Search results for: weather prediction

Authors: Talha A. Al-Dubai, Rashad A. Bantan, Ramadan H. Abu-Zied, Brian G. Jones, Aaid G. Al-Zubieri

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Contemporary foraminiferal samples sediments were collected from the intertidal sabkha of Al-Kharrar Lagoon, Saudi Arabia, to study the vertical distribution of Foraminifera and, based on a modern training set, their potential to develop a predictor of former sea-level changes in the area. Based on hierarchical cluster analysis, the intertidal sabkha is divided into three vertical zones (A, B & C) represented by three foraminiferal assemblages, where agglutinated species occupied Zone A and calcareous species occupied the other two zones. In Zone A (high intertidal), Agglutinella compressa, Clavulina angularis and C. multicamerata are dominant species with a minor presence of Peneroplis planatus, Coscinospira hemprichii, Sorites orbiculus, Quinqueloculina lamarckiana, Q. seminula, Ammonia convexa and A. tepida. In contrast, in Zone B (middle intertidal) the most abundant species are P. planatus, C. hemprichii, S. orbiculus, Q. lamarckiana, Q. seminula and Q. laevigata, while Zone C (low intertidal) is characterised by C. hemprichii, Q. costata, S. orbiculus, P. planatus, A. convexa, A. tepida, Spiroloculina communis and S. costigera. A transfer function for sea-level reconstruction was developed using a modern dataset of 75 contemporary sediment samples and 99 species collected from several transects across the sabkha. The model provided an error of 0.12m, suggesting that intertidal foraminifers are able to predict the past sea-level changes with high precision in Al-Kharrar Lagoon, and thus the future prediction of those changes in the area.

Keywords: Lagoonal foraminifers, intertidal sabkha, vertical zonation, transfer function, sea level

Procedia PDF Downloads 148

Authors: Antti Lanki, Justiina Halonen, Juuso Punnonen, Emmi Rantavuo

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Marine oil spill response operation requires extensive vessel maneuvering and navigation skills. At-sea oil containment and recovery include both single vessel and multi-vessel operations. Towing long oil containment booms that are several hundreds of meters in length, is a challenge in itself. Boom deployment and towing in multi-vessel configurations is an added challenge that requires precise coordination and control of the vessels. Efficient communication, as a prerequisite for shared situational awareness, is needed in order to execute the response task effectively. To gain and maintain adequate maritime skills, practical training is needed. Field exercises are the most effective way of learning, but especially the related vessel operations are resource-intensive and costly. Field exercises may also be affected by environmental limitations such as high sea-state or other adverse weather conditions. In Finland, the seasonal ice-coverage also limits the training period to summer seasons only. In addition, environmental sensitiveness of the sea area restricts the use of real oil or other target substances. This paper examines, whether maritime simulator training can offer a complementary method to overcome the training challenges related to field exercises. The objective is to assess the efficiency and the learning impact of simulator training, and the specific skills that can be trained most effectively in simulators. This paper provides an overview of learning results from two oil spill response pilot courses, in which maritime navigational bridge simulators were used to train the oil spill response authorities. The simulators were equipped with an oil spill functionality module. The courses were targeted at coastal Fire and Rescue Services responsible for near shore oil spill response in Finland. The competence levels of the participants were surveyed before and after the course in order to measure potential shifts in competencies due to the simulator training. In addition to the quantitative analysis, the efficiency of the simulator training is evaluated qualitatively through feedback from the participants. The results indicate that simulator training is a valid and effective method for developing marine oil spill response competencies that complement traditional field exercises. Simulator training provides a safe environment for assessing various oil containment and recovery tactics. One of the main benefits of the simulator training was found to be the immediate feedback the spill modelling software provides on the oil spill behaviour as a reaction to response measures.

Keywords: maritime training, oil spill response, simulation, vessel manoeuvring

Procedia PDF Downloads 144

Authors: Charles St. Geste, Michael Neumann, Catherine Twohig

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Our primary aim is to examine farmer-centered participatory action learning as a tool to improve agricultural production, build resilience to climate shocks and, more broadly, advance community-driven solutions for sustainable development in rural communities across Haiti. For over six years, sixty plus farmers from Deslandes, Haiti, organized in three traditional work groups called konbits, have designed and tested low-input agroecology techniques as part of the Konbit Vanyan Kapab Pwoje Agroekoloji. The project utilizes a participatory action learning approach, emphasizing social inclusion, building on local knowledge, experiential learning, active farmer participation in trial design and evaluation, and cross-community sharing. Mixed methods were used to evaluate changes in knowledge and adoption of agroecology techniques, confidence in advancing agroecology locally, and innovation among Konbit Vanyan Kapab farmers. While skill and knowledge in application of agroecology techniques varied among individual farmers, a majority of farmers successfully adopted techniques outside of the trial farms. The use of agroecology techniques on trial and individual farms has doubled crop production in many cases. Farm income has also increased, and farmers report less damage to crops and property caused by extreme weather events. Furthermore, participatory action strategies have led to greater local self-determination and greater capacity for sustainable community development. With increased self-confidence and the knowledge and skills acquired from participating in the project, farmers prioritized sharing their successful techniques with other farmers and have developed a farmer-to-farmer training program that incorporates participatory action learning. Using adult education methods, farmers, trained as agroecology educators, are currently providing training in sustainable farming practices to farmers from five villages in three departments across Haiti. Konbit Vanyan Kapab farmers have also begun testing production of value-added food products, including a dried soup mix and tea. Key factors for success include: opportunities for farmers to actively participate in all phases of the project, group diversity, resources for application of agroecology techniques, focus on group processes and overcoming local barriers to inclusive decision-making.

Keywords: agroecology, participatory action learning, rural Haiti, sustainable community development

Procedia PDF Downloads 121

Authors: Shafait Hussain Ali

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Since its outbreak in China, the Covid_19 19 disease has been caused by the corona virus SARS N coyote 2. Italy was the first Western country to be severely affected, and the first country to take drastic measures to control the disease. In start of December 2019, the sudden outbreaks of the Coronary Virus Disease was caused by a new Corona 2 virus (SARS-CO2) of acute respiratory syndrome in china city Wuhan. The World Health Organization declared the epidemic a public health emergency of international concern on January 30, 2020,. On February 14, 2020, 49,053 laboratory-confirmed deaths and 1481 deaths have been reported worldwide. The threat of the disease has forced most of the governments to implement various control measures. Therefore it becomes necessary to analyze the Italian data very carefully, in particular to investigates and to find out the present condition and the number of infected persons in the form of positive cases, death, hospitalized or some other features of infected persons will clear in simple form. So used such a model that will clearly shows the real facts and figures and also understandable to every readable person which can get some real benefit after reading it. The model used must includes(total positive cases, current positive cases, hospitalized patients, death, recovered peoples frequency rates ) all features that explains and clear the wide range facts in very simple form and helpful to administration of that country.

Keywords: machine learning tools and techniques, rapid miner tool, Naive-Bayes algorithm, predictions

Procedia PDF Downloads 85

Authors: Sikander Nawaz Khan

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Natural disasters like flood, earthquake, cyclone, volcanic eruption and others are causing immense losses to the property and lives every year. Current status and actual loss information of natural hazards can be determined and also prediction for next probable disasters can be made using different remote sensing and mapping technologies. Global Positioning System (GPS) calculates the exact position of damage. It can also communicate with wireless sensor nodes embedded in potentially dangerous places. GPS provide precise and accurate locations and other related information like speed, track, direction and distance of target object to emergency responders. Remote Sensing facilitates to map damages without having physical contact with target area. Now with the addition of more remote sensing satellites and other advancements, early warning system is used very efficiently. Remote sensing is being used both at local and global scale. High Resolution Satellite Imagery (HRSI), airborne remote sensing and space-borne remote sensing is playing vital role in disaster management. Early on Geographic Information System (GIS) was used to collect, arrange, and map the spatial information but now it has capability to analyze spatial data. This analytical ability of GIS is the main cause of its adaption by different emergency services providers like police and ambulance service. Full potential of these so called 3S technologies cannot be used in alone. Integration of GPS and other remote sensing techniques with GIS has pointed new horizons in modeling of earth science activities. Many remote sensing cases including Asian Ocean Tsunami in 2004, Mount Mangart landslides and Pakistan-India earthquake in 2005 are described in this paper.

Keywords: disaster mitigation, GIS, GPS, remote sensing

Procedia PDF Downloads 439

Authors: Alexander J. Severinsky

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Radiative forces of greenhouse gases (GHG) increase the temperature of the Earth's surface, more on land, and less in oceans, due to their thermal capacities. Given this inertia, the temperature increase is delayed over time. Air temperature, however, is not delayed as air thermal capacity is much lower. In this study, through analysis and synthesis of multidisciplinary science and data, an estimate of atmospheric temperature increase is made. Then, this estimate is used to shed light on current observations of ice and snow loss, desertification and forest fires, and increased extreme air disturbances. The reason for this inquiry is due to the author’s skepticism that current changes cannot be explained by a "~1 ^oC" global average surface temperature rise within the last 50-60 years. The only other plausible cause to explore for understanding is that of atmospheric temperature rise. The study utilizes an analysis of air temperature rise from three different scientific disciplines: thermodynamics, climate science experiments, and climactic historical studies. The results coming from these diverse disciplines are nearly the same, within ± 1.6%. The direct radiative force of GHGs with a high level of scientific understanding is near 4.7 W/m² on average over the Earth’s entire surface in 2018, as compared to one in pre-Industrial time in the mid-1700s. The additional radiative force of fast feedbacks coming from various forms of water gives approximately an additional ~15 W/m². In 2018, these radiative forces heated the atmosphere by approximately 5.1 ^oC, which will create a thermal equilibrium average ground surface temperature increase of 4.6 ^oC to 4.8 ^oC by the end of this century. After 2018, the temperature will continue to rise without any additional increases in the concentration of the GHGs, primarily of carbon dioxide and methane. These findings of the radiative force of GHGs in 2018 were applied to estimates of effects on major Earth ecosystems. This additional force of nearly 20 W/m² causes an increase in ice melting by an additional rate of over 90 cm/year, green leaves temperature increase by nearly 5 ^oC, and a work energy increase of air by approximately 40 Joules/mole. This explains the observed high rates of ice melting at all altitudes and latitudes, the spread of deserts and increases in forest fires, as well as increased energy of tornadoes, typhoons, hurricanes, and extreme weather, much more plausibly than the 1.5 ^oC increase in average global surface temperature in the same time interval. Planned mitigation and adaptation measures might prove to be much more effective when directed toward the reduction of existing GHGs in the atmosphere.

Keywords: greenhouse radiative force, greenhouse air temperature, greenhouse thermodynamics, greenhouse historical, greenhouse radiative force on ice, greenhouse radiative force on plants, greenhouse radiative force in air

Procedia PDF Downloads 78

Authors: Brahim Mazian, Anne Bergeret, Jean-Charles Benezet, Sandrine Bayle, Luc Malhautier

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Hemp fibers are increasingly used as reinforcements in polymer matrix composites due to their competitive performance (low density, mechanical properties and biodegradability) compared to conventional fibres such as glass fibers. However, the huge variation of their biochemical, physical and mechanical properties limits the use of these natural fibres in structural applications when high consistency and homogeneity are required. In the hemp industry, traditional processes termed field retting are commonly used to facilitate the extraction and separation of stem fibers. This retting treatment consists to spread out the stems on the ground for a duration ranging from a few days to several weeks. Microorganisms (fungi and bacteria) grow on the stem surface and produce enzymes that degrade pectinolytic substances in the middle lamellae surrounding the fibers. This operation depends on the weather conditions and is currently carried out very empirically in the fields so that a large variability in the hemp fibers quality (mechanical properties, color, morphology, chemical composition…) is resulting. Nonetheless, if controlled, retting might be favorable for good properties of hemp fibers and then of hemp fibers reinforced composites. Therefore, the present study aims to investigate the influence of controlled retting within a designed environmental chamber (lab-scale pilot unit) on the quality of the hemp fibres harvested at the seed maturity growth stage. Various assessments were applied directly on fibers: color observations, morphological (optical microscope), surface (ESEM), biochemical (gravimetry) analysis, spectrocolorimetric measurements (pectins content), thermogravimetric analysis (TGA) and tensile testing. The results reveal that controlled retting leads to a rapid change of color from yellow to dark grey due to development of microbial communities (fungi and bacteria) at the stem surface. An increase of thermal stability of fibres due to the removal of non-cellulosic components along retting is also observed. A separation of bast fibers to elementary fibers occurred with an evolution of chemical composition (degradation of pectins) and a rapid decrease in tensile properties (380MPa to 170MPa after 3 weeks) due to accelerated retting process. The influence of controlled retting on the biocomposite material (PP / hemp fibers) properties is under investigation.

Keywords: controlled retting, hemp fibre, mechanical properties, thermal stability

Procedia PDF Downloads 130

Authors: Ahmad Idris, Bishir Kado, Murtala Umar, Armaya`u Suleiman Labo

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Superpave is the short form of Superior Performing Asphalt Pavement and represents a basis for specifying component materials, asphalt mixture design and analysis, and pavement performance prediction. This new technology is the result of long research projects conducted by the strategic Highway Research program (SHRP) of the Federal Highway Administration. This research was aimed at examining the suitability of Aggregates found in Kano for used in Superpave design method. Aggregates samples were collected from different sources in Kano Nigeria and their Engineering properties, as they relate to the SUPERPAVE design requirements were determined. The average result of Coarse Aggregate Angularity in Kano was found to be 87% and 86% of one fractured face and two or more fractured faces respectively with a standard of 80% and 85% respectively. Fine Aggregate Angularity average result was found to be 47% with a requirement of 45% minimum. A flat and elongated particle which was found to be 10% has a maximum criterion of 10%. Sand equivalent was found to be 51% with the criteria of 45% minimum. Strength tests were also carried out, and the results reflect the requirements of the standards. The tests include Impact value test, Aggregate crushing value, and Aggregate Abrasion tests and the results are 27.5%, 26.7%, and 13%, respectively, with the maximum criteria of 30%. Specific gravity was also carried out and the result was found to have an average value of 2.52 with a criterion of 2.6 to 2.9 and Water absorption was found to be 1.41% with maximum criteria of 0.6%. From the study, the result of the tests indicated that the aggregates properties has met the requirements of Superpave design method based on the specifications of ASTMD 5821, ASTM D 4791, AASHTO T176, AASHTO T33 and BS815.

Keywords: Superpave, aggregates, asphalt mix, Kano

Procedia PDF Downloads 370

Authors: Kwok W. Mui, Ling T. Wong, Chi T. Cheung

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Cooling energy consumption in the residential sector, different from shopping mall, office or commercial buildings, is significantly subject to occupant decisions where in-depth investigations are found limited. It shows that energy consumptions could be associated with housing types. Surveys have been conducted in existing Hong Kong public housings to understand the housing characteristics, apartment electricity demands, occupant’s thermal expectations, and air–conditioning usage patterns for further cooling energy-saving assessments. The aim of this study is to develop a hybrid cooling energy prediction model, which integrated by EnergyPlus (EP) and artificial neural network (ANN) to estimate cooling energy consumption in public residential sector. Sensitivity tests are conducted to find out the energy impacts with changing building parameters regarding to external wall and window material selection, window size reduction, shading extension, building orientation and apartment size control respectively. Assessments are performed to investigate the relationships between cooling demands and occupant behavior on thermal environment criteria and air-conditioning operation patterns. The results are summarized into a cooling energy calculator for layman use to enhance the cooling energy saving awareness in their own living environment. The findings can be used as a directory framework for future cooling energy evaluation in residential buildings, especially focus on the occupant behavioral air–conditioning operation and criteria of energy-saving incentives.

Keywords: artificial neural network, cooling energy, occupant behavior, residential buildings, thermal environment

Procedia PDF Downloads 134

Authors: Yeaji Seok, Myung Kyung Lee

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Background: Even if patients survive after a stroke, stroke patients may experience disability in mobility, sensation, cognition, and speech and language. Stroke patients require rehabilitation for functional recovery and daily life for a considerable time. During rehabilitation, the role of caregivers is important. However, the stroke patients’ quality of life may deteriorate due to family caregivers’ non-preparedness and increased role burden. Purpose: To investigate the prediction of caregivers' preparedness and role burden on stroke patients’ quality of life. Methods: The target population was stroke patients who were hospitalized for rehabilitation and their family care providers. A total of 153 patient-family caregiver dyads were recruited from June to August 2021. Data were collected from self-reported questionnaires and analyzed using descriptive statistics, t-tests, chi-squared test, one-way analysis of variance, Pearson’s correlation coefficients, and multiple regression with SPSS statistics 28 programs. Results: Family caregivers’ preparedness affected stroke patients’ mobility (β = .20, p < 0.05) and character (β = -.084, p < 0.05) and production activities (β = -.197, p < 0.05) in quality of life. The role burden of family caregivers affected language skills (β = .310, p<0.05), visual functions (β=-.357, p < 0.05), thinking skills (β = 0.443, p = 0.05), mood conditions (β = 0.565, p < 0.001), family roles (β = -0.361, p < 0.001), and social roles (β = -0.304, p < 0.001), while the caregivers’ burden of performing self-protection negatively affected patients’ social roles (β = .180, p=.048). In addition, caregivers’ role burden of personal life sacrifice affected patients’ mobility (β = .311, p < 0.05), self-care (β =.232, p < 0.05) and energy (β = .239, p < 0.05). Conclusion: This study indicated that family caregivers' preparedness and role burden affected stroke patients’ quality of life. The results of this study suggested that intervention to improve family caregivers’ preparedness and to reduce role burden should be required for quality of life in stroke patients.

Keywords: quality of life, preparedness, role burden, caregivers, stroke

Procedia PDF Downloads 180

Authors: Suwapitch Chalongkulasak, Teerasak E-Kobon, Pramote Chumnanpuen

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Acne vulgaris is a common skin disease mainly caused by the Gram–positive pathogenic bacterium, Propionibacterium acnes. This bacterium stimulates inflammation process in human sebaceous glands. Giant African snail (Achatina fulica) is alien species that rapidly reproduces and seriously damages agricultural products in Thailand. There were several research reports on the medical and pharmaceutical benefits of this snail mucus peptides and proteins. This study aimed to in silico predict multifunctional bioactive peptides from A. fulica mucus peptidome using several bioinformatic tools for determination of antimicrobial (iAMPpred), anti–biofilm (dPABBs), cytotoxic (Toxinpred), cell membrane penetrating (CPPpred) and anti–quorum sensing (QSPpred) peptides. Three candidate peptides with the highest predictive score were selected and re-designed/modified to improve the required activities. Structural and physicochemical properties of six anti–P. acnes (APA) peptide candidates were performed by PEP–FOLD3 program and the five aforementioned tools. All candidates had random coiled structure and were named as APA1–ori, APA2–ori, APA3–ori, APA1–mod, APA2–mod and APA3–mod. To validate the APA activity, these peptide candidates were synthesized and tested against six isolates of P. acnes. The modified APA peptides showed high APA activity on some isolates. Therefore, our biomimetic mucus peptides could be useful for preventing acne vulgaris and further examined on other activities important to medical and pharmaceutical applications.

Keywords: Propionibacterium acnes, Achatina fulica, peptidomes, antibacterial peptides, snail mucus

Procedia PDF Downloads 109

Authors: Iftekhar Hassan, Sayedil Morsalin, Easir A Khan

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Accidents involving fire occur frequently in recent times and their causes showing a great deal of variety which require intervention methods and risk assessment strategies are unique in each case. On September 4, 2020, a fire and explosion occurred in a confined space caused by a methane gas leak from an underground pipeline in Baitus Salat Jame mosque during Night (Esha) prayer in Narayanganj District, Bangladesh that killed 34 people. In this research, this incident is simulated using Fire Dynamics Simulator (FDS) software to analyze and understand the nature of the accident and associated consequences. FDS is an advanced computational fluid dynamics (CFD) system of fire-driven fluid flow which solves numerically a large eddy simulation form of the Navier–Stokes’s equations for simulation of the fire and smoke spread and prediction of thermal radiation, toxic substances concentrations and other relevant parameters of fire. This study focuses on understanding the nature of the fire and consequence evaluation due to thermal radiation caused by vapor cloud explosion. An evacuation modeling was constructed to visualize the effect of evacuation time and fractional effective dose (FED) for different types of agents. The results were presented by 3D animation, sliced pictures and graphical representation to understand fire hazards caused by thermal radiation or smoke due to vapor cloud explosion. This study will help to design and develop appropriate respond strategy for preventing similar accidents.

Keywords: consequence modeling, fire and explosion, fire dynamics simulation (FDS), thermal radiation

Procedia PDF Downloads 196

Authors: Bernard Igoche Igoche, Olumuyiwa Matthew, Peter Bednar, Alexander Gegov

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This study presents an ontology-driven approach for knowledge discovery and validation from admission databases in Nigerian polytechnic institutions. The research aims to address the challenges of extracting meaningful insights from vast amounts of admission data and utilizing them for decision-making and process improvement. The proposed methodology combines the knowledge discovery in databases (KDD) process with a structural causal model (SCM) ontological framework. The admission database of Benue State Polytechnic Ugbokolo (Benpoly) is used as a case study. The KDD process is employed to mine and distill knowledge from the database, while the SCM ontology is designed to identify and validate the important features of the admission process. The SCM validation is performed using the conditional independence test (CIT) criteria, and an algorithm is developed to implement the validation process. The identified features are then used for machine learning (ML) modeling and prediction of admission status. The results demonstrate the adequacy of the SCM ontological framework in representing the admission process and the high predictive accuracies achieved by the ML models, with k-nearest neighbors (KNN) and support vector machine (SVM) achieving 92% accuracy. The study concludes that the proposed ontology-driven approach contributes to the advancement of educational data mining and provides a foundation for future research in this domain.

Keywords: admission databases, educational data mining, machine learning, ontology-driven knowledge discovery, polytechnic education, structural causal model

Procedia PDF Downloads 27

Authors: Mukesh Khadka, Alexander Paz, Hanns de la Fuente-Mella

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Typically, pavement performance models are developed in two steps: (i) pavement segments with similar characteristics are grouped together to form a cluster, and (ii) the corresponding performance models are developed using statistical techniques. A challenge is to select the characteristics that define clusters and the segments associated with them. If inappropriate characteristics are used, clusters may include homogeneous segments with different performance behavior or heterogeneous segments with similar performance behavior. Prediction accuracy of performance models can be improved by grouping the pavement segments into more uniform clusters by including both characteristics and a performance measure. This grouping is not always possible due to limited information. It is impractical to include all the potential significant factors because some of them are potentially unobserved or difficult to measure. Historical performance of pavement segments could be used as a proxy to incorporate the effect of the missing potential significant factors in clustering process. The current state-of-the-art proposes Clusterwise Linear Regression (CLR) to determine the pavement clusters and the associated performance models simultaneously. CLR incorporates the effect of significant factors as well as a performance measure. In this study, a mathematical program was formulated for CLR models including multiple explanatory variables. Pavement data collected recently over the entire state of Nevada were used. International Roughness Index (IRI) was used as a pavement performance measure because it serves as a unified standard that is widely accepted for evaluating pavement performance, especially in terms of riding quality. Results illustrate the advantage of the using CLR. Previous studies have used CLR along with experimental data. This study uses actual field data collected across a variety of environmental, traffic, design, and construction and maintenance conditions.

Keywords: clusterwise regression, pavement management system, performance model, optimization

Procedia PDF Downloads 227

Authors: Lujain Khraiba

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Recently, there is a broad recognition that micro-climate conditions contribute to the quality of life in urban spaces outdoors, both from economical and social viewpoints. The consideration of urban micro-climate and outdoor thermal comfort in urban design and planning processes has become one of the important aspects in current related studies. However, these aspects are so far not considered in urban planning regulations in practice and these regulations are often poorly adapted to the local climate and culture. Therefore, there is a huge need to adapt the existing planning regulations to the local climate especially in cities that have extremely hot weather conditions. The overall aim of this study is to point out the complexity of the relationship between urban planning regulations, urban design, micro-climate and outdoor thermal comfort in the hot dry city of Damascus, Syria. The main aim is to investigate the temporal and spatial effects of micro-climate on urban surface temperatures and outdoor thermal comfort in different urban design patterns as a result of urban planning regulations during the extreme summer conditions. In addition, studying different alternatives of how to mitigate the surface temperature and thermal stress is also a part of the aim. The novelty of this study is to highlight the combined effect of urban surface materials and vegetation to develop the thermal environment. This study is based on micro-climate simulations using ENVI-met 3.1. The input data is calibrated according to a micro-climate fieldwork that has been conducted in different urban zones in Damascus. Different urban forms and geometries including the old and the modern parts of Damascus are thermally evaluated. The Physiological Equivalent Temperature (PET) index is used as an indicator for outdoor thermal comfort analysis. The study highlights the shortcomings of existing planning regulations in terms of solar protection especially at street levels. The results show that the surface temperatures in Old Damascus are lower than in the modern part. This is basically due to the difference in urban geometries that prevent the solar radiation in Old Damascus to reach the ground and heat up the surface whereas in modern Damascus, the streets are prescribed as wide spaces with high values of Sky View Factor (SVF is about 0.7). Moreover, the canyons in the old part are paved in cobblestones whereas the asphalt is the main material used in the streets of modern Damascus. Furthermore, Old Damascus is less stressful than the modern part (the difference in PET index is about 10 °C). The thermal situation is enhanced when different vegetation are considered (an improvement of 13 °C in the surface temperature is recorded in modern Damascus). The study recommends considering a detailed landscape code at street levels to be integrated in urban regulations of Damascus in order to achieve a better urban development in harmony with micro-climate and comfort. Such strategy will be very useful to decrease the urban warming in the city.

Keywords: micro-climate, outdoor thermal comfort, urban planning regulations, urban spaces

Procedia PDF Downloads 459

Authors: Albert Suryaprawira

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The Nasion-Sella Line (NSL) has been used for several years as a reference line in longitudinal growth study. Therefore this line is considered to be stable not only to evaluate treatment outcome and to predict relapse possibility but also to manage prognosis. This is a radiographic superimposition of an adult male aged 19 years who complained of difficulty in aesthetic, talking and chewing. Patient has a midface hypoplasia profile (concave). He was diagnosed to have a severe Skeletal Class III with Class III malocclusion, increased lower vertical height, and an anterior open bite. A pre-treatment cephalometric radiograph was taken to analyse the skeletal problem and to measure the amount of bone movement and the prediction soft tissue response. A panoramic radiograph was also taken to analyse bone quality, bone abnormality, third molar impaction, etc. Before the surgery, a pre-surgical cephalometric radiograph was taken to re-evaluate the plan and to settle the final amount of bone cut. After the surgery, a post-surgical cephalometric radiograph was taken to confirm the result with the plan. The superimposition using NSL as a reference line between those radiographs was performed to analyse the outcome. It is important to describe the amount of hard and soft tissue movement and to predict the possibility of relapse after the surgery. The patient also needs to understand all the surgical plan, outcome and relapse prevention. The surgical management included maxillary impaction and advancement of Le Fort I osteotomy. The evaluation using NSL as a reference was a very useful method in determining the outcome and prognosis.

Keywords: Nasion-Sella Line, midface hypoplasia, Le Fort 1, maxillary advancement

Procedia PDF Downloads 122

Authors: Shidvash Vakilipour, Masoud Mohammadi, Rouzbeh Riazi, Scott Ormiston, Kimia Amiri, Sahar Barati

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An essential component of a finite volume method (FVM) is the advection scheme that estimates values on the cell faces based on the calculated values on the nodes or cell centers. The most widely used advection schemes are upwind schemes. These schemes have been developed in FVM on different kinds of structured and unstructured grids. In this research, the physical influence scheme (PIS) is developed for a cell-centered FVM that uses an implicit coupled solver. Results are compared with the exponential differencing scheme (EDS) and the skew upwind differencing scheme (SUDS). Accuracy of these schemes is evaluated for a lid-driven cavity flow at Re = 1000, 3200, and 5000 and a backward-facing step flow at Re = 800. Simulations show considerable differences between the results of EDS scheme with benchmarks, especially for the lid-driven cavity flow at high Reynolds numbers. These differences occur due to false diffusion. Comparing SUDS and PIS schemes shows relatively close results for the backward-facing step flow and different results in lid-driven cavity flow. The poor results of SUDS in the lid-driven cavity flow can be related to its lack of sensitivity to the pressure difference between cell face and upwind points, which is critical for the prediction of such vortex dominant flows.

Keywords: cell-centered finite volume method, coupled solver, exponential differencing scheme (EDS), physical influence scheme (PIS), pressure weighted interpolation method (PWIM), skew upwind differencing scheme (SUDS)

Procedia PDF Downloads 250

Authors: Rafael Zhindon Almeida

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Surface water quality is an important concern for the evaluation and prediction of water quality conditions. The objective of this study is to develop a statistical model that can accurately predict the water quality of the El Macho estuary in the city of Machala, El Oro province. The methodology employed in this study is of a basic type that involves a thorough search for theoretical foundations to improve the understanding of statistical modeling for water quality analysis. The research design is correlational, using a multivariate statistical model involving multiple linear regression and principal component analysis. The results indicate that water quality parameters such as fecal coliforms, biochemical oxygen demand, chemical oxygen demand, iron and dissolved oxygen exceed the allowable limits. The water of the El Macho estuary is determined to be below the required water quality criteria. The multiple linear regression model, based on chemical oxygen demand and total dissolved solids, explains 99.9% of the variance of the dependent variable. In addition, principal component analysis shows that the model has an explanatory power of 86.242%. The study successfully developed a statistical model to evaluate the water quality of the El Macho estuary. The estuary did not meet the water quality criteria, with several parameters exceeding the allowable limits. The multiple linear regression model and principal component analysis provide valuable information on the relationship between the various water quality parameters. The findings of the study emphasize the need for immediate action to improve the water quality of the El Macho estuary to ensure the preservation and protection of this valuable natural resource.

Keywords: statistical modeling, water quality, multiple linear regression, principal components, statistical models

Procedia PDF Downloads 56

Authors: F. Q. Shao, W. Q. Wang, Y. Yin, T. P. Yu, D. B. Zou, J. M. Ouyang

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The Radiation Pressure Acceleration (RPA) mechanism is very promising in laser-driven ion acceleration because of high laser-ion energy conversion efficiency. Although some experiments have shown the characteristics of RPA, the energy of ions is quite limited. The ion energy obtained in experiments is only several MeV/u, which is much lower than theoretical prediction. One possible limiting factor is the transverse instability incited in the RPA process. The transverse instability is basically considered as the Rayleigh-Taylor (RT) instability, which is a kind of interfacial instability and occurs when a light fluid pushes against a heavy fluid. Multi-dimensional particle-in-cell (PIC) simulations show that the onset of transverse instability will destroy the acceleration process and broaden the energy spectrum of fast ions during the RPA dominant ion acceleration processes. The evidence of the RT instability driven by radiation pressure has been observed in a laser-foil interaction experiment in a typical RPA regime, and the dominant scale of RT instability is close to the laser wavelength. The development of transverse instability in the radiation-pressure-acceleration dominant laser-foil interaction is numerically examined by two-dimensional particle-in-cell simulations. When a laser interacts with a foil with modulated surface, the internal instability is quickly incited and it develops. The linear growth and saturation of the transverse instability are observed, and the growth rate is numerically diagnosed. In order to optimize interaction parameters, a method of information entropy is put forward to describe the chaotic degree of the transverse instability. With moderate modulation, the transverse instability shows a low chaotic degree and a quasi-monoenergetic proton beam is produced.

Keywords: information entropy, radiation pressure acceleration, Rayleigh-Taylor instability, transverse instability

Procedia PDF Downloads 320

Authors: Varis Ransibrahmanakul, Doug Pirhalla, Scott Sheridan, Cameron Lee

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U.S. coastal areas and ecosystems are facing multiple sea level rise threats and effects: heavy rain events, cyclones, and changing wind and weather patterns all influence coastal flooding, sedimentation, and erosion along critical barrier islands and can strongly impact habitat resiliency and water quality in protected habitats. These impacts are increasing over time and have accelerated the need for new tracking techniques, models and tools of flood risk to support enhanced preparedness for coastal management and mitigation. To address this issue, NOAA National Ocean Service (NOS) evaluated new metrics from satellite altimetry AVISO/Copernicus and MODIS IR flood extents to isolate nodes atmospheric variability indicative of elevated sea level and nuisance flood events. Using de-trended time series of cross-shelf sea surface heights (SSH), we identified specific Self Organizing Maps (SOM) nodes and transitions having a strongest regional association with oceanic spatial patterns (e.g., heightened downwelling favorable wind-stress and enhanced southward coastal transport) indicative of elevated coastal sea levels. Results show the impacts of the inverted barometer effect as well as the effects of surface wind forcing; Ekman-induced transport along broad expanses of the U.S. eastern coastline. Higher sea levels and corresponding localized flooding are associated with either pattern indicative of enhanced on-shore flow, deepening cyclones, or local- scale winds, generally coupled with an increased local to regional precipitation. These findings will support an integration of satellite products and will inform seasonal outlook model development supported through NOAAs Climate Program Office and NOS office of Center for Operational Oceanographic Products and Services (CO-OPS). Overall results will prioritize ecological areas and coastal lab facilities at risk based on numbers of nuisance flood projected and inform coastal management of flood risk around low lying areas subjected to bank erosion.

Keywords: AVISO satellite altimetry SSHA, MODIS IR flood map, nuisance flood, remote sensing of flood

Procedia PDF Downloads 118

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: Pranav Ajmera, Amit Kharat, Tanveer Gupte, Richa Pant, Viraj Kulkarni, Vinay Duddalwar, Purnachandra Lamghare

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The cardiothoracic ratio (CTR) is the ratio of the diameter of the heart to the diameter of the thorax. An abnormal CTR, that is, a value greater than 0.55, is often an indicator of an underlying pathological condition. The accurate prediction of an abnormal CTR from chest X-rays (CXRs) aids in the early diagnosis of clinical conditions. We propose a deep learning-based model for automatic CTR calculation that can assist the radiologist with the diagnosis of cardiomegaly and optimize the radiology flow. The study population included 1012 posteroanterior (PA) CXRs from a single institution. The Attention U-Net deep learning (DL) architecture was used for the automatic calculation of CTR. A CTR of 0.55 was used as a cut-off to categorize the condition as cardiomegaly present or absent. An observer performance test was conducted to assess the radiologist's performance in diagnosing cardiomegaly with and without artificial intelligence (AI) assistance. The Attention U-Net model was highly specific in calculating the CTR. The model exhibited a sensitivity of 0.80 [95% CI: 0.75, 0.85], precision of 0.99 [95% CI: 0.98, 1], and a F1 score of 0.88 [95% CI: 0.85, 0.91]. During the analysis, we observed that 51 out of 1012 samples were misclassified by the model when compared to annotations made by the expert radiologist. We further observed that the sensitivity of the reviewing radiologist in identifying cardiomegaly increased from 40.50% to 88.4% when aided by the AI-generated CTR. Our segmentation-based AI model demonstrated high specificity and sensitivity for CTR calculation. The performance of the radiologist on the observer performance test improved significantly with AI assistance. A DL-based segmentation model for rapid quantification of CTR can therefore have significant potential to be used in clinical workflows.

Keywords: cardiomegaly, deep learning, chest radiograph, artificial intelligence, cardiothoracic ratio

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Authors: Wenbo Wang, Yi-Fang Brook Wu

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The spread of misinformation and disinformation has become a major concern, particularly with the rise of social media as a primary source of information for many people. As a means to address this phenomenon, automated fact-checking has emerged as a safeguard against the spread of misinformation and disinformation. Existing fact-checking approaches aim to determine whether a news claim is true or false, and they have achieved decent veracity prediction accuracy. However, the state-of-the-art methods rely on manually verified external information to assist the checking model in making judgments, which requires significant human resources. This study introduces a framework, SAC, which focuses on 1) augmenting the representation of a claim by incorporating additional context using general-purpose, comprehensive, and authoritative data; 2) developing a search function to automatically select relevant, new, and credible references; 3) focusing on the important parts of the representations of a claim and its reference that are most relevant to the fact-checking task. The experimental results demonstrate that 1) Augmenting the representations of claims and references through the use of a knowledge base, combined with the multi-head attention technique, contributes to improved performance of fact-checking. 2) SAC with auto-selected references outperforms existing fact-checking approaches with manual selected references. Future directions of this study include I) exploring knowledge graphs in Wikidata to dynamically augment the representations of claims and references without introducing too much noise, II) exploring semantic relations in claims and references to further enhance fact-checking.

Keywords: fact checking, claim verification, deep learning, natural language processing

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Authors: Namwinwelbere Dabire, Eugene C. Ezin, Adandedji M. Firmin

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The prediction of hydrological flows (rainfall-depth or rainfall-discharge) is becoming increasingly important in the management of hydrological risks such as floods. In this study, the Long Short-Term Memory (LSTM) network, a state-of-the-art algorithm dedicated to time series, is applied to predict the daily water level of Nokoue Lake in Benin. This paper aims to provide an effective and reliable method enable of reproducing the future daily water level of Nokoue Lake, which is influenced by a combination of two phenomena: rainfall and river flow (runoff from the Ouémé River, the Sô River, the Porto-Novo lagoon, and the Atlantic Ocean). Performance analysis based on the forecasting horizon indicates that LSTM can predict the water level of Nokoué Lake up to a forecast horizon of t+10 days. Performance metrics such as Root Mean Square Error (RMSE), coefficient of correlation (R²), Nash-Sutcliffe Efficiency (NSE), and Mean Absolute Error (MAE) agree on a forecast horizon of up to t+3 days. The values of these metrics remain stable for forecast horizons of t+1 days, t+2 days, and t+3 days. The values of R² and NSE are greater than 0.97 during the training and testing phases in the Nokoué Lake basin. Based on the evaluation indices used to assess the model's performance for the appropriate forecast horizon of water level in the Nokoué Lake basin, the forecast horizon of t+3 days is chosen for predicting future daily water levels.

Keywords: forecasting, long short-term memory cell, recurrent artificial neural network, Nokoué lake

Procedia PDF Downloads 33

Authors: Shahad Nagoor, Lucy Hederman, Kevin Koidl, Annalina Caputo

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Doctors’ notes reflect their impressions, attitudes, clinical sense, and opinions about patients’ conditions and progress, and other information that is essential for doctors’ daily clinical decisions. Despite their value, clinical notes are insufficiently researched within the language processing community. Automatically extracting information from unstructured text data is known to be a difficult task as opposed to dealing with structured information such as vital physiological signs, images, and laboratory results. The aim of this research is to investigate how Natural Language Processing (NLP) techniques and machine learning techniques applied to clinician notes can assist in doctors’ decision-making in Intensive Care Unit (ICU) for coronavirus disease 2019 (COVID-19) patients. The hypothesis is that clinical outcomes like survival or mortality can be useful in influencing the judgement of clinical sentiment in ICU clinical notes. This paper introduces two contributions: first, we introduce COVID_ICU_BERT, a fine-tuned version of clinical transformer models that can reliably predict clinical sentiment for notes of COVID patients in the ICU. We train the model on clinical notes for COVID-19 patients, a type of notes that were not previously seen by clinicalBERT, and Bio_Discharge_Summary_BERT. The model, which was based on clinicalBERT achieves higher predictive accuracy (Acc 93.33%, AUC 0.98, and precision 0.96 ). Second, we perform data augmentation using clinical contextual word embedding that is based on a pre-trained clinical model to balance the samples in each class in the data (survived vs. deceased patients). Data augmentation improves the accuracy of prediction slightly (Acc 96.67%, AUC 0.98, and precision 0.92 ).

Keywords: BERT fine-tuning, clinical sentiment, COVID-19, data augmentation

Procedia PDF Downloads 171

Authors: Gizem Eser Erdek

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This study is research on predicting the remaining life of industrial cutting tools used in the industrial production process with deep learning methods. When the life of cutting tools decreases, they cause destruction to the raw material they are processing. This study it is aimed to predict the remaining life of the cutting tool based on the damage caused by the cutting tools to the raw material. For this, hole photos were collected from the hole-drilling machine for 8 months. Photos were labeled in 5 classes according to hole quality. In this way, the problem was transformed into a classification problem. Using the prepared data set, a model was created with convolutional neural networks, which is a deep learning method. In addition, VGGNet and ResNet architectures, which have been successful in the literature, have been tested on the data set. A hybrid model using convolutional neural networks and support vector machines is also used for comparison. When all models are compared, it has been determined that the model in which convolutional neural networks are used gives successful results of a %74 accuracy rate. In the preliminary studies, the data set was arranged to include only the best and worst classes, and the study gave ~93% accuracy when the binary classification model was applied. The results of this study showed that the remaining life of the cutting tools could be predicted by deep learning methods based on the damage to the raw material. Experiments have proven that deep learning methods can be used as an alternative for cutting tool life estimation.

Keywords: classification, convolutional neural network, deep learning, remaining life of industrial cutting tools, ResNet, support vector machine, VggNet

Procedia PDF Downloads 47

Authors: Fatemeh Deldarabdolmaleki, Mohamad Fakri Zaky Bin Ja'afar

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Having a daylit space together with view results in a pleasant and productive environment for office employees. A daylit space is a space which utilizes daylight as a basic source of illumination to fulfill user’s visual demands and minimizes the electric energy consumption. Malaysian weather is hot and humid all over the year because of its location in the equatorial belt. however, because most of the commercial buildings in Malaysia are air-conditioned, huge glass windows are normally installed in order to keep the physical and visual relation between inside and outside. As a result of climatic situation and mentioned new trend, an ordinary office has huge heat gain, glare, and discomfort for occupants. Balancing occupant’s comfort and energy conservation in a tropical climate is a real challenge. This study concentrates on evaluating a venetian blind system using per pixel analyzing tools based on the suggested cut-out metrics by the literature. Workplace area in a private office room has been selected as a case study. Eight-day measurement experiment was conducted to investigate the effect of different venetian blind angles in an office area under daylight conditions in Serdang, Malaysia. The study goal was to explore daylight comfort of a commercially available venetian blind system, its’ daylight sufficiency and excess (8:00 AM to 5 PM) as well as Glare examination. Recently developed software, analyzing High Dynamic Range Images (HDRI captured by CCD camera), such as radiance based Evalglare and hdrscope help to investigate luminance-based metrics. The main key factors are illuminance and luminance levels, mean and maximum luminance, daylight glare probability (DGP) and luminance ratio of the selected mask regions. The findings show that in most cases, morning session needs artificial lighting in order to achieve daylight comfort. However, in some conditions (e.g. 10° and 40° slat angles) in the second half of day the workplane illuminance level exceeds the maximum of 2000 lx. Generally, a rising trend is discovered toward mean window luminance and the most unpleasant cases occur after 2 P.M. Considering the luminance criteria rating, the uncomfortable conditions occur in the afternoon session. Surprisingly in no blind condition, extreme case of window/task ratio is not common. Studying the daylight glare probability, there is not any DGP value higher than 0.35 in this experiment.

Keywords: daylighting, energy simulation, office environment, Venetian blind

Procedia PDF Downloads 228

Authors: I. Kurashvili, G. Darsavelidze, T. Kimeridze, G. Chubinidze, I. Tabatadze

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At present, a number of modern semiconductor devices based on SiGe alloys have been created in which the latest achievements of high technologies are used. These devices might cause significant changes to networking, computing, and space technology. In the nearest future new materials based on SiGe will be able to restrict the A³B⁵ and Si technologies and firmly establish themselves in medium frequency electronics. Effective realization of these prospects requires the solution of prediction and controlling of structural state and dynamical physical –mechanical properties of new SiGe materials. Based on these circumstances, a complex investigation of structural defects and structural-sensitive dynamic mechanical characteristics of SiGe alloys under different external impacts (deformation, radiation, thermal cycling) acquires great importance. Internal friction (IF) and shear modulus temperature and amplitude dependences of the monocrystalline boron-doped Si_1-xGe_x(x≤0.05) alloys grown by Czochralski technique is studied in initial and ⁶⁰Co gamma-irradiated states. In the initial samples, a set of dislocation origin relaxation processes and accompanying modulus defects are revealed in a temperature interval of 400-800 ⁰C. It is shown that after gamma-irradiation intensity of relaxation internal friction in the vicinity of 280 ⁰C increases and simultaneously activation parameters of high temperature relaxation processes reveal clear rising. It is proposed that these changes of dynamical mechanical characteristics might be caused by a decrease of the dislocation mobility in the Cottrell atmosphere enriched by the radiation defects.

Keywords: internal friction, shear modulus, gamma-irradiation, SiGe alloys

Procedia PDF Downloads 117

Authors: Pamela Del Rosario, Elisabetta Palumbo, Marzia Traverso

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The building sector plays an important role in addressing pressing environmental issues such as climate change and resource scarcity. The energy performance of buildings is considerably affected by the external envelope. In fact, a considerable proportion of the building energy demand is due to energy losses through the windows. Nevertheless, according to literature, to pay attention only to the contribution of windows to the building energy performance, i.e., their influence on energy use during building operation, could result in a partial evaluation. Hence, it is important to consider not only the building energy performance but also the environmental performance of windows, and this not only during the operational stage but along its complete life cycle. Life Cycle Assessment (LCA) according to ISO 14040:2006 and ISO 14044:2006+A1:2018 is one of the most adopted and robust methods to evaluate the environmental performance of products throughout their complete life cycle. This life-cycle based approach avoids the shift of environmental impacts of a life cycle stage to another, allowing to allocate them to the stage in which they originated and to adopt measures that optimize the environmental performance of the product. Moreover, the LCA method is widely implemented in the construction sector to assess whole buildings as well as construction products and materials. LCA is regulated by the European Standards EN 15978:2011, at the building level, and EN 15804:2012+A2:2019, at the level of construction products and materials. In this work, the environmental performance of wooden windows was assessed by implementing the LCA method and adopting primary data. More specifically, the emphasis is given to embedded and operational impacts. Furthermore, correlations are made between these environmental impacts and aspects such as type of wood and window transmittance. In the particular case of the operational impacts, special attention is set on the definition of suitable maintenance scenarios that consider the potential climate influence on the environmental impacts. For this purpose, a literature review was conducted, and expert consultation was carried out. The study underlined the variability of the embedded environmental impacts of wooden windows by considering different wood types and transmittance values. The results also highlighted the need to define appropriate maintenance scenarios for precise assessment results. It was found that both the service life and the window maintenance requirements in terms of treatment and its frequency are highly dependent not only on the wood type and its treatment during the manufacturing process but also on the weather conditions of the place where the window is installed. In particular, it became evident that maintenance-related environmental impacts were the highest for climate regions with the lowest temperatures and the greatest amount of precipitation.

Keywords: embedded impacts, environmental performance, life cycle assessment, LCA, maintenance stage, operational impacts, wooden windows

Procedia PDF Downloads 206

Authors: Idrissa Kayijuka

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The logistic regression and Cox regression models (proportional hazard model) at present are being employed in the analysis of prospective epidemiologic research looking into risk factors in their application on chronic diseases. However, a theoretical relationship between the two models has been studied. By definition, Cox regression model also called Cox proportional hazard model is a procedure that is used in modeling data regarding time leading up to an event where censored cases exist. Whereas the Logistic regression model is mostly applicable in cases where the independent variables consist of numerical as well as nominal values while the resultant variable is binary (dichotomous). Arguments and findings of many researchers focused on the overview of Cox and Logistic regression models and their different applications in different areas. In this work, the analysis is done on secondary data whose source is SPSS exercise data on BREAST CANCER with a sample size of 1121 women where the main objective is to show the application difference between Cox regression model and logistic regression model based on factors that cause women to die due to breast cancer. Thus we did some analysis manually i.e. on lymph nodes status, and SPSS software helped to analyze the mentioned data. This study found out that there is an application difference between Cox and Logistic regression models which is Cox regression model is used if one wishes to analyze data which also include the follow-up time whereas Logistic regression model analyzes data without follow-up-time. Also, they have measurements of association which is different: hazard ratio and odds ratio for Cox and logistic regression models respectively. A similarity between the two models is that they are both applicable in the prediction of the upshot of a categorical variable i.e. a variable that can accommodate only a restricted number of categories. In conclusion, Cox regression model differs from logistic regression by assessing a rate instead of proportion. The two models can be applied in many other researches since they are suitable methods for analyzing data but the more recommended is the Cox, regression model.

Keywords: logistic regression model, Cox regression model, survival analysis, hazard ratio

Procedia PDF Downloads 426