along slope (SilincALO). Mechanical parameters like uniaxial compressive strength (UCS), tensile strength (TS), Young’s modulus (E), Poisson’s ratio, and density were adjusted for varied scenarios: UCS (0.5 to 5 MPa), TS (0.1 to 1 MPa), and E (6 to 60 MPa). In elastic analysis of shale rock masses, stress distributions vary based on layer properties. When shale and silt layers have the same elasticity modulus (E), stress concentrates at corners. If the silt layer has a lower E than shale, marginal changes in maximum stress (σmax) occur for SilHL. A decrease in σmax is evident at SilVL. Slight variations in σmax are observed for SilincAGS and SilincALO. In the elastoplastic analysis, the overall decrease of 20%, 40%, 60%, 80%, and 90% was considered. For SilHL:(i) Same E, UCS, and TS for silt layer and shale, UCS/TS ratio 5: strength decrease led to shear (S), tension then shear (T then S) failure; noticeable failure at 60% decrease, significant at 80%, collapse at 90%. (ii) Lower E for silt layer, same strength as shale: No significant differences. (iii) Lower E and UCS, silt layer strength 1/10: No significant differences. For SilVL: (i) Same E, UCS, and TS for silt layer and shale, UCS/TS ratio 5: Similar effects as SilHL. (ii) Lower E for silt layer, same strength as shale: Slip occurred. (iii) Lower E and UCS, silt layer strength 1/10: Bitension failure also observed with larger slip. For SilincAGS: (i) Same E, UCS, and TS for silt layer and shale, UCS/TS ratio 5: Effects similar to SilHL. (ii) Lower E for silt layer, same strength as shale: Slip occurred. (iii) Lower E and UCS, silt layer strength 1/10: Tension failure also observed with larger slip. For SilincALO: (i) Same E, UCS, and TS for silt layer and shale, UCS/TS ratio 5: Similar to SilHL with tension failure. (ii) Lower E for silt layer, same strength as shale: No significant differences; failure diverged. (iii) Lower E and UCS, silt layer strength 1/10: Bitension failure also observed with larger slip; failure diverged. Toppling failure was observed for lower E cases of SilVL and SilincAGS. The presence of silt interlayers in shale greatly impacts slope stability. Designing slopes requires careful consideration of both the silt and shale's mechanical properties. The temporal degradation of strength in these layers is a major concern. Thus, slope design must comprehensively analyze the immediate and long-term mechanical behavior of interlayer silt and shale to effectively mitigate instability.

Keywords: shale rock masses, silt layers, slope stability, elasto-plastic model, temporal degradation

Procedia PDF Downloads 34

Authors: Aysun Sezer

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Magnetic Resonance Imaging (MRI) is one of the advanced diagnostic tools for evaluating shoulder pathologies. Proton Density (PD)-weighted MRI sequences prove highly effective in detecting edema. However, they are deficient in the anatomical identification of bones due to a trauma-induced decrease in signal-to-noise ratio and blur in the traumatized cortices. Computer-based diagnostic systems require precise segmentation, identification, and localization of anatomical regions in medical imagery. Deep learning-based object detection algorithms exhibit remarkable proficiency in real-time object identification and localization. In this study, the YOLOv8 model was employed to detect humeral head and scapular regions in 665 axial PD-weighted MR images. The YOLOv8 configuration achieved an overall success rate of 99.60% and 89.90% for detecting the humeral head and scapula, respectively, with an intersection over union (IoU) of 0.5. Our findings indicate a significant promise of employing YOLOv8-based detection for the humerus and scapula regions, particularly in the context of PD-weighted images affected by both noise and intensity inhomogeneity.

Keywords: YOLOv8, object detection, humerus, scapula, IRM

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Authors: Marie Pavelková, Hana Lukášová

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Pupils´ inquisitiveness at the beginning of their school attendance is reflected by characteristics of the questions they ask. Clearly most of the classroom communication sequences are initiated by the teacher. But the teaching process also includes questions initiated by pupils in the need to satisfy their need for knowledge. The purpose of our research is to present the results of our pre-research strategy of occurrence of pupil-initiated questions in math lessons at the lower elementary school level, and to reveal the extent to which they are influenced by the teacher´s teaching strategy. We used the research methods of direct and indirect observations of fifth year classes in primary school. We focused on questions asked by the pupils in their math lessons. Our research sample for the pre-research observation method was a collection of video recordings available online. We used them for analysing the nature of pupils´ questions identified there. On the basis of the analysis, we hereby present the results concerning the nature of pupils´ questions asked in math lessons on the lower elementary school level. The interpretation of the collected results will be the starting point for the selection of research strategies in the next research stages concerning pupils’ questions in the future.

Keywords: beginning of schooling, pre-research, questions of pupils, teaching strategy

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Authors: Sankaran Rajendran

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Remote sensing plays a vital role in mapping of resources and monitoring of environments of the earth. In the present research study, mapping and monitoring of clay siltations occurred in the Alkhod Dam of Muscat, Sultanate of Oman are carried out using low-cost multispectral Landsat and ASTER data. The dam is constructed across the Wadi Samail catchment for ground water recharge. The occurrence and spatial distribution of siltations in the dam are studied with five years of interval from the year 1987 of construction to 2014. The deposits are mainly due to the clay, sand, and silt occurrences derived from the weathering rocks of ophiolite sequences occurred in the Wadi Samail catchment. The occurrences of clays are confirmed by minerals identification using ASTER VNIR-SWIR spectral bands and Spectral Angle Mapper supervised image processing method. The presence of clays and their spatial distribution are verified in the field. The study recommends the technique and the low-cost satellite data to similar region of the world.

Keywords: Alkhod Dam, ASTER siltation, Landsat, remote sensing, Oman

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Authors: U. Thisara G. Perera, C. M. Kanchana N. K. Chandrasekara

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Dengue is an epidemic which is spread by Aedes Egyptai and Aedes Albopictus mosquitoes. The cases of dengue show a dramatic growth rate of the epidemic in urban and semi urban areas spatially in tropical and sub-tropical regions of the world. Incidence of dengue has become a prominent reason for hospitalization and deaths in Asian countries, including Sri Lanka. During the last decade the dengue epidemic began to spread from urban to semi-urban and then to rural settings of the country. The highest number of dengue infected patients was recorded in Sri Lanka in the year 2016 and the highest number of patients was identified in Colombo district. Together with the commercial, industrial, and other supporting services, the district suffers from rapid urbanization and high population density. Thus, drainage and waste disposal patterns of the people in this area exert an additional pressure to the environment. The district is situated in the wet zone and thus low lying lands constitute the largest portion of the district. This situation additionally facilitates mosquito breeding sites. Therefore, the purpose of the present study was to assess the spatial and temporal distribution patterns of dengue epidemic in Kolonnawa MOH area (Medical Officer of Health) in the district of Colombo. The study was carried out using 615 recorded dengue cases in Kollonnawa MOH area during the south east monsoon season from May to September 2016. The Moran’s I and Kernel density estimation were used as analytical methods. The analysis of data was accomplished through the integrated use of ArcGIS 10.1 software packages along with Microsoft Excel analytical tool. Field observation was also carried out for verification purposes during the study period. Results of the Moran’s I index indicates that the spatial distribution of dengue cases showed a cluster distribution pattern across the area. Kernel density estimation emphasis that dengue cases are high where the population has gathered, especially in areas comprising housing schemes. Results of the Kernel Density estimation further discloses that hot spots of dengue epidemic are located in the western half of the Kolonnawa MOH area, which is close to the Colombo municipal boundary and there is a significant relationship with high population density and unplanned urban land use practices. Results of the field observation confirm that the drainage systems in these areas function poorly and careless waste disposal methods of the people further encourage mosquito breeding sites. This situation has evolved harmfully from a public health issue to a social problem, which ultimately impacts on the economy and social lives of the country.

Keywords: Dengue epidemic, health hazards, Kernel density, Moran’s I, Sri Lanka

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Authors: Gizem Arslan Özgen, Kutay Yücetürk, Metin Tanoğlu, Engin Aktaş

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Power transmitted from the engine to the final drive where useful work is applied through a system consisting of a gearbox, clutch, drive shaft and a differential in the rear-wheel-drive automobiles. It is well-known that the steel drive shaft is usually manufactured in two pieces to increase the fundamental bending natural frequency to ensure safe operation conditions. In this work, hybrid one-piece propeller shafts composed of carbon/epoxy and glass/epoxy composites have been designed for a rear wheel drive automobile satisfying three design specifications, such as static torque transmission capability, torsional buckling and the fundamental natural bending frequency. Hybridization of carbon and glass fibers is being studied to optimize the cost/performance requirements. Composites shaft materials with various fiber orientation angles and stacking sequences are being fabricated and analyzed using finite element analysis (FEA).

Keywords: composite propeller shaft, hybridization, epoxy matrix, static torque transmission capability, torsional buckling strength, fundamental natural bending frequency.

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Authors: Vanessa H. S. Zago, Ana Maria H. de Avila, Paula P. Costa, Welington Corozolla, Liriam S. Teixeira, Eliana C. de Faria

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Objectives: The outcome of cardiovascular disease is affected by environment and climate. This study evaluated the possible relationships between climatic and environmental changes and the occurrence of biological rhythms in serum lipids and lipoproteins in a large population sample in the city of Campinas, State of Sao Paulo, Brazil. In addition, it determined the temporal variations of death due to atherosclerotic events in Campinas during the time window examined. Methods: A large 8-year retrospective study was carried out to evaluate the lipid profiles of individuals attended at the University of Campinas (Unicamp). The study population comprised 27.543 individuals of both sexes and of all ages. Normolipidemic and dyslipidemic individuals classified according to Brazilian guidelines on dyslipidemias, participated in the study. For the same period, the temperature, relative humidity and daily brightness records were obtained from the Centro de Pesquisas Meteorologicas e Climaticas Aplicadas a Agricultura/Unicamp and frequencies of death due to atherosclerotic events in Campinas were acquired from the Brazilian official database DATASUS, according to the International Classification of Diseases. Statistical analyses were performed using both Cosinor and ARIMA temporal analysis methods. For cross-correlation analysis between climatic and lipid parameters, cross-correlation functions were used. Results: Preliminary results indicated that rhythmicity was significant for LDL-C and HDL-C in the cases of both normolipidemic and dyslipidemic subjects (n =respectively 11.892 and 15.651 both measures increasing in the winter and decreasing in the summer). On the other hand, for dyslipidemic subjects triglycerides increased in summer and decreased in winter, in contrast to normolipidemic ones, in which triglycerides did not show rhythmicity. The number of deaths due to atherosclerotic events showed significant rhythmicity, with maximum and minimum frequencies in winter and summer, respectively. Cross-correlation analyzes showed that low humidity and temperature, higher thermal amplitude and dark cycles are associated with increased levels of LDL-C and HDL-C during winter. In contrast, TG showed moderate cross-correlations with temperature and minimum humidity in an inverse way: maximum temperature and humidity increased TG during the summer. Conclusions: This study showed a coincident rhythmicity between low temperatures and high concentrations of LDL-C and HDL-C and the number of deaths due to atherosclerotic cardiovascular events in individuals from the city of Campinas. The opposite behavior of cholesterol and TG suggest different physiological mechanisms in their metabolic modulation by climate parameters change. Thus, new analyses are underway to better elucidate these mechanisms, as well as variations in lipid concentrations in relation to climatic variations and their associations with atherosclerotic disease and death outcomes in Campinas.

Keywords: atherosclerosis, climatic variations, lipids and lipoproteins, associations

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Authors: A. Harras, A. Tsuji, K. Terada

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Tracking of moving people has gained a matter of great importance due to rapid technological advancements in the field of computer vision. The objective of this study is to design a motion based detection and tracking multiple walking pedestrians randomly in different directions. In our proposed method, Gaussian mixture model (GMM) is used to determine moving persons in image sequences. It reacts to changes that take place in the scene like different illumination; moving objects start and stop often, etc. Background noise in the scene is eliminated through applying morphological operations and the motions of tracked people which is determined by using the Kalman filter. The Kalman filter is applied to predict the tracked location in each frame and to determine the likelihood of each detection. We used a benchmark data set for the evaluation based on a side wall stationary camera. The actual scenes from the data set are taken on a street including up to eight people in front of the camera in different two scenes, the duration is 53 and 35 seconds, respectively. In the case of walking pedestrians in close proximity, the proposed method has achieved the detection ratio of 87%, and the tracking ratio is 77 % successfully. When they are deferred from each other, the detection ratio is increased to 90% and the tracking ratio is also increased to 79%.

Keywords: automatic detection, tracking, pedestrians, counting

Procedia PDF Downloads 233

Authors: Paul Hernandez-Herrera, Fernando Montoya, Juan Manuel Rendon, Alberto Darszon, Gabriel Corkidi

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Intracellular calcium ([Ca2+]i) regulates sperm motility. The analysis of [Ca2+]i has been traditionally achieved in two dimensions while the real movement of the cell takes place in three spatial dimensions. Due to optical limitations (high speed cell movement and low light emission) important data concerning the three dimensional movement of these flagellated cells had been neglected. Visualizing [Ca2+]i in 3D is not a simple matter since it requires complex fluorescence microscopy techniques where the resulting images have very low intensity and consequently low SNR (Signal to Noise Ratio). In 4D sequences, this problem is magnified since the flagellum oscillates (for human sperm) at least at an average frequency of 15 Hz. In this paper, a novel approach to extract the flagellum’s center-line in 4D stacks is presented. For this purpose, an iterative algorithm based on the fast-marching method is proposed to extract the flagellum’s center-line. Quantitative and qualitative results are presented in a 4D stack to demonstrate the ability of the proposed algorithm to trace the flagellum’s center-line. The method reached a precision and recall of 0.96 as compared with a semi-manual method.

Keywords: flagellum, minimal path, segmentation, sperm

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Authors: Bitewulign Mekonnen

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Context: This scientific paper focuses on the use of near-infrared (NIR) spectroscopy to determine glucose concentration in aqueous solutions accurately and rapidly. The study compares six different machine learning methods for predicting glucose concentration and also explores the development of a deep learning model for classifying NIR spectra. The objective is to optimize the detection model and improve the accuracy of glucose prediction. This research is important because it provides a comprehensive analysis of various machine-learning techniques for estimating aqueous glucose concentrations. Research Aim: The aim of this study is to compare and evaluate different machine-learning methods for predicting glucose concentration from NIR spectra. Additionally, the study aims to develop and assess a deep-learning model for classifying NIR spectra. Methodology: The research methodology involves the use of machine learning and deep learning techniques. Six machine learning regression models, including support vector machine regression, partial least squares regression, extra tree regression, random forest regression, extreme gradient boosting, and principal component analysis-neural network, are employed to predict glucose concentration. The NIR spectra data is randomly divided into train and test sets, and the process is repeated ten times to increase generalization ability. In addition, a convolutional neural network is developed for classifying NIR spectra. Findings: The study reveals that the SVMR, ETR, and PCA-NN models exhibit excellent performance in predicting glucose concentration, with correlation coefficients (R) > 0.99 and determination coefficients (R²)> 0.985. The deep learning model achieves high macro-averaging scores for precision, recall, and F1-measure. These findings demonstrate the effectiveness of machine learning and deep learning methods in optimizing the detection model and improving glucose prediction accuracy. Theoretical Importance: This research contributes to the field by providing a comprehensive analysis of various machine-learning techniques for estimating glucose concentrations from NIR spectra. It also explores the use of deep learning for the classification of indistinguishable NIR spectra. The findings highlight the potential of machine learning and deep learning in enhancing the prediction accuracy of glucose-relevant features. Data Collection and Analysis Procedures: The NIR spectra and corresponding references for glucose concentration are measured in increments of 20 mg/dl. The data is randomly divided into train and test sets, and the models are evaluated using regression analysis and classification metrics. The performance of each model is assessed based on correlation coefficients, determination coefficients, precision, recall, and F1-measure. Question Addressed: The study addresses the question of whether machine learning and deep learning methods can optimize the detection model and improve the accuracy of glucose prediction from NIR spectra. Conclusion: The research demonstrates that machine learning and deep learning methods can effectively predict glucose concentration from NIR spectra. The SVMR, ETR, and PCA-NN models exhibit superior performance, while the deep learning model achieves high classification scores. These findings suggest that machine learning and deep learning techniques can be used to improve the prediction accuracy of glucose-relevant features. Further research is needed to explore their clinical utility in analyzing complex matrices, such as blood glucose levels.

Keywords: machine learning, signal processing, near-infrared spectroscopy, support vector machine, neural network

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Authors: Chawarat Rotejanaprasert, Andrew Lawson

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Early detection of unusual health events depends on the ability to detect rapidly any substantial changes in disease, thus facilitating timely public health interventions. To assist public health practitioners to make decisions, statistical methods are adopted to assess unusual events in real time. We introduce a surveillance Kullback-Leibler (SKL) measure for timely detection of disease outbreaks for small area health data. The detection methods are compared with the surveillance conditional predictive ordinate (SCPO) within the framework of Bayesian hierarchical Poisson modeling and applied to a case study of a group of respiratory system diseases observed weekly in South Carolina counties. Properties of the proposed surveillance techniques including timeliness and detection precision are investigated using a simulation study.

Keywords: Bayesian, spatial, temporal, surveillance, prospective

Procedia PDF Downloads 285

Authors: A. Anastasopoulou, A. Kolios, T. Somorin, A. Sowale, Y. Jiang, B. Fidalgo, A. Parker, L. Williams, M. Collins, E. J. McAdam, S. Tyrrel

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Developing countries are nowadays confronted with great challenges related to domestic sanitation services in view of the imminent water scarcity. Contemporary sanitation technologies established in these countries are likely to pose health risks unless waste management standards are followed properly. This paper provides a solution to sustainable sanitation with the development of an innovative toilet system, called Nano Membrane Toilet (NMT), which has been developed by Cranfield University and sponsored by the Bill & Melinda Gates Foundation. The particular technology converts human faeces into energy through gasification and provides treated wastewater from urine through membrane filtration. In order to evaluate the environmental profile of the NMT system, a deterministic life cycle assessment (LCA) has been conducted in SimaPro software employing the Ecoinvent v3.3 database. The particular study has determined the most contributory factors to the environmental footprint of the NMT system. However, as sensitivity analysis has identified certain critical operating parameters for the robustness of the LCA results, adopting a stochastic approach to the Life Cycle Inventory (LCI) will comprehensively capture the input data uncertainty and enhance the credibility of the LCA outcome. For that purpose, Monte Carlo simulations, in combination with an artificial neural network (ANN) model, have been conducted for the input parameters of raw material, produced electricity, NO_X emissions, amount of ash and transportation of fertilizer. The given analysis has provided the distribution and the confidence intervals of the selected impact categories and, in turn, more credible conclusions are drawn on the respective LCIA (Life Cycle Impact Assessment) profile of NMT system. Last but not least, the specific study will also yield essential insights into the methodological framework that can be adopted in the environmental impact assessment of other complex engineering systems subject to a high level of input data uncertainty.

Keywords: sanitation systems, nano-membrane toilet, lca, stochastic uncertainty analysis, Monte Carlo simulations, artificial neural network

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