Search results for: forest fire detection

Authors: Uliana N. Tumanova, Viacheslav M. Lyapin, Vladimir G. Bychenko, Alexandr I. Shchegolev, Gennady T. Sukhikh

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It is well known that the gas formed as a result of postmortem decomposition of tissues can be detected already 24-48 hours after death. In addition, the conditions of keeping and storage of the corpse (temperature and humidity of the environment) significantly determine the rate of occurrence and development of posthumous changes. The presence of sepsis is accompanied by faster postmortem decomposition and decay of the organs and tissues of the body. The presence of gas in the vessels and cavities can be revealed fully at postmortem CT. Radiologists must certainly report on the detection of intraorganic or intravascular gas, wich was detected at postmortem CT, to forensic experts or pathologists before the autopsy. This gas can not be detected during autopsy, but it can be very important for establishing a diagnosis. To explore the possibility of postmortem CT for the evaluation of gas accumulations in the newborns' vessels, who died from congenital sepsis. Researched of 44 newborns bodies (25 male and 19 female sex, at the age from 6 hours to 27 days) after 6 - 12 hours of death. The bodies were stored in the refrigerator at a temperature of +4°C in the supine position. Grouped 12 bodies of newborns that died from congenital sepsis. The control group consisted of 32 bodies of newborns that died without signs of sepsis. Postmortem CT examination was performed at the GEMINI TF TOF16 device, before the autopsy. The localizations of gas accumulations in the vessels were determined on the CT tomograms. The sepsis diagnosis was on the basis of clinical and laboratory data and autopsy results. Gases in the vessels were detected in 33.3% of cases in the group with sepsis, and in the control group - in 34.4%. A group with sepsis most often the gas localized in the heart and liver vessels - 50% each, of observations number with the detected gas in the vessels. In the heart cavities, aorta and mesenteric vessels - 25% each. In control most often gas was detected in the liver (63.6%) and abdominal cavity (54.5%) vessels. In 45.5% the gas localized in the cavities, and in 36.4% in the vessels of the heart. In the cerebral vessels and in the aorta gas was detected in 27.3% and 9.1%, respectively. Postmortem CT has high diagnostic capabilities to detect free gas in vessels. Postmortem changes in newborns that died from sepsis do not affect intravascular gas production within 6-12 hours. Radiation methods should be used as a supplement to the autopsy, including as a kind of ‘guide’, with the indication to the forensic medical expert of certain changes identified during CT studies, for better definition of pathological processes during the autopsy. Postmortem CT can be recommend as a first stage of autopsy.

Keywords: congenital sepsis, gas, newborn, postmortem CT

Procedia PDF Downloads 141

Authors: A. Sopharak, B. Uyyanonvara, S. Barman

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Diabetic retinopathy is characterized by the development of retinal microaneurysms. The damage can be prevented if disease is treated in its early stages. In this paper, we are comparing Support Vector Machine (SVM) and Naïve Bayes (NB) classifiers for automatic microaneurysm detection in images acquired through non-dilated pupils. The Nearest Neighbor classifier is used as a baseline for comparison. Detected microaneurysms are validated with expert ophthalmologists’ hand-drawn ground-truths. The sensitivity, specificity, precision and accuracy of each method are also compared.

Keywords: diabetic retinopathy, microaneurysm, naive Bayes classifier, SVM classifier

Procedia PDF Downloads 325

Authors: Alexey V. Klyuev, Valery P. Samarin, Viktor F. Klyuev, Andrey V. Klyuev

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A power measurement algorithm of the input mix components of the noise signal and pulse interference is considered. The algorithm efficiency analysis has been carried out for different interference to signal ratio. Algorithm performance features have been explored by numerical experiment results.

Keywords: noise signal, pulse interference, signal power, spectrum width, detection

Procedia PDF Downloads 335

Authors: Zeinab Farhat, Nicolas Errien, Romuald Wernert, Véronique Verriele, Frédéric Amiard, Philippe Daniel

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Ovarian cancer, also known as the silent killer, is the fifth most common cancer among women worldwide, and its death rate is higher than that of other gynecological cancers. The low survival rate of women with high-grade serous ovarian carcinoma highlights the critical need for the development of new methods for early detection and diagnosis of the disease. The aim of this study was to evaluate if Raman spectroscopy combined with chemometric methods such as Principal Component Analysis (PCA) could differentiate between cancerous and normal tissues from different types of samples, such as paraffin embedding, chemical deparaffinized, formalin-fixed and fresh samples of the same normal and malignant ovarian tissue. The method was applied specifically to two critical spectral regions: the signature region (860-1000 〖cm〗^(-1)) and the high-frequency region (2800-3100 〖cm〗^(-1) ). The mean spectra of paraffin-embedded in normal and malignant tissues showed almost similar intensity. On the other hand, the mean spectra of normal and cancer tissues from chemical deparaffinized, formalin-fixed, and fresh samples show significant intensity differences. These spectral differences reflect variations in the molecular composition of the tissues, particularly lipids and proteins. PCA, which was applied to distinguish between cancer and normal tissues, was performed on whole spectra and on selected regions—the PCA score plot of paraffin-embedded shows considerable overlap between the two groups. However, the PCA score of chemicals deparaffinized, formalin-fixed, and fresh samples showed a good discrimination of tissue types. Our findings were validated by analyses of a set of samples whose status (normal and cancerous) was not previously known. The results of this study suggest that Raman Spectroscopy associated with PCA methods has the capacity to provide clinically significant differentiation between normal and cancerous ovarian tissues.

Keywords: Raman spectroscopy, ovarian cancer, signal processing, Principal Component Analysis, classification

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Authors: Anees Raza, Liu Chunyan

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Renewable energy technologies are need of the time and are already making the big impact in the climatic outlook of the world. Biogas technology is one of those, and it has a lot of benefits for its users. It is cost effective because it is produced from the raw material which is available free of cost to the livestock farmers. Bio-slurry, a by-product of biogas, is being used as fertilizer for the crops production and increasing soil fertility. There are many other household benefits of technology. Research paper discusses the benefits of biogas as perceived by the biogas users as well as non-users of Tehsil Jhang. Data were collected from 60 respondents (30 users and 30 non-users) selected purposively through validated and pre-tested interview schedule from the respondents. Collected data were analyzed by using Statistical Package for Social Sciences (SPSS). Household benefits like ‘makes cooking easy,’ ‘Less breathing issues for working women in kitchens’ and ‘Use of bio-slurry as organic fertilizer’ had the highly significant relationship between them with t-values of 3.24, 4.39 and 2.80 respectively. Responses of the respondents about environmental benefits of biogas technology showed that ‘less air pollution’ had a significant relationship between them while ‘less temperature rise up than due to the burning of wood /dung’ had the non-significant relationship in the responses of interviewed respondents. It was clear from the research that biogas users were becoming influential in convincing non-users to adopt this technology due to its noticeable benefits. Research area where people were depending on wood to be used as fire fuel could be helped in reduction of cutting of trees which will help in controlling deforestation and saving the environment.People should be encouraged in using of biogas technology through providing them subsidies and low mark up loans.

Keywords: biogas technology, deforestation, environmental benefits, renewable energy

Procedia PDF Downloads 261

Authors: K. Akutsu, S. Mori, T. Hanashima

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Fluorescent probes are useful for the selective detection of trace amount of ions and biomolecular imaging in living cells. Various kinds of metal ion-selective fluorescent compounds have been developed, and some compounds have been applied as effective metal ion-selective fluorescent probes. However, because competition between the ligand and water molecules for the metal ion constitutes a major contribution to the stability of a complex in aqueous solution, it is difficult to develop a highly sensitive, selective, and stable fluorescent probe in aqueous solution. The micelles, these are formed in the surfactant aqueous solution, provides a unique hydrophobic nano-environment for stabilizing metal-organic complexes in aqueous solution. Therefore, we focused on the unique properties of micelles to develop a new fluorescence analysis system. We have been developed a fluorescence analysis system for Sr(II) by using a Sr(II) fluorescent sensor, N-(2-hydroxy-3-(1H-benzimidazol-2-yl)-phenyl)-1-aza-18-crown-6-ether (BIC), and studied its complexation behavior with Sr(II) in micellar solution. We revealed that the stability constant of Sr(II)-BIC complex was 10 times higher than that in aqueous solution. In addition, its detection limit value was also improved up to 300 times by this system. However, the mechanisms of these phenomena have remained obscure. In this study, we investigated the structure of Sr(II)-BIC complex in aqueous micellar solution by combining use the extended X-ray absorption fine structure (EXAFS) and neutron reflectivity (NR) method to understand the unique properties of the fluorescence analysis system from the view point of structural chemistry. EXAFS and NR experiments were performed on BL-27B at KEK-PF and on BL17 SHARAKU at J-PARC MLF, respectively. The obtained EXAFS spectra and their fitting results indicated that Sr(II) and BIC formed a Sr(18-crown-6-ether)-like complex in aqueous micellar solution. The EXAFS results also indicated that the hydrophilic head group of surfactant molecule was directly coordinated with Sr(II). In addition, the NR results also indicated that Sr(II)-BIC complex would interact with the surface of micelle molecules. Therefore, we concluded that Sr(II), BIC, and surfactant molecule formed a ternary complexes in aqueous micellar solution, and at least, it is clear that the improvement of the stability constant in micellar solution is attributed to the result of the formation of Sr(BIC)(surfactant) complex.

Keywords: micell, fluorescent probe, neutron reflectivity, EXAFS

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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

Procedia PDF Downloads 76

Authors: Aya Salama

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Digital Twin is an emerging research topic that attracted researchers in the last decade. It is used in many fields, such as smart manufacturing and smart healthcare because it saves time and money. It is usually related to other technologies such as Data Mining, Artificial Intelligence, and Machine Learning. However, Human digital twin (HDT), in specific, is still a novel idea that still needs to prove its feasibility. HDT expands the idea of Digital Twin to human beings, which are living beings and different from the inanimate physical entities. The goal of this research was to create a Human digital twin that is responsible for real-time human replies automation by simulating human behavior. For this reason, clustering, supervised classification, topic extraction, and sentiment analysis were studied in this paper. The feasibility of the HDT for personal replies generation on social messaging applications was proved in this work. The overall accuracy of the proposed approach in this paper was 63% which is a very promising result that can open the way for researchers to expand the idea of HDT. This was achieved by using Random Forest for clustering the question data base and matching new questions. K-nearest neighbor was also applied for sentiment analysis.

Keywords: human digital twin, sentiment analysis, topic extraction, supervised machine learning, unsupervised machine learning, classification, clustering

Procedia PDF Downloads 83

Authors: Abdelaziz Messis, Azzeddine Bettache, Nawel Boucherba, Said Benallaoua, Mouloud Kecha

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Actinobacteria are of special biotechnological interest since they are known to produce chemically diverse compounds with a wide range of biological activity. This distinct clade of Gram-positve bacteria include some of the key antibiotic producers and are also sources of several bioactive compounds, established commercially a newly filamentous bacteria was recovered from Tikjda forest soil (Algeria) for its high antifungal activity against various pathogenic and phytopathogenic fungi. The nucleotide sequence of the 16S rRNA gene (1454 pb) of Streptomyces sp. TKJ2 exhibited close similarity (99 %) with other Streptomyces16S rRNA genes. Antifungal metabolite production of Streptomyces sp TKJ2 was evaluated using six different fermentation media. The extracellular products contained potent antifungal agents. Antifungal protein produced by Streptomyces sp. TKJ2 on PCA medium has been purified by ammonium sulfate precipitation, SPE column chromatography and high-performance liquid chromatography in a reverse-phase column. The UV chromatograms of the active fractions obtained at 214 nm by NanoLC-ESI-MS/MS have different molecular weights. The F20 Peptidic fraction obtained from culture filtrat of Streptomyces sp. TKJ2 precipitated at 30% of ammonium sulfate was selected for analysis by infusion ESI-MS which yielded a singly charged ion mass of 437.17 Da.

Keywords: actinobacteria, antifungal protein, chromatography, Streptomyces

Procedia PDF Downloads 379

Authors: Zarana Dave

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Cerium-doped lanthanum bromide, LaBr₃ (Ce), scintillator shows attracting properties for spectroscopy that makes it a suitable solution for security, medical, geophysics and high energy physics applications. Here, the performance parameters of a cylindrical LaBr₃ (Ce) scintillator was investigated. The first aspect is the determination of the efficiency for γ - ray detection, measured with GEANT4 simulation toolkit from 10keV to 10MeV energy range. The second is the detailed study of background radiation of LaBr₃ (Ce). It has relatively high intrinsic radiation background due to naturally occurring ¹³⁸La and ²²⁷Ac radioisotopes.

Keywords: LaBr₃(Ce), GEANT4, efficiency, background radiation

Procedia PDF Downloads 220

Authors: Li Maksym, Prabhakar M. N., Jung-Il Song

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In this study, a flame retardant and hydrophobic cotton textile were prepared by utilizing a renewable halogen-free bio-based solution based on chitosan, urea, and phytic acid, named bio-flame retardant liquid (BFL), through facile dip-coating technology. Deposition of BFL on the surface of the cotton was confirmed by Fourier-transform infrared spectroscopy and scanning electron microscope coupled with energy-dispersive X-ray spectrometer. Thermal and flame retardant properties of the cottons were studied with thermogravimetric analysis, differential scanning calorimetry, vertical flame test, cone calorimeter test. Only with 8.8% of dry weight gain treaded cotton showed self-extinguish properties during fire test. Cone calorimeter test revealed a reduction of peak heat release rate from 203.2 to 21 kW/m2 and total heat release from 20.1 to 2.8 MJ/m2. Incidentally, BFL remarkably improved the thermal stability of flame retardant cotton from expressed in an enhanced amount of char at 700 °C (6.7 vs. 33.5%). BFL initiates the formation of phosphorous and silica contain char layer whichrestrains the propagation of heat and oxygen to unburned materialstrengthen by the liberation of non-combustible gases, which reduce the concentration of flammable volatiles and oxygen hence reducing the flammability of cotton. In addition, hydrophobicity and specific ignition test for upholstery application were performed. In conjunction, the proposed flame retardant cotton is potentially translatable to be utilized as upholstery materials in public transport.

Keywords: cotton farbic, flame retardancy, surface coating, intumescent mechanism

Procedia PDF Downloads 89

Authors: Feixiong Chen, Naoufel Haddour, Marie Frenea-Robin, Yves MéRieux, Yann Chevolot, Virginie Monnier

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Neonatal thrombopenia occurs when the mother generates antibodies against her baby’s platelet antigens. It is particularly critical for newborns because it can cause coagulation troubles leading to intracranial hemorrhage. In this case, diagnosis must be done quickly to make platelets transfusion immediately after birth. Before transfusion, platelet antigens must be tested carefully to avoid rejection. The majority of thrombopenia (95 %) are caused by antibodies directed against Human Platelet Antigen 1a (HPA-1a) or 5b (HPA-5b). The common method for antigen platelets detection is polymerase chain reaction allowing for identification of gene sequence. However, it is expensive, time-consuming and requires significant blood volume which is not suitable for newborns. We propose to develop a point-of-care device based on double functionalized magnetic colloids with 1) antibodies specific to antigen platelets and 2) highly sensitive electroactive molecules in order to be detected by an electrochemical microsensor. These magnetic colloids will be used first to isolate platelets from other blood components, then to capture specifically platelets bearing HPA-1a and HPA-5b antigens and finally to attract them close to sensor working electrode for improved electrochemical signal. The expected advantages are an assay time lower than 20 min starting from blood volume smaller than 100 µL. Our functionalization procedure based on amine dendrimers and NHS-ester modification of initial carboxyl colloids will be presented. Functionalization efficiency was evaluated by colorimetric titration of surface chemical groups, zeta potential measurements, infrared spectroscopy, fluorescence scanning and cyclic voltammetry. Our results showed that electroactive molecules and antibodies can be immobilized successfully onto magnetic colloids. Application of a magnetic field onto working electrode increased the detected electrochemical signal. Magnetic colloids were able to capture specific purified antigens extracted from platelets.

Keywords: Magnetic Nanoparticles , Electroactive Molecules, Antibody, Platelet

Procedia PDF Downloads 268

Authors: Mahtab Davari, Charles Edward Okon, Somayeh Aghanavesi

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Every institute of learning is usually interested in the performance of enrolled students. The level of these performances determines the approach an institute of study may adopt in rendering academic services. The focus of this paper is to evaluate students' academic performance in given courses of study using machine learning methods. This study evaluated various supervised machine learning classification algorithms such as Logistic Regression (LR), Support Vector Machine, Random Forest, Decision Tree, K-Nearest Neighbors, Linear Discriminant Analysis, and Quadratic Discriminant Analysis, using selected features to predict study performance. The accuracy, precision, recall, and F1 score obtained from a 5-Fold Cross-Validation were used to determine the best classification algorithm to predict students’ performances. SVM (using a linear kernel), LDA, and LR were identified as the best-performing machine learning methods. Also, using the LR model, this study identified students' educational habits such as reading and paying attention in class as strong determinants for a student to have an above-average performance. Other important features include the academic history of the student and work. Demographic factors such as age, gender, high school graduation, etc., had no significant effect on a student's performance.

Keywords: student performance, supervised machine learning, classification, cross-validation, prediction

Procedia PDF Downloads 121

Authors: Yasir E. Mohieldeen

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Qatar is one of the most water scarce countries in the World. In 2014, the average per capita rainfall was less than 29 m3/y/ca, while the global average is 6,000 m3/y/ca. However, the per capita water consumption in Qatar is among the highest in the World: more than 500 liters per person per day, whereas the global average is 160 liters per person per day. Since the early 2000s, Qatar has been relying heavily on desalinated water from the Arabian Gulf as the main source of fresh water. In 2009, about 99.9% of the total potable water produced was desalinated. Reliance on desalinated water makes Qatar very vulnerable to water related natural disasters, such as the red-tide phenomenon. Qatar’s strategic water reserve lasts for only 7 days. In case of red-tide outbreak, the country would not be able to desalinate water for days, let alone the months that this disaster would bring about (as it clogs the desalination equipment). The 2008-09 red-tide outbreak, for instance, lasted for more than eight months and forced the closure of desalination plants in the region for weeks. This study aims at identifying favorite conditions for red-tide outbreaks, using satellite data along with in-situ measurements. This identification would allow the prediction of these outbreaks and their hotspots. Prediction and monitoring of outbreaks are crucial to water security in the country, as different measures could be put in place in advance to prevent an outbreak and mitigate its impact if it happened. Red-tide outbreaks are detected using different algorithms for chlorophyll concentration in the Gulf waters. Vegetation indices, such as Normalized Difference Vegetation Index (NDVI) and Enhanced Vegetation Index (EVI) were used along with Surface Algae Bloom Index (SABI) to detect known outbreaks. MODIS (or Moderate Resolution Imaging Spectroradiometer) bands are used to calculate these indices. A red-tide outbreaks atlas in the Arabian Gulf is being produced. Prediction of red-tide outbreaks ahead of their occurrences would give critical information on possible water-shortage in the country. Detecting known outbreaks in the past few decades and related parameters (e.g. water salinity, water surface temperature, nutrition, sandstorms, … etc) enables the identification of favorite conditions of red-tide outbreak that are key to the prediction of these outbreaks.

Keywords: Arabian Gulf, MODIS, red-tide detection, strategic water reserve, water desalination

Procedia PDF Downloads 100

Authors: Tariq A. El Shheibia, Halima Mohamed Belhamad

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This paper is considered securing data based on multi-path protocol (SDMP) in mobile ad hoc network utilizing OPNET simulator modular 14.5, including the AODV routing protocol at the network as based multi-path algorithm for message security in MANETs. The main idea of this work is to present a way that is able to detect the attacker inside the MANETs. The detection for this attacker will be performed by adding some effective parameters to the network.

Keywords: MANET, AODV, malicious node, OPNET

Procedia PDF Downloads 292

Authors: Brendah Nagula, Omolo Fredrick Okalebo, Ronald Ssengendo, Ivan Bamweyana

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The Kampala Physical Development Plan (KPDP) was developed in 2012 and projected both long term and short term developments within the City .The purpose of the plan was to not only shape the city into a spatially planned area but also to control the urban sprawl trends that had expanded with pronounced instances of informal settlements. This plan was approved by the National Physical Planning Board and a signature was appended by the Minister in 2013. Much as the KPDP plan has been implemented using different approaches such as detailed planning, development control, subdivision planning, carrying out construction inspections, greening and beautification, there is still limited knowledge on the level of conformance towards this plan. Therefore, it is yet to be determined whether it has been effective in shaping the City into an ideal spatially planned area. Attaining a clear picture of the level of conformance towards the KPDP 2012 through evaluation between the planned and the existing land use in Kampala City was performed. Methods such as Supervised Classification and Post Classification Change Detection were adopted to perform this evaluation. Scrutiny of findings revealed Central Division registered the lowest level of conformance to the planning standards specified in the KPDP 2012 followed by Nakawa, Rubaga, Kawempe, and Makindye. Furthermore, mixed-use development was identified as the land use with the highest level of non-conformity of 25.11% and institutional land use registered the highest level of conformance of 84.45 %. The results show that the aspect of location was not carefully considered while allocating uses in the KPDP whereby areas located near the Central Business District have higher land rents and hence require uses that ensure profit maximization. Also, the prominence of development towards mixed-use denotes an increased demand for land towards compact development that was not catered for in the plan. Therefore in order to transform Kampala city into a spatially planned area, there is need to carefully develop detailed plans especially for all the Central Division planning precincts indicating considerations for land use densification.

Keywords: spatial plan, post classification change detection, Kampala city, landuse

Procedia PDF Downloads 86

Authors: Sepulveda Juan

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This article explains the elements and considerations taken into account when implementing and applying patent evaluation and scientometric study in the identifications of technology trends, and the tools that led to the implementation of a software application for patent revision. Univariate analysis helped recognize the technological leaders in the field of energy, and steered the way for a multivariate analysis of this sample, which allowed for a graphical description of the techniques of mature technologies, as well as the detection of emerging technologies. This article ends with a validation of the methodology as applied to the case of fuel cells.

Keywords: patents, scientometric, renewable energy, technology maps

Procedia PDF Downloads 300

Authors: Lena Ernstgård, Aishwarya M. Dwivedi, Johan Lundström, Gunnar Johanson

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A simple inexpensive olfactometer was constructed to enable valid measures of detection threshold of low concentrations of vapours of chemicals. The delivery system consists of seven syringe pumps, each connected to a Tedlar bag containing a predefined concentration of the test chemical in the air. The seven pumps are connected to a 8-way mixing valve which in turn connects to a birhinal nose piece. Chemical vapor of known concentration is generated by injection of an appropriate amount of the test chemical into a Tedlar bag with a known volume of clean air. Complete vaporization is assured by gentle heating of the bag from the outside with a heat flow. The six test concentrations are obtained by adding different volumes from the starting bag to six new Tedlar bags with known volumes of clean air. One bag contains clean air only. Thus, six different test concentrations and clean air can easily be tested in series by shifting the valve to new positions. Initial in-line measurement with a photoionization detector showed that the delivery system quickly responded to a shift in valve position. Thus 90% of the desired concentration was reached within 15 seconds. The concentrations in the bags are verified daily by gas chromatography. The stability of the system in terms of chemical concentration is monitored in real time by means of a photo-ionization detector. To determine lateralization thresholds, an additional pump supplying clean air is added to the delivery system in a way so that the nostrils can be separately and interchangeably be exposed to clean air and test chemical. Odor and lateralization thresholds were determined for three aldehydes; acrolein, crotonaldehyde, and hexanal in 20 healthy naïve individuals. Aldehydes generally have a strong odour, and the selected aldehydes are also considered to be irritating to mucous membranes. The median odor thresholds of the three aldehydes were 0.017, 0.0008, and 0.097 ppm, respectively. No lateralization threshold could be identified for acrolein, whereas the medians for crotonaldehyde and hexanal were 0.003 and 0.39 ppm, respectively. In conclusion, we constructed a simple, inexpensive olfactometer that allows for stable and easily measurable concentrations of vapors of the test chemical. Our test with aldehydes demonstrates that the system produces valid detection among volunteers in terms of odour and lateralization thresholds.

Keywords: irritation, odour delivery, olfactometer, smell

Procedia PDF Downloads 214

Authors: Sirajdin Olagoke Adeshina, Haidi Ibrahim, Akeem Salawu

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large lecture theatres cannot be covered by a single camera but rather by a multicamera setup because of their size, shape, and seating arrangements. Although, classroom capture is achievable through a single camera. Therefore, a design and implementation of a multicamera setup for a large lecture hall were considered. Researchers have shown emphasis on the impact of class attendance taken on the academic performance of students. However, the traditional method of carrying out this exercise is below standard, especially for large lecture theatres, because of the student population, the time required, sophistication, exhaustiveness, and manipulative influence. An automated large classroom attendance system is, therefore, imperative. The common approach in this system is face detection and recognition, where known student faces are captured and stored for recognition purposes. This approach will require constant face database updates due to constant changes in the facial features. Alternatively, face counting can be performed by cropping the localized faces on the video or image into a folder and then count them. This research aims to develop a face localization-based approach to detect student faces in classroom images captured using a multicamera setup. A selected Haar-like feature cascade face detector trained with an asymmetric goal to minimize the False Rejection Rate (FRR) relative to the False Acceptance Rate (FAR) was applied on Raspberry Pi 4B. A relationship between the two factors (FRR and FAR) was established using a constant (λ) as a trade-off between the two factors for automatic adjustment during training. An evaluation of the proposed approach and the conventional AdaBoost on classroom datasets shows an improvement of 8% TPR (output result of low FRR) and 7% minimization of the FRR. The average learning speed of the proposed approach was improved with 1.19s execution time per image compared to 2.38s of the improved AdaBoost. Consequently, the proposed approach achieved 97% TPR with an overhead constraint time of 22.9s compared to 46.7s of the improved Adaboost when evaluated on images obtained from a large lecture hall (DK5) USM.

Keywords: automatic attendance, face detection, haar-like cascade, manual attendance

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Authors: Somia Lakehal Ayat, Ibtissem Samai, Srara Lakehal Ayat, Chaima Dahmani

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The aim of this work relates to the physicochemical diversity and the characterization of the different types of soils of the edough massif (North East of Algeria) and to the evaluation and characterization of the existing organic matter as well as to the evolution. and the dynamics of the latter, also on its influence on changes in the physical properties of soils. In order to know the soil properties of seraidi forest, we established a stratified sampling plan. The results obtained show that we are in the presence of a great diversity of soils, such as neutral to alkaline, whose adsorbent complex is sufficiently saturated. Also, the presence of limestone offers the soil a fairly significant buffering capacity. In our study region, the texture of the soils is varied between clayey and silty, where it offers medium porosity, there is a strong accumulation of organic matter, therefore soils rich in organic matter.The fractionation of the organic matter of the soils allowed to obtain a very high rate of humification. The soil characteristics of the edough massif (North East of Algeria) are controlled by the contribution of organic matter, which presents a dynamic and an important evolution and which varies with the climatic conditions and the nature and the type of plant formation, and these the latter have a capital and important role in the rate of mineralization of organic matter.

Keywords: organic matter, soil, foresty, diversity, mineralization

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Authors: Reggie Chandra

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Trafficaccidents and traffic signal optimization are correlated. However, 70-90% of the traffic signals across the USA are not synchronized. The reason behind that is insufficient resources to create and implement timing plans. In this work, we will discuss the use of a breakthrough Artificial Intelligence (AI) technology to optimize traffic flow and collect 24/7/365 accurate traffic data using a vehicle detection system. We will discuss what are recent advances in Artificial Intelligence technology, how does AI work in vehicles, pedestrians, and bike data collection, creating timing plans, and what is the best workflow for that. Apart from that, this paper will showcase how Artificial Intelligence makes signal timing affordable. We will introduce a technology that uses Convolutional Neural Networks (CNN) and deep learning algorithms to detect, collect data, develop timing plans and deploy them in the field. Convolutional Neural Networks are a class of deep learning networks inspired by the biological processes in the visual cortex. A neural net is modeled after the human brain. It consists of millions of densely connected processing nodes. It is a form of machine learning where the neural net learns to recognize vehicles through training - which is called Deep Learning. The well-trained algorithm overcomes most of the issues faced by other detection methods and provides nearly 100% traffic data accuracy. Through this continuous learning-based method, we can constantly update traffic patterns, generate an unlimited number of timing plans and thus improve vehicle flow. Convolutional Neural Networks not only outperform other detection algorithms but also, in cases such as classifying objects into fine-grained categories, outperform humans. Safety is of primary importance to traffic professionals, but they don't have the studies or data to support their decisions. Currently, one-third of transportation agencies do not collect pedestrian and bike data. We will discuss how the use of Artificial Intelligence for data collection can help reduce pedestrian fatalities and enhance the safety of all vulnerable road users. Moreover, it provides traffic engineers with tools that allow them to unleash their potential, instead of dealing with constant complaints, a snapshot of limited handpicked data, dealing with multiple systems requiring additional work for adaptation. The methodologies used and proposed in the research contain a camera model identification method based on deep Convolutional Neural Networks. The proposed application was evaluated on our data sets acquired through a variety of daily real-world road conditions and compared with the performance of the commonly used methods requiring data collection by counting, evaluating, and adapting it, and running it through well-established algorithms, and then deploying it to the field. This work explores themes such as how technologies powered by Artificial Intelligence can benefit your community and how to translate the complex and often overwhelming benefits into a language accessible to elected officials, community leaders, and the public. Exploring such topics empowers citizens with insider knowledge about the potential of better traffic technology to save lives and improve communities. The synergies that Artificial Intelligence brings to traffic signal control and data collection are unsurpassed.

Keywords: artificial intelligence, convolutional neural networks, data collection, signal control, traffic signal

Procedia PDF Downloads 164

Authors: Katawut Kaewbanjong

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We analyzed a volume of data and found significant user satisfaction in software project factors. A statistical significance analysis (logistic regression) and collinearity analysis determined the significance factors from a group of 71 pre-defined factors from 191 software projects in ISBSG Release 12. The eight prediction models used for testing the prediction potential of these factors were Neural network, k-NN, Naïve Bayes, Random forest, Decision tree, Gradient boosted tree, linear regression and logistic regression prediction model. Fifteen pre-defined factors were truly significant in predicting user satisfaction, and they provided 82.71% prediction accuracy when used with a neural network prediction model. These factors were client-server, personnel changes, total defects delivered, project inactive time, industry sector, application type, development type, how methodology was acquired, development techniques, decision making process, intended market, size estimate approach, size estimate method, cost recording method, and effort estimate method. These findings may benefit software development managers considerably.

Keywords: prediction model, statistical analysis, software project, user satisfaction factor

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Authors: Shaik Asha Begum, S. Joshna Rani, Shaik Abdul Rahaman

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INTRODUCTION: Breast cancer is a disease that creates in breast cells. "KAP" study estimates the Knowledge, Attitude, and Practices of a local area. More than 1.5 million ladies (25% of all ladies with malignancy) are determined to have bosom disease consistently all through the world. Understanding the degrees of Knowledge, Attitude and Practice will empower a more effective cycle of mindfulness creation as it will permit the program to be custom-made all the more properly to the necessities of the local area. OBJECTIVES: The objective of this study is to assess the knowledge on signs and symptoms, risk factors, provide awareness on the practicing of the early detection techniques of breast cancer and provide knowledge on the overall breast cancer including preventive techniques. METHODOLOGY: This is an expressive cross-sectional investigation. This investigation of KAP was done in the Nirmala Educational Institutions from January to April 2021. A total of 300 participants are included from women students in pharmacy graduates & lecturers, and also from graduates other than the pharmacy. The examiners are taken from the BCAM (Breast Cancer Awareness Measure), tool compartment (Version 2). RESULT: According to the findings of the study, the majority of the participants were not well informed about breast cancer. A lump in the breast was the most commonly mentioned sign of breast cancer, followed by pain in the breast or nipple. The percentage of knowledge related to the breast cancer risk factors was also very less. The correct answers for breast cancer risk factors were radiation exposure (58.20 percent), a positive family history (47.6 percent), obesity (46.9 percent), a lack of physical activity (43.6 percent), and smoking (43.2 percent). Breast cancer screening, on the other hand, was uncommon (only 30 and 11.3 percent practiced clinical breast examination and mammography respectively). CONCLUSION: In this study, the knowledge on the signs and symptoms, risk factors of breast cancer - pharmacy graduates have more knowledge than the non-pharmacy graduates but in the preventive techniques and early detective tools of breast cancer -had poor knowledge in the pharmacy and non-pharmacy graduate. After the awareness program, pharmacy and non-pharmacy graduates got supportive knowledge on the preventive techniques and also practiced the early detective techniques of breast cancer.

Keywords: breast cancer, mammography, KAP study, early detection

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Authors: Samuel Emeka Anarah, Kingsley Nnaemeka Ogbu, Obasi Arinze

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Quantifying the hydrological response due to changes in climate change is imperative for proper management of water resources within a watershed. The impact of climate change on the hydrology of the Upper Ebony River (UER) watershed, South East Nigeria, was studied using the Soil and Water Assessment Tool (SWAT) hydrological model. A climatological time series analysis from 1985 - 2014 using non-parametric test showed significant negative trends in precipitation and relative humidity trend while minimum and maximum temperature, solar radiation and wind speed showed significant positive trends. Future hypothetical land-use change scenarios (Scenarios 1, 2, 3 and 4) representing urbanization and conversion of forest to agricultural land were combined with future downscaled climate model (CSIRO-Mk3-6-0) and simulated in SWAT model. Relative to the Baseline scenario (2005 - 2014), the results showed a decrease in streamflow by 10.29%, 26.20%, 11.80% and 26.72% for Scenarios 1, 2, 3, and 4 respectively. Model results suggest development of adaptation strategies to cope with the predicted hydrological conditions under future climate change in the watershed.

Keywords: climate change, hydrology, runoff, SWAT model

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Authors: Sangseung Kang, Kyekyung Kim, Suyoung Chi

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A good posture is the most important factor in riding. In this paper, we present an effective posture correction system for a riding simulator environment to provide position error detection and customized training functions. The proposed system detects and analyzes the rider's posture using depth data and inertial sensing data. Our experiments show that including these functions will help users improve their seat for a riding.

Keywords: posture correction, posture training, riding posture, riding simulator

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Authors: Agoston Restas

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Introduction: In case of managing disasters decision makers can face many times such a special situation where any pre-sign of the drastically change is missing therefore the improvised decision making can be required. The complexity, ambiguity, uncertainty or the volatility of the situation can require many times the improvisation as decision making. It can be taken at any level of the management (strategic, operational and tactical) but at tactical level the main reason of the improvisation is surely time pressure. It is certainly the biggest problem during the management. Methods: The author used different tools and methods to achieve his goals; one of them was the study of the relevant literature, the other one was his own experience as a firefighting manager. Other results come from two surveys that are referred to; one of them was an essay analysis, the second one was a word association test, specially created for the research. Results and discussion: This article proves that, in certain situations, the multi-criteria, evaluating decision-making processes simply cannot be used or only in a limited manner. However, it can be seen that managers, directors or commanders are many times in situations that simply cannot be ignored when making decisions which should be made in a short time. The functional background of decisions made in a short time, their mechanism, which is different from the conventional, was studied lately and this special decision procedure was given the name recognition-primed decision. In the article, author illustrates the limits of the possibilities of analytical decision-making, presents the general operating mechanism of recognition-primed decision-making, elaborates on its special model relevant to managers at tactical level, as well as explore and systemize the factors that facilitate (catalyze) the processes with an example with fire managers.

Keywords: decision making, disaster managers, recognition primed decision, model for making decisions in emergencies

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Authors: Collins Ogbu, Godwin Johnny Akpan, James NDA Jacob

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The debilitating outcomes of violent conflict, consummated by rural armed banditry have nonetheless, occasioned the need for the mapping of crime zones in Nigeria. As a step towards understanding the scourge of armed bandits, ungoverned spaces have been uncovered as the most dominant excuse for rural crimes and fierce confrontations. From the creeks of the Niger Delta to the forest of Sambisa, Small Arms and Light Weapons (SALW) have proliferated to the vagaries of national insecurity. While the trends present indications of State fragility, the paucity of governance in these so-called ungoverned spaces has persistently reflected a Hobbesian state of nature, where the fittest survives. This study, therefore, interrogates the demographic implications of these ungoverned spaces by specifically identifying the most immediate features of the characters in the areas under investigation. The Farmers-Herders Crises, Niger-Delta Militancy, Boko-Haram Insurgency, Armed Robbery, Kidnapping and Cattle Rustling all define the major focus. In undertaking this study, anecdotal sources will be relied on, while extant information on the concept of ungoverned spaces will be content-analyzed. It is hoped that the knowledge gathered, as a result, will ultimately aid in proffering a dependable panacea to the crises of rural armed banditry in Nigeria.

Keywords: ungoverned spaces, rural armed banditry, state fragility, conflicts

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Authors: Sónia Ribeiro, Diana Farinha, Hélia Sales, Rita Pontes, João Nunes

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The demand and commitment to sustainability in the agrifood sector introduce news opportunities for new composite materials. Composite materials are emerging as a vital entity for the sustainable development. Polylactic acid (PLA) has been recognized as a potential polymer with attractive characteristics for agrifood sector applications. PLA that can be beneficial for the development of composites, biocomposites, films, porous gels, and so on. The production of PLA from lignocellulosic biomass residues matrix is a key option towards a sustainable and circular bioeconomy and a non-competitive application with feed and food sector. The Flui and BeirInov projects presents news developments in the production of PLA composites to value the Portuguese forest ecosystem, with high amount of lignocellulosic biomass residues and available. A performance production of lactic acid from lignocellulosic biomass undergoes a process of autohydrolysis, saccharification and fermentation, originating a lactic acid fermentation medium with a 72.27g.L-1 was obtained and a final purification of 72%. The high purification PLA from multi lignocellulosic residues representing one economic expensive process, and a new materials and application for the polymers and a combination with others types of composites matrix characteristic is the drive-up for this green market.

Keywords: polylactic acid, lignocellulosic biomass, agrifood, composite materials

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Authors: Idowu Olugbenga Adewumi, Foluke Iyabo Oluwatoyinbo

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Presently, there are several discussions on the food security with increase in yield of arable crop throughout the world. This article, briefly present research efforts to create digital interfaces to nature, in particular to area of crop production in agriculture with increase in yield with interest on pervasive computing. The approach goes beyond the use of sensor networks for environmental monitoring but also by emphasizing the development of a system architecture that detect intruder (Intrusion Process) which reduce the yield of the farmer at the end of the planting/harvesting period. The objective of the work is to set a model for setting up the hand held or portable device for increasing the quality and quantity of arable crop. This process incorporates the use of infrared motion image sensor with security alarm system which can send a noise signal to intruder on the farm. This model of the portable image sensing device in monitoring or scaring human, rodent, birds and even pests activities will reduce post harvest loss which will increase the yield on farm. The nano intelligence technology was proposed to combat and minimize intrusion process that usually leads to low quality and quantity of produce from farm. Intranet system will be in place with wireless radio (WLAN), router, server, and client computer system or hand held device e.g PDAs or mobile phone. This approach enables the development of hybrid systems which will be effective as a security measure on farm. Since, precision agriculture has developed with the computerization of agricultural production systems and the networking of computerized control systems. In the intelligent plant production system of controlled greenhouses, information on plant responses, measured by sensors, is used to optimize the system. Further work must be carry out on modeling using pervasive computing environment to solve problems of agriculture, as the use of electronics in agriculture will attracts more youth involvement in the industry.

Keywords: pervasive computing, intrusion detection, precision agriculture, security, arable crop

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Authors: Hamza Zidoum

Abstract:

This paper investigates the impact of human genetic variation on the function of human proteins using machine-learning algorithms. Single-Nucleotide Polymorphism represents the most common form of human genome variation. We focus on the single amino-acid polymorphism located in the coding region as they can affect the protein function leading to pathologic phenotypic change. We use several supervised Machine Learning methods to identify structural properties correlated with increased risk of the missense mutation being damaging. SVM associated with Principal Component Analysis give the best performance.

Keywords: single-nucleotide polymorphism, machine learning, feature selection, SVM

Procedia PDF Downloads 373