Search results for: fire prediction

Authors: Siti Badriyah Rushayati, Resti Meilani, Rachmad Hermawan

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A biosphere reserve is developed to create harmony amongst economic development, community development, and environmental protection, through partnership between human and nature. Giam Siak Kecil Bukit Batu Biosphere Reserve (GSKBB BR) in Riau Province, Indonesia, is unique in that it has peat soil dominating the area, many springs essential for human livelihood, high biodiversity. Furthermore, it is the only biosphere reserve covering privately managed production forest areas. The annual occurrences of deforestation and forest fire pose a threat toward such unique biosphere reserve. Forest fire produced smokes that along with mass airflow reached neighboring countries, particularly Singapore and Malaysia. In this research, we aimed at analyzing the threat of deforestation and forest fire, and the potential of CO2 emission at GSKBB BR. We used Landsat image, arcView software, and ERDAS IMAGINE 8.5 Software to conduct spatial analysis of land cover and land use changes, calculated CO2 emission based on emission potential from each land cover and land use type, and exercised simple linear regression to demonstrate the relation between CO2 emission potential and deforestation. The result showed that, beside in the buffer zone and transition area, deforestation also occurred in the core area. Spatial analysis of land cover and land use changes from years 2010, 2012, and 2014 revealed that there were changes of land cover and land use from natural forest and industrial plantation forest to other land use types, such as garden, mixed garden, settlement, paddy fields, burnt areas, and dry agricultural land. Deforestation in core area, particularly at the Giam Siak Kecil Wildlife Reserve and Bukit Batu Wildlife Reserve, occurred in the form of changes from natural forest in to garden, mixed garden, shrubs, swamp shrubs, dry agricultural land, open area, and burnt area. In the buffer zone and transition area, changes also happened, what once swamp forest changed into garden, mixed garden, open area, shrubs, swamp shrubs, and dry agricultural land. Spatial analysis on land cover and land use changes indicated that deforestation rate in the biosphere reserve from 2010 to 2014 had reached 16 119 ha/year. Beside deforestation, threat toward the biosphere reserve area also came from forest fire. The occurrence of forest fire in 2014 had burned 101 723 ha of the area, in which 9 355 ha of core area, and 92 368 ha of buffer zone and transition area. Deforestation and forest fire had increased CO2 emission as much as 24 903 855 ton/year.

Keywords: biosphere reserve, CO2 emission, deforestation, forest fire

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Authors: Soha A. Bahanshal, Byung G. Kim

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Identification of patients at high risk for hospital readmission is of crucial importance for quality health care and cost reduction. Predicting hospital readmissions among diabetic patients has been of great interest to many researchers and health decision makers. We build a prediction model to predict hospital readmission for diabetic patients within 30 days of discharge. The core of the prediction model is a modified k Nearest Neighbor called Hybrid Fuzzy Weighted k Nearest Neighbor algorithm. The prediction is performed on a patient dataset which consists of more than 70,000 patients with 50 attributes. We applied data preprocessing using different techniques in order to handle data imbalance and to fuzzify the data to suit the prediction algorithm. The model so far achieved classification accuracy of 80% compared to other models that only use k Nearest Neighbor.

Keywords: machine learning, prediction, classification, hybrid fuzzy weighted k-nearest neighbor, diabetic hospital readmission

Procedia PDF Downloads 156

Authors: Gabrielle Peck, Ryan Hayes

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The use of a phi meter allows for definition of equivalence ratio during a fire test. Previous phi meter designs have used expensive catalysts and had restricted portability due to the large furnace and requirement for pure oxygen. The new design of the phi meter did not require the use of a catalyst. The furnace design was based on the existing micro-scale combustion calorimetry (MCC) furnace and operating conditions based on the secondary oxidizer furnace used in the steady state tube furnace (SSTF). Preliminary tests were conducted to study the effects of varying furnace temperatures on combustion efficiency. The SSTF was chosen to validate the phi meter measurements as it can both pre-set and independently quantify the equivalence ratio during a test. The data were in agreement with the data obtained on the SSTF. It was also validated by a comparison of CO2 yields obtained from the SSTF oxidizer and those obtained by the phi meter. The phi meter designed and constructed in this work was proven to work effectively on a bench-scale. The phi meter was then used to measure the equivalence ratio on a series of large-scale ISO 9705 tests for numerous fire conditions. The materials used were a range of non-homogenous materials such as polyurethane. The measurements corresponded accurately to the data collected, showing the novel design can be used from bench to large-scale tests to measure equivalence ratio. This cheaper, more portable, safer and easier to use phi meter design will enable more widespread use and the ability to quantify fire conditions of tests, allowing for better understanding of flammability and smoke toxicity.

Keywords: phi meter, smoke toxicity, fire condition, ISO9705, novel equipment

Procedia PDF Downloads 77

Authors: Stepan Kuchar, Martin Golasowski, Radim Vavrik, Michal Podhoranyi, Boris Sir, Jan Martinovic

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The main goal of this article is to describe the online flood monitoring and prediction system Floreon+ primarily developed for the Moravian-Silesian region in the Czech Republic and the basic process it uses for running automatic rainfall-runoff and hydrodynamic simulations along with their calibration and uncertainty modeling. It takes a long time to execute such process sequentially, which is not acceptable in the online scenario, so the use of high-performance computing environment is proposed for all parts of the process to shorten their duration. Finally, a case study on the Ostravice river catchment is presented that shows actual durations and their gain from the parallel implementation.

Keywords: flood prediction process, high performance computing, online flood prediction system, parallelization

Procedia PDF Downloads 463

Authors: Suriya

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Rapid socio-economic development and urbanization have led to an increasingly serious air pollution problem in Ulaanbaatar (UB), the capital of Mongolia. PM₂.₅ pollution has become the most pressing aspect of UB air pollution. Therefore, monitoring and predicting PM₂.₅ concentration in UB is of great significance for the health of the local people and environmental management. As of yet, very few studies have used models to predict PM₂.₅ concentrations in UB. Using data from 0:00 on June 1, 2018, to 23:00 on April 30, 2020, we proposed two deep learning models based on Bayesian-optimized LSTM (Bayes-LSTM) and CNN-LSTM. We utilized hourly observed data, including Himawari8 (H8) aerosol optical depth (AOD), meteorology, and PM₂.₅ concentration, as input for the prediction of PM₂.₅ concentrations. The correlation strengths between meteorology, AOD, and PM₂.₅ were analyzed using the gray correlation analysis method; the comparison of the performance improvement of the model by using the AOD input value was tested, and the performance of these models was evaluated using mean absolute error (MAE) and root mean square error (RMSE). The prediction accuracies of Bayes-LSTM and CNN-LSTM deep learning models were both improved when AOD was included as an input parameter. Improvement of the prediction accuracy of the CNN-LSTM model was particularly enhanced in the non-heating season; in the heating season, the prediction accuracy of the Bayes-LSTM model slightly improved, while the prediction accuracy of the CNN-LSTM model slightly decreased. We propose two novel deep learning models for PM₂.₅ concentration prediction in UB, Bayes-LSTM, and CNN-LSTM deep learning models. Pioneering the use of AOD data from H8 and demonstrating the inclusion of AOD input data improves the performance of our two proposed deep learning models.

Keywords: deep learning, AOD, PM2.5, prediction, Ulaanbaatar

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Authors: A. Majidian

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The life prediction of thermal power plant components is necessary to prevent the unexpected outages, optimize maintenance tasks in periodic overhauls and plan inspection tasks with their schedules. One of the main critical components in a power plant is condenser because its failure can affect many other components which are positioned in downstream of condenser. This paper deals with factors affecting life of condenser. Failure rates dependency vs. these factors has been investigated using Artificial Neural Network (ANN) and fuzzy logic algorithms. These algorithms have shown their capabilities as dynamic tools to evaluate life prediction of power plant equipments.

Keywords: life prediction, condenser tube, neural network, fuzzy logic

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Authors: Farokh Alipour, Ali Falavand, Neda Beit Saeid

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The calculation of the effect of accidental releases of flammable materials such as LNG requires the use of a suitable consequence model. This study is due to providing a planning advice for developments in the vicinity of LNG sites and other sites handling flammable materials. In this paper, an applicable algorithm that is able to model pool fires on water is presented and applied to estimate pool fire damage zone. This procedure can be used to model pool fires on land and could be helpful in consequence modeling and domino effect zone measurements of flammable materials which is needed in site selection and plant layout.

Keywords: LNG, pool fire, spill, radiation

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Authors: Pavel A. Strizhak, Geniy V. Kuznetsov, Ivan S. Voytkov, Dmitri V. Antonov

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This paper presents the results of experiments carried out at a specialized test site for establishing macroscopic patterns of heat and mass transfer processes at localizing model combustion sources of ground forest fires with the use of barrier lines in the form of a wetted lay of material in front of the zone of flame burning and thermal decomposition. The experiments were performed using needles, leaves, twigs, and mixtures thereof. The dimensions of the model combustion source and the ranges of heat release correspond well to the real conditions of ground forest fires. The main attention is paid to the complex analysis of the effect of dispersion of water aerosol (concentration and size of droplets) used to form the barrier line. It is shown that effective conditions for localization and subsequent suppression of flame combustion and thermal decomposition of forest fuel can be achieved by creating a group of barrier lines with different wetting width and depth of the material. Relative indicators of the effectiveness of one and combined barrier lines were established, taking into account all the main characteristics of the processes of suppressing burning and thermal decomposition of forest combustible materials. We performed the prediction of the necessary and sufficient parameters of barrier lines (water volume, width, and depth of the wetted lay of the material, specific irrigation density) for combustion sources with different dimensions, corresponding to the real fire extinguishing practice.

Keywords: forest fire, barrier water lines, pyrolysis front, flame front

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Authors: Kwanele Qonono

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Despite the significance and economic benefits of petrol stations in South Africa, these still pose a huge risk of fire and explosion threatening public safety. This research paper examines the extent to which land-use planning in Stellenbosch, South Africa, considers the fire risk posed by petrol stations and the implications for public safety as well as preparedness for large fires or explosions. To achieve this, the research identified the land-use types around petrol stations in Stellenbosch and determined the extent to which their locations comply with the local, national, and international land-use planning regulations. A mixed research method consisting of the collection and analysis of geospatial data and qualitative data was an applied method, where petrol stations within a six-kilometre radius of Stellenbosch’s town centre were utilised as study sites. The research examined the risk of fires/explosions at these petrol stations. The research investigated Stellenbosch Municipality’s institutional preparedness to respond in the event of a fire/explosion at these petrol stations. The research observed that siting of petrol stations does not comply with local, national, and international good practices, thus exposing the surrounding developments to fires and explosions. Land-use planning practice does not consider hazards created by petrol stations. Despite the potential for major fires at petrol stations, Stellenbosch Municipality’s level of preparedness to respond to petrol station fires appears low due to the prioritisation of more frequent events.

Keywords: petrol stations, technological hazard, drr, land-use planning, risk analysis

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Authors: Tarek Aboueldahab, Amin Mohamed Nassar

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Wind energy is a fluctuating energy source unlike conventional power plants, thus, it is necessary to accurately predict short term wind speed to integrate wind energy in the electricity supply structure. To do so, we present a hybrid artificial intelligence model of short term wind speed prediction based on passive aggregation of the particle swarm optimization and neural networks. As a result, improvement of the prediction accuracy is obviously obtained compared to the standard artificial intelligence method.

Keywords: artificial intelligence, neural networks, particle swarm optimization, passive aggregation, wind speed prediction

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Authors: Thinh Ngo, Paul Rad, Brian Kelley

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Noise estimation is essential in today wireless systems for power control, adaptive modulation, interference suppression and quality of service. Deep learning (DL) has already been applied in the physical layer for modulation and signal classifications. Unacceptably low accuracy of less than 50% is found to undermine traditional application of DL classification for SNR prediction. In this paper, we use divide-and-conquer algorithm and classifier fusion method to simplify SNR classification and therefore enhances DL learning and prediction. Specifically, multiple CNNs are used for classification rather than a single CNN. Each CNN performs a binary classification of a single SNR with two labels: less than, greater than or equal. Together, multiple CNNs are combined to effectively classify over a range of SNR values from −20 ≤ SNR ≤ 32 dB.We use pre-trained CNNs to predict SNR over a wide range of joint channel parameters including multiple Doppler shifts (0, 60, 120 Hz), power-delay profiles, and signal-modulation types (QPSK,16QAM,64-QAM). The approach achieves individual SNR prediction accuracy of 92%, composite accuracy of 70% and prediction convergence one order of magnitude faster than that of traditional estimation.

Keywords: classification, CNN, deep learning, prediction, SNR

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Authors: Pengwei Qiao, Sucai Yang, Wenxia Wei

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Soil pollution has become an important issue in China. Accurate spatial distribution prediction of pollutants with interpolation methods is the basis for soil remediation in the site. However, a relatively strong variability of pollutants would decrease the prediction accuracy. Theoretically, partition interpolation can result in accurate prediction results. In order to verify the applicability of partition interpolation for a site, benzo (b) fluoranthene (BbF) in four soil layers was adopted as the research object in this paper. IDW (inverse distance weighting)-, RBF (radial basis function)-and OK (ordinary kriging)-based partition interpolation accuracies were evaluated, and their influential factors were analyzed; then, the uncertainty and applicability of partition interpolation were determined. Three conclusions were drawn. (1) The prediction error of partitioned interpolation decreased by 70% compared to unpartitioned interpolation. (2) Partition interpolation reduced the impact of high CV (coefficient of variation) and high concentration value on the prediction accuracy. (3) The prediction accuracy of IDW-based partition interpolation was higher than that of RBF- and OK-based partition interpolation, and it was suitable for the identification of highly polluted areas at a contaminated site. These results provide a useful method to obtain relatively accurate spatial distribution information of pollutants and to identify highly polluted areas, which is important for soil pollution remediation in the site.

Keywords: accuracy, applicability, partition interpolation, site, soil pollution, uncertainty

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Authors: Gabrielle Peck, Ryan Hayes

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Current large scale fire tests focus on flammability and heat release measurements. Smoke toxicity isn’t considered despite it being a leading cause of death and injury in unwanted fires. A key reason could be that the practical difficulties associated with quantifying individual toxic components present in a fire effluent often require specialist equipment and expertise. Fire effluent contains a mixture of unreactive and reactive gases, water, organic vapours and particulate matter, which interact with each other. This interferes with the operation of the analytical instrumentation and must be removed without changing the concentration of the target analyte. To mitigate the need for expensive equipment and time-consuming analysis, a portable gas analysis system was designed, constructed and tested for use in large-scale fire tests as a simpler and more robust alternative to online FTIR measurements. The novel equipment aimed to be easily portable and able to run on battery or mains electricity; be able to be calibrated at the test site; be capable of quantifying CO, CO2, O2, HCN, HBr, HCl, NOx and SO2 accurately and reliably; be capable of independent data logging; be capable of automated switchover of 7 bubblers; be able to withstand fire effluents; be simple to operate; allow individual bubbler times to be pre-set; be capable of being controlled remotely. To test the analysers functionality, it was used alongside the ISO/TS 19700 Steady State Tube Furnace (SSTF). A series of tests were conducted to assess the validity of the box analyser measurements and the data logging abilities of the apparatus. PMMA and PA 6.6 were used to assess the validity of the box analyser measurements. The data obtained from the bench-scale assessments showed excellent agreement. Following this, the portable analyser was used to monitor gas concentrations during large-scale testing using the ISO 9705 room corner test. The analyser was set up, calibrated and set to record smoke toxicity measurements in the doorway of the test room. The analyser was successful in operating without manual interference and successfully recorded data for 12 of the 12 tests conducted in the ISO room tests. At the end of each test, the analyser created a data file (formatted as .csv) containing the measured gas concentrations throughout the test, which do not require specialist knowledge to interpret. This validated the portable analyser’s ability to monitor fire effluent without operator intervention on both a bench and large-scale. The portable analyser is a validated and significantly more practical alternative to FTIR, proven to work for large-scale fire testing for quantification of smoke toxicity. The analyser is a cheaper, more accessible option to assess smoke toxicity, mitigating the need for expensive equipment and specialist operators.

Keywords: smoke toxicity, large-scale tests, iso 9705, analyser, novel equipment

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Authors: Jin Lin, Shouxiang Lu, Kim Meow Liew

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In a fire accident, fire smoke often poses a serious threat to human safety especially in the enclosed space such as submarine and space-crafts environment. Efficient removal of the hazardous gas products particularly a large amount of CO and CO₂ gases from these confined space is critical for the security of the staff and necessary for the post-fire environment recovery. In this work, Cu-Mn-Ce composite oxide catalysts coupled with CO₂ sorbents were prepared using wet impregnation method, solid-state impregnation method and wet/solid-state impregnation method. The as-prepared samples were tested dynamically and isothermally for CO oxidation and CO₂ sorption and further characterized by the X-ray diffraction (XRD), nitrogen adsorption and desorption, and field emission scanning electron microscopy (FE-SEM). The results showed that all the samples were able to catalyze CO into CO₂ and capture CO₂ in situ by chemisorption. Among all the samples, the sample synthesized by the wet/solid-state impregnation method showed the highest catalytic activity toward CO oxidation and the fine ability of CO₂ sorption. The sample prepared by the solid-state impregnation method showed the second CO oxidation performance, while the coupled sample using the wet impregnation method exhibited much poor CO oxidation activity. The various CO oxidation and CO₂ sorption properties of the samples might arise from the different dispersed states of the CO₂ sorbent in the CO catalyst, owing to the different preparation methods. XRD results confirmed the high-dispersed sorbent phase in the samples prepared by the wet and solid impregnation method, while that of the sample prepared by wet/solid-state impregnation method showed the larger bulk phase as indicated by the high-intensity diffraction peaks. Nitrogen adsorption and desorption results further revealed that the latter sample had a higher surface area and pore volume, which were beneficial for the CO oxidation over the catalyst. Hence, the Cu-Mn-Ce oxide catalyst coupled with CO₂ sorbent using wet/solid-state impregnation method could be a good choice for fire smoke removal in the enclosed space.

Keywords: CO oxidation, CO₂ sorption, preparation methods, smoke removal

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Authors: Engin Eyceyurt, Josko Zec

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The current and future cellular mobile communication networks generate enormous amounts of data. Networks have become extremely complex with extensive space of parameters, features and counters. These networks are unmanageable with legacy methods and an enhanced design and optimization approach is necessary that is increasingly reliant on machine learning. This paper proposes that machine learning as a viable approach for uplink throughput prediction. LTE radio metric, such as Reference Signal Received Power (RSRP), Reference Signal Received Quality (RSRQ), and Signal to Noise Ratio (SNR) are used to train models to estimate expected uplink throughput. The prediction accuracy with high determination coefficient of 91.2% is obtained from measurements collected with a simple smartphone application.

Keywords: drive test, LTE, machine learning, uplink throughput prediction

Procedia PDF Downloads 126

Authors: Wei Wang, Yuan Hu

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Epoxy resins (EP), one of the most important thermosetting polymers, is widely applied in various fields due to its desirable properties, such as excellent electrical insulation, low shrinkage, outstanding mechanical stiffness, satisfactory adhesion and solvent resistance. However, like most of the polymeric materials, EP has the fatal drawbacks including inherent flammability and high yield of toxic smoke, which restricts its application in the fields requiring fire safety. So, it is still a challenge and an interesting subject to develop new flame retardants which can not only remarkably improve the flame retardancy, but also render modified resins low toxic gases generation. In recent work, polymer nanocomposites based on nanohybrids that contain two or more kinds of nanofillers have drawn intensive interest, which can realize performance enhancements. The realization of previous hybrids of carbon nanotubes (CNTs) and molybdenum disulfide provides us a novel route to decorate layered double hydroxide (LDH) nanosheets on the surface of β-FeOOH nanorods; the deposited LDH nanosheets can fill the network and promote the work efficiency of β-FeOOH nanorods. Moreover, the synergistic effects between LDH and β-FeOOH can be anticipated to have potential applications in reducing fire hazards of EP composites for the combination of condense-phase and gas-phase mechanism. As reported, β-FeOOH nanorods can act as a core to prepare hybrid nanostructures combining with other nanoparticles through electrostatic attraction through layer-by-layer assembly technique. In this work, LDH nanosheets wrapped β-FeOOH nanorods (LDH-β-FeOOH) hybrids was synthesized by a facile method, with the purpose of combining the characteristics of one dimension (1D) and two dimension (2D), to improve the fire resistance of epoxy resin. The hybrids showed a well dispersion in EP matrix and had no obvious aggregation. Thermogravimetric analysis and cone calorimeter tests confirmed that LDH-β-FeOOH hybrids into EP matrix with a loading of 3% could obviously improve the fire safety of EP composites. The plausible flame retardancy mechanism was explored by thermogravimetric infrared (TG-IR) and X-ray photoelectron spectroscopy. The reasons were concluded: condense-phase and gas-phase. Nanofillers were transferred to the surface of matrix during combustion, which could not only shield EP matrix from external radiation and heat feedback from the fire zone, but also efficiently retard transport of oxygen and flammable pyrolysis.

Keywords: fire hazards, toxic gases, self-assembly, epoxy

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Authors: Pingshan Chen, Zhiliang Dong

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In order to estimate the post-construction settlement more objectively, the power-polynomial model is proposed, which can reflect the trend of settlement development based on the observed settlement data. It was demonstrated by an actual case history of an embankment, and during the prediction. Compared with the other three prediction models, the power-polynomial model can estimate the post-construction settlement more accurately with more simple calculation.

Keywords: prediction, model, post-construction settlement, soft ground

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Authors: Yoftahe Nigussie Worku

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This report unveils a meticulous project focused on the design intricacies of a Fire Tube Boiler tailored for the efficient generation of saturated steam. The overarching objective is to produce 2000kg/h of saturated steam at 12-bar design pressure, achieved through the development of an advanced fire tube boiler. This design is meticulously crafted to harmonize cost-effectiveness and parameter refinement, with a keen emphasis on material selection for component parts, construction materials, and production methods throughout the analytical phases. The analytical process involves iterative calculations, utilizing pertinent formulas to optimize design parameters, including the selection of tube diameters and overall heat transfer coefficients. The boiler configuration incorporates two passes, a strategic choice influenced by tube and shell size considerations. The utilization of heavy oil fuel no. 6, with a higher heating value of 44000kJ/kg and a lower heating value of 41300kJ/kg, results in a fuel consumption of 140.37kg/hr. The boiler achieves an impressive heat output of 1610kW with an efficiency rating of 85.25%. The fluid flow pattern within the boiler adopts a cross-flow arrangement strategically chosen for inherent advantages. Internally, the welding of the tube sheet to the shell, secured by gaskets and welds, ensures structural integrity. The shell design adheres to European Standard code sections for pressure vessels, encompassing considerations for weight, supplementary accessories (lifting lugs, openings, ends, manhole), and detailed assembly drawings. This research represents a significant stride in optimizing fire tube boiler technology, balancing efficiency and safety considerations in the pursuit of enhanced saturated steam production.

Keywords: fire tube, saturated steam, material selection, efficiency

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Authors: Yang Zhang, Jian He

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Heart disease is one of the leading causes of death in the world, and coronary heart disease (CHD) is one of the major heart diseases. Electrocardiogram (ECG) is widely used in the detection of heart diseases, but the traditional manual method for CHD prediction by analyzing ECG requires lots of professional knowledge for doctors. This paper introduces sliding window and continuous wavelet transform (CWT) to transform ECG signals into images, and then ResNet and Bi-LSTM are introduced to build the ECG feature extraction network (namely ECGNet). At last, an auxiliary system for coronary heart disease prediction was developed based on modified ResNet18 and Bi-LSTM, and the public ECG dataset of CHD from MIMIC-3 was used to train and test the system. The experimental results show that the accuracy of the method is 83%, and the F1-score is 83%. Compared with the available methods for CHD prediction based on ECG, such as kNN, decision tree, VGGNet, etc., this method not only improves the prediction accuracy but also could avoid the degradation phenomenon of the deep learning network.

Keywords: Bi-LSTM, CHD, ECG, ResNet, sliding window

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Authors: Sophie N. Selby-Pham, Yudie Wang, Louise Bennett

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Consumption of grapes promotes health and reduces the risk of chronic diseases due to the action of grape phytochemicals in regulation of Oxidative Stress and Inflammation (OSI). The bioefficacy of phytochemicals depends on their absorption in the human body. The time required for phytochemicals to achieve maximal plasma concentration (Tₘₐₓ) after oral intake reflects the time window of maximal bioefficacy of phytochemicals, with Tₘₐₓ dependent on physicochemical properties of phytochemicals. This research collated physicochemical properties of grape phytochemicals from white and red grapes to predict their Tₘₐₓ using pharmacokinetic modelling. The predicted values of Tₘₐₓ were then compared to the measured Tₘₐₓ collected from clinical studies to determine the accuracy of prediction. In both liquid and solid intake forms, white grapes exhibit a shorter Tₘₐₓ range (0.5-2.5 h) versus red grapes (1.5-5h). The prediction accuracy of Tₘₐₓ for grape phytochemicals was 33.3% total error of prediction compared to the mean, indicating high prediction accuracy. Pharmacokinetic modelling allows prediction of Tₘₐₓ without costly clinical trials, informing dosing frequency for sustained presence of phytochemicals in the body to optimize the health benefits of phytochemicals.

Keywords: absorption kinetics, phytochemical, phytochemical absorption prediction model, Vitis vinifera

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Authors: Cedric Leong, Parth Desai, Parth Patel

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The purpose of this project was to develop a neural network based on qualitative team data to predict alliance scores to determine winners of matches in the FIRST Robotics Competition (FRC). The game for the competition changes every year with different objectives and game objects, however the idea was to create a prediction system which can be reused year by year using some of the statistics that are constant through different games, making our system adaptable to future games as well. Aerial Assist is the FRC game for 2014, and is played in alliances of 3 teams going against one another, namely the Red and Blue alliances. This application takes any 6 teams paired into 2 alliances of 3 teams and generates the prediction for the final score between them.

Keywords: artifical neural network, prediction system, qualitative team data, FIRST Robotics Competition (FRC)

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Authors: Zhila Hemati, Seyed Sajjad Hosseni, Sohrab Zamzami

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In nature, forests serve a multitude of functions. They stabilize and nourish soil, store carbon, clean the air and water, and support biodiverse ecosystems. A natural disaster that can affect forests and ecosystems locally or globally is wildfires. Iran experiences annual forest fires that affect roughly 6000 hectares, with the Arasbaran forest being the most affected. These fires may be generated unnaturally by human activity in the forests, or they could occur naturally as a result of climate change. These days, wildfires pose a major natural risk. Wildfires significantly reduce the amount of property and human life in ecosystems globally. Concerns regarding the immediate and longterm effects have been raised by the rise in fire activity in various Iranian regions in recent decades. Natural ecosystem abundance, quality, and health will all be impacted by pasture and forest fires. Monitoring is the first line of defense against and control for forest fires. To determine the spatial-temporal variations of these occurrences in the vegetation regions of Arasbaran, this study was carried out to estimate the areas affected by fires. The findings indicated that July through September, which spans over 130000 hectares, is when fires in Arasbaran's vegetation areas occur to their greatest extent. A significant portion of the nation's forests caught fire in 2024, particularly in the northwest of the Arasbaran vegetation area. On the other hand, January through March sees the least number of fire locations in the Arasbaran vegetation areas. The Arasbaran forest experiences its greatest number of forest fires during the hot, dry months of the year. As a result, the linear association between the burned and active fire regions in the Arasbaran forest indicates a substantial relationship between species abundance and plant species. This link demonstrates that some of the active forest fire centers are the burned regions in Arasbaran's vegetation areas.

Keywords: wildfire, vegetation, plant species, forest

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Authors: Khalaf Khatatneh, Nabeel Al-Milli, Amjad Hudaib, Monther Ali Tarawneh

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Software fault prediction identify potential faults in software modules during the development process. In this paper, we present a novel approach for software fault prediction by combining a feedforward neural network with particle swarm optimization (PSO). The PSO algorithm is employed as a feature selection technique to identify the most relevant metrics as inputs to the neural network. Which enhances the quality of feature selection and subsequently improves the performance of the neural network model. Through comprehensive experiments on software fault prediction datasets, the proposed hybrid approach achieves better results, outperforming traditional classification methods. The integration of PSO-based feature selection with the neural network enables the identification of critical metrics that provide more accurate fault prediction. Results shows the effectiveness of the proposed approach and its potential for reducing development costs and effort by detecting faults early in the software development lifecycle. Further research and validation on diverse datasets will help solidify the practical applicability of the new approach in real-world software engineering scenarios.

Keywords: feature selection, neural network, particle swarm optimization, software fault prediction

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Authors: Ajayi Olusola Olajide, Alonge Olaide Moses

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Predicting the outcome of soccer matches poses an interesting challenge for which it is realistically impossible to successfully do so for every match. Despite this, there are lots of resources that are being expended on the correct prediction of soccer matches weekly, and all over the world. Soccer Match Result Prediction System Model (SMRPSM) is a system that is proposed whereby the results of matches between two soccer teams are auto-generated, with the added excitement of giving users a chance to test their predictive abilities. Soccer teams from different league football are loaded by the application, with each team’s corresponding manager and other information like team location, team logo and nickname. The user is also allowed to interact with the system by selecting the match to be predicted and viewing of the results of completed matches after registering/logging in.

Keywords: predicting, soccer match, outcome, soccer, matches, result prediction, system, model

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Authors: Jerzy Ejsmont, Beata Świeczko-Żurek, Grzegorz Ronowski

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Gas cylinders in general and particularly cylinders containing acetylene constitute a great potential danger for fire and rescue services involved in salvage operations. Experiments show that gas cylinders with acetylene, oxygen, hydrogen, CNG, LPG or CO2 may blow after short exposition to heat with very destructive effect as fragments of blown cylinder may fly even several hundred meters. In the case of acetylene, the explosion may occur also several hours after the cylinder is cooled down. One of the possible neutralization procedures that in many cases may be used to prevent explosions is shooting dangerous cylinders by rifle bullets. This technique is used to neutralize acetylene cylinders in a few European countries with great success. In Poland research project 'BLOW' was launched in 2014 with the aim to investigate phenomena related to fire influence on industrial and home used cylinders and to evaluate usefulness of the shooting technique. All together over 100 gas cylinders with different gases were experimentally tested at the military blasting grounds and in shelters. During the experiments cylinder temperature and pressure were recorded. In the case of acetylene that is subjected to thermal decomposition also concentration of hydrogen was monitored. Some of the cylinders were allowed to blow and others were shot by snipers. It was observed that shooting hot cylinders has never created more dangerous situations than letting the cylinders to explode spontaneously. In a great majority of cases cylinders that were punctured by bullets released gas in a more or less violent but relatively safe way. The paper presents detailed information about experiments and presents particularities of behavior of cylinders containing different gases. Extensive research was also done in order to select bullets that may be safely and efficiently used to puncture different cylinders. The paper shows also results of those experiments as well as gives practical information related to techniques that should be used during shooting.

Keywords: fire, gas cylinders, neutralization, shooting

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Authors: Shital Suresh Borse, Vijayalaxmi Kadroli

Abstract:

E-commerce industries nowadays implement the latest AI, ML Techniques to improve their own performance and prediction accuracy. This helps to gain a huge profit from the online market. Ant Colony Optimization, Genetic algorithm, Particle Swarm Optimization, Neural Network & GWO help many e-commerce industries for up-gradation of their predictive performance. These algorithms are providing optimum results in various applications, such as stock price prediction, prediction of drug-target interaction & user ratings of similar products in e-commerce sites, etc. In this study, customer reviews will play an important role in prediction analysis. People showing much interest in buying a lot of services& products suggested by other customers. This ultimately increases net profit. In this work, a convolution neural network (CNN) is proposed which further is useful to optimize the prediction accuracy of an e-commerce website. This method shows that CNN is used to optimize hyperparameters of GWO algorithm using an appropriate coding scheme. Accurate model results are verified by comparing them to PSO results whose hyperparameters have been optimized by CNN in Amazon's customer review dataset. Here, experimental outcome proves that this proposed system using the GWO algorithm achieves superior execution in terms of accuracy, precision, recovery, etc. in prediction analysis compared to the existing systems.

Keywords: prediction analysis, e-commerce, machine learning, grey wolf optimization, particle swarm optimization, CNN

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Authors: Estelle Arbellay, Lori D. Daniels, Shawn D. Mansfield, Alice S. Chang

Abstract:

Mountain pine beetle (MPB) outbreaks are currently devastating lodgepole pine forests in western North America, which are also widely disturbed by frequent wildfires. Both MPB and fire can leave scars on lodgepole pine trees, thereby diminishing their commercial value and possibly compromising their utilization in solid wood products. In order to fully exploit the affected resource, it is crucial to understand how wounding from these two disturbance agents impact wood properties. Moreover, previous research on lodgepole pine has focused solely on sound wood and stained wood resulting from the MPB-transmitted blue fungi. By means of a quantitative multi-proxy approach, we tested the hypotheses that (i) wounding (of either MPB or fire origin) caused significant changes in wood properties of lodgepole pine and that (ii) MPB-induced wound effects could differ from those induced by fire in type and magnitude. Pith-to-bark strips were extracted from 30 MPB scars and 30 fire scars. Strips were cut immediately adjacent to the wound margin and encompassed 12 rings from normal wood formed prior to wounding and 12 rings from wound wood formed after wounding. Wood properties evaluated within this 24-year window included ring width, relative wood density, cellulose crystallinity, fibre dimensions, and carbon and nitrogen concentrations. Methods used to measure these proxies at a (sub-)annual resolution included X-ray densitometry, X-ray diffraction, fibre quality analysis, and elemental analysis. Results showed a substantial growth release in wound wood compared to normal wood, as both earlywood and latewood width increased over a decade following wounding. Wound wood was also shown to have a significantly different latewood density than normal wood 4 years after wounding. Latewood density decreased in MPB scars while the opposite was true in fire scars. By contrast, earlywood density was presented only minor variations following wounding. Cellulose crystallinity decreased in wound wood compared to normal wood, being especially diminished in MPB scars the first year after wounding. Fibre dimensions also decreased following wounding. However, carbon and nitrogen concentrations did not substantially differ between wound wood and normal wood. Nevertheless, insect-feeding and fire-heating wounding were shown to significantly alter most wood properties of lodgepole pine, as demonstrated by the existence of several morphological anomalies in wound wood. MPB and fire generally elicited similar anomalies, with the major exception of latewood density. In addition to providing quantitative criteria for differentiating between biotic (MPB) and abiotic (fire) disturbances, this study provides the wood industry with fundamental information on the physiological response of lodgepole pine to wounding in order to evaluate the utilization of scarred trees in solid wood products.

Keywords: elemental analysis, fibre quality analysis, lodgepole pine, wood properties, wounding, X-ray densitometry, X-ray diffraction

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Authors: C. Manjula, Lilly Florence

Abstract:

Software technology is developing rapidly which leads to the growth of various industries. Now-a-days, software-based applications have been adopted widely for business purposes. For any software industry, development of reliable software is becoming a challenging task because a faulty software module may be harmful for the growth of industry and business. Hence there is a need to develop techniques which can be used for early prediction of software defects. Due to complexities in manual prediction, automated software defect prediction techniques have been introduced. These techniques are based on the pattern learning from the previous software versions and finding the defects in the current version. These techniques have attracted researchers due to their significant impact on industrial growth by identifying the bugs in software. Based on this, several researches have been carried out but achieving desirable defect prediction performance is still a challenging task. To address this issue, here we present a machine learning based hybrid technique for software defect prediction. First of all, Genetic Algorithm (GA) is presented where an improved fitness function is used for better optimization of features in data sets. Later, these features are processed through Decision Tree (DT) classification model. Finally, an experimental study is presented where results from the proposed GA-DT based hybrid approach is compared with those from the DT classification technique. The results show that the proposed hybrid approach achieves better classification accuracy.

Keywords: decision tree, genetic algorithm, machine learning, software defect prediction

Procedia PDF Downloads 303

Authors: Rodrigo Aguiar, Adelino Ferreira

Abstract:

Road traffic accidents are the leading cause of unnatural death and injuries worldwide, representing a significant problem of road safety. In this context, the use of artificial intelligence with advanced machine learning techniques has gained prominence as a promising approach to predict traffic accidents. This article investigates the application of machine learning algorithms to develop traffic accident frequency prediction models. Models are evaluated based on performance metrics, making it possible to do a comparative analysis with traditional prediction approaches. The results suggest that machine learning can provide a powerful tool for accident prediction, which will contribute to making more informed decisions regarding road safety.

Keywords: machine learning, artificial intelligence, frequency of accidents, road safety

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Authors: Abraham O. Olaniyi, Oluyemi Oke, Atilade Otunla

Abstract:

The structural performance of bridges decreases progressively throughout their service life due to many contributing factors (fatigue, carbonation, fire incidents etc.). Around the world, numerous bridges have attained their estimated service life and many have approached this limit. The structural integrity assessment of the burnt composite bridge located at CH57+450, Koita village along Abuja-Abaji-Lokoja road, Nigeria, is presented as a case study and shall be forthwith referred to as the 'Koita bridge' in this paper. From the technical evaluation, the residual compressive strength of the concrete piers was found to be below 16.0 N/mm2. This value is very low compared to the expected design value of 30.0 N/mm2. The pier capping beam at pier location 1 has a very low residual compressive strength. The cover to the reinforcement of certain capping beams has an outline of reinforcement which signifies poor concrete cover and the mean compressive strength is also less than 20.0 N/mm2. The steel girder indicated black colouration as a result of the fire incident without any significant structural defect like buckling or warping of the steel section. This paper reviews the structural integrity assessment and repair methodology of the Koita bridge; a composite bridge damaged by fire, highlighting the various challenges of limited obtainable guidance documents about the bridge. The objectives are to increase the understanding of processes and versatile equipment required to test and assess a fire-damaged bridge in order to improve the quality of structural appraisal and rehabilitation; thus, eliminating the prejudice associated with current visual inspection techniques.

Keywords: assessment, bridge, rehabilitation, sustainability

Procedia PDF Downloads 338