Search results for: weather forecast

Authors: Emmanuel Yussuf, John Muthama, John Ng'ang'A

Abstract:

There has been increasing noise levels all over the world in the last decade. Many factors contribute to this increase, which is causing health related effects to humans. Developing countries are not left out of the whole picture as they are still growing and advancing their development. Motor vehicles are increasing on urban roads; there is an increase in infrastructure due to the rising population, increasing number of industries to provide goods and so many other activities. All this activities lead to the high noise levels in cities. This study was conducted in Nairobi’s Central Business District (CBD) with the main objective of simulating noise levels in order to understand the noise exposed to the people within the urban area, in relation to weather parameters namely temperature, rainfall and wind field. The study was achieved using the Neighbourhood Proximity Model and Time Series Analysis, with data obtained from proxies/remotely-sensed from satellites, in order to establish the levels of noise exposed to which people of Nairobi CBD are exposed to. The findings showed that there is an increase in temperature (0.1°C per year) and a decrease in precipitation (40 mm per year), which in comparison to the noise levels in the area, are increasing. The study also found out that noise levels exposed to people in Nairobi CBD were roughly between 61 and 63 decibels and has been increasing, a level which is high and likely to cause adverse physical and psychological effects on the human body in which air temperature, precipitation and wind contribute so much in the spread of noise. As a noise reduction measure, the use of sound proof materials in buildings close to busy roads, implementation of strict laws to most emitting sources as well as further research on the study was recommended. The data used for this study ranged from the year 2000 to 2015, rainfall being in millimeters (mm), temperature in degrees Celsius (°C) and the urban form characteristics being in meters (m).

Keywords: simulation, noise exposure, weather, proxy

Procedia PDF Downloads 348

Authors: Cody Stolle, Mojdeh Asadollahipajouh, Khaleb Pafford, Jada Iwuoha, Samantha White, Becky Mueller

Abstract:

Battery-electric vehicles (BEVs) are growing in sales and popularity in the United States as an alternative to traditional internal combustion engine vehicles (ICEVs). BEVs are generally heavier than corresponding models of ICEVs, with large battery packs located beneath the vehicle floorpan, a “skateboard” chassis, and have front and rear crush space available in the trunk and “frunk” or front trunk. The geometrical and frame differences between the vehicles may lead to incompatibilities with gasoline vehicles during vehicle-to-vehicle crashes as well as run-off-road crashes with roadside barriers, which were designed to handle lighter ICEVs with higher centers-of-mass and with dedicated structural chasses. Crash data were collected from 10 states spanning a five-year period between 2017 and 2021. Vehicle Identification Number (VIN) codes were processed with the National Highway Traffic Safety Administration (NHTSA) VIN decoder to extract BEV models from ICEV models. Crashes were filtered to isolate only vehicles produced between 2010 and 2021, and the crash circumstances (weather, time of day, maximum injury) were compared between BEVs and ICEVs. In Washington, 436,613 crashes were identified, which satisfied the selection criteria, and 3,371 of these crashes (0.77%) involved a BEV. The number of crashes which noted a fire were comparable between BEVs and ICEVs of similar model years (0.3% and 0.33%, respectively), and no differences were discernable for the time of day, weather conditions, road geometry, or other prevailing factors (e.g., run-off-road). However, crashes involving BEVs rose rapidly; 31% of all BEV crashes occurred in just 2021. Results indicate that BEVs are performing comparably to ICEVs, and events surrounding BEV crashes are statistically indistinguishable from ICEV crashes.

Keywords: battery-electric vehicles, transportation safety, infrastructure crashworthiness, run-off-road crashes, ev crash data analysis

Procedia PDF Downloads 55

Authors: Victoria Bessonova, Robert Dorrell, Nina Dethlefs, Evdokia Tapoglou, Katharine York

Abstract:

As the global pursuit of renewable energy intensifies, offshore wind farms have emerged as a promising solution to combat climate change. The global offshore wind installed capacity is projected to increase 56-fold by 2055. However, the impacts of climate change, particularly changes in wave climate, are not widely understood. Offshore wind installation and maintenance activities often require specific weather windows, characterized by calm seas and low wave heights, to ensure safe and efficient operations. However, climate change-induced alterations in wave characteristics can reduce the availability of suitable weather windows, leading to delays and disruptions in project timelines. it applied the operational limits of installation and maintenance vessels to past and future climate wave projections. This revealed changes in the annual and monthly accessibility of offshore wind farms at key global development locations. When accessibility is only defined by significant wave height, spatial patterns in the annual accessibility roughly follow changes in significant wave height, with increased availability where significant wave height is decreasing. This resulted in a 1-6% increase in Europe and North America and a similar decrease in South America, Australia and Asia. Monthly changes suggest unchanged or slightly decreased (1-2%) accessibility in summer months and increased (2-6%) in winter. Further assessment includes assessing the sensitivity of accessibility to operational limits defined by wave height combined with wave period and wave height combined with wind speed. Results of this assessment will be included in the presentation. These findings will help stakeholders inform climate change adaptations in installation and maintenance planning practices.

Keywords: climate change, offshore wind, offshore wind installation, operations and maintenance, wave climate, wind farm accessibility

Procedia PDF Downloads 48

Authors: Yi-Yu Huang, Chien-Kuo Wang, Sreerag Chota Veettil, Hang Zhang, Hu Yike

Abstract:

Regarding the pressing issue of reducing energy consumption and carbon footprint of buildings, past research has focused more on analyzing the thermal performance of the extensive terrestrial green roofs with sedum plants in summer. However, the disadvantages of this type of green roof are relatively limited thermal performance, low extreme weather adaptability, relatively higher demands in maintenance, and lower added value in healing landscape. In view of this, this research aims to develop the extensive wetland green roofs with higher thermal performance, high extreme weather adaptability, low demands in maintenance, and high added value in healing landscape, and to measure its thermal performance for buildings in winter. The following factors are considered including the type and mixing formula of growth medium (light weight soil, akadama, creek gravel, pure water) and the type of aquatic plants. The research adopts a four-stage field experiment conducting on the rooftop of a building in a humid subtropical climate. The results found that emergent (Roundleaf rotala), submerged (Ribbon weed), floating-leaved (Water lily) wetland green roofs had similar thermal performance, and superior over wetland green roof without plant, traditional terrestrial green roof (without plant), and pure water green roof (without plant, nighttime only) in terms of overall passive cooling (8.00C) and thermal insulation (4.50C) effects as well as a reduction in heat amplitude (77-85%) in winter in a humid subtropical climate. The thermal performance of the free-floating (Water hyacinth) wetland green roof is inferior to that of the other three types of wetland green roofs, whether in daytime or nighttime.

Keywords: thermal performance, extensive wetland green roof, Aquatic plant, Winter , Humid subtropical climate

Procedia PDF Downloads 141

Authors: Jamal Almatawah, Hamad Matar, Abdulsalam Altemeemi

Abstract:

The World Health Organization (WHO) has recognized environmental noise as harmful pollution that causes adverse psychosocial and physiologic effects on human health. The motor vehicle is considered to be one of the main source of noise pollution. It is a universal phenomenon, and it has grown to the point that it has become a major concern for both the public and policymakers. The aim of this paper, therefore, is to investigate the Traffic noise levels and the contributing factors that affect its level, such as traffic volume, heavy-vehicle Speed and other metrological factors in Ishbiliyah as a sample of a residential area in Kuwait. Three types of roads were selected in Ishbiliyah expressway, major arterial and collector street. The other source of noise that interferes the traffic noise has also been considered in this study. Traffic noise level is measured and analyzed using the Bruel & Kjaer outdoor sound level meter 2250-L (2250 Light). The Count-Cam2 Video Camera has been used to collect the peak and off-peak traffic count. Ambient Weather WM-5 Handheld Weather Station is used for metrological factors such as temperature, humidity and wind speed. Also, the spot speed was obtained using the radar speed: Decatur Genesis model GHD-KPH. All the measurement has been detected at the same time (simultaneously). The results showed that the traffic noise level is over the allowable limit on all types of roads. The average equivalent noise level (LAeq) for the Expressway, Major arterial and Collector Street was 74.3 dB(A), 70.47 dB(A) and 60.84 dB(A), respectively. In addition, a Positive Correlation coefficient between the traffic noise versus traffic volume and between traffic noise versus 85th percentile speed was obtained. However, there was no significant relation and Metrological factors. Abnormal vehicle noise due to poor maintenance or user-enhanced exhaust noise was found to be one of the highest factors that affected the overall traffic noise reading.

Keywords: traffic noise, residential area, pollution, vehicle noise

Procedia PDF Downloads 34

Authors: Melike Sultan Karasu Asnaz, Ayse Ozdogan Dolcek

Abstract:

Crop drying, which aims to reduce the moisture content to a certain level, is a method used to extend the shelf life and prevent it from spoiling. One of the oldest food preservation techniques is open sunor shade drying. Even though this technique is the most affordable of all drying methods, there are some drawbacks such as contamination by insects, environmental pollution, windborne dust, and direct expose to weather conditions such as wind, rain, hail. However, solar dryers that provide a hygienic and controllable environment to preserve food and extend its shelf life have been developed and used to dry agricultural products. Thus, foods can be dried quickly without being affected by weather variables, and quality products can be obtained. This research is mainly devoted to investigating the modelling of drying kinetics of cantaloupe in a forced convection solar dryer. Mathematical models for the drying process should be defined to simulate the drying behavior of the foodstuff, which will greatly contribute to the development of solar dryer designs. Thus, drying experiments were conducted and replicated five times, and various data such as temperature, relative humidity, solar irradiation, drying air speed, and weight were instantly monitored and recorded. Moisture content of sliced and pretreated cantaloupe were converted into moisture ratio and then fitted against drying time for constructing drying curves. Then, 10 quasi-theoretical and empirical drying models were applied to find the best drying curve equation according to the Levenberg-Marquardt nonlinear optimization method. The best fitted mathematical drying model was selected according to the highest coefficient of determination (R²), and the mean square of the deviations (χ^²) and root mean square error (RMSE) criterial. The best fitted model was utilized to simulate a thin layer solar drying of cantaloupe, and the simulation results were compared with the experimental data for validation purposes.

Keywords: solar dryer, mathematical modelling, drying kinetics, cantaloupe drying

Procedia PDF Downloads 88

Authors: Devansh Desai, Rahul Nigam

Abstract:

Crop water stress (W) at a given growth stage starts to set in as moisture availability (M) to roots falls below 75% of maximum. It has been found that ratio of crop evapotranspiration (ET) and reference evapotranspiration (ET0) is an indicator of moisture adequacy and is strongly correlated with ‘M’ and ‘W’. The spatial variability of ET0 is generally less over an agricultural farm of 1-5 ha than ET, which depends on both surface and atmospheric conditions, while the former depends only on atmospheric conditions. Solutions from surface energy balance (SEB) and thermal infrared (TIR) remote sensing are now known to estimate latent heat flux of ET. In the present study, ET and moisture adequacy index (MAI) (=ET/ET0) have been estimated over two contrasting western India agricultural farms having rice-wheat system in semi-arid climate and arid grassland system, limited by moisture availability. High-resolution multi-band TIR sensing observations at 65m from ECOSTRESS (ECOsystemSpaceborne Thermal Radiometer Experiment on Space Station) instrument on-board International Space Station (ISS) were used in an analytical SEB model, STIC (Surface Temperature Initiated Closure) to estimate ET and MAI. The ancillary variables used in the ET modeling and MAI estimation were land surface albedo, NDVI from close-by LANDSAT data at 30m spatial resolution, ET0 product at 4km spatial resolution from INSAT 3D, meteorological forcing variables from short-range weather forecast on air temperature and relative humidity from NWP model. Farm-scale ET estimates at 65m spatial resolution were found to show low RMSE of 16.6% to 17.5% with R2 >0.8 from 18 datasets as compared to reported errors (25 – 30%) from coarser-scale ET at 1 to 8 km spatial resolution when compared to in situ measurements from eddy covariance systems. The MAI was found to show lower (<0.25) and higher (>0.5) magnitudes in the contrasting agricultural farms. The study showed the potential need of high-resolution high-repeat spaceborne multi-band TIR payloads alongwith optical payload in estimating farm-scale ET and MAI for estimating consumptive water use and water stress. A set of future high-resolution multi-band TIR sensors are planned on-board Indo-French TRISHNA, ESA’s LSTM, NASA’s SBG space-borne missions to address sustainable irrigation water management at farm-scale to improve crop water productivity. These will provide precise and fundamental variables of surface energy balance such as LST (Land Surface Temperature), surface emissivity, albedo and NDVI. A synchronization among these missions is needed in terms of observations, algorithms, product definitions, calibration-validation experiments and downstream applications to maximize the potential benefits.

Keywords: thermal remote sensing, land surface temperature, crop water stress, evapotranspiration

Procedia PDF Downloads 43

Authors: Meimei Zheng

Abstract:

Product personalization could not only bring new profits to companies but also provide the direction of long-term development for companies. However, the characteristics of personalized product cause some new problems. This paper investigates how companies make decisions on the supply of personalized products when facing different customer attitudes to personalized product and service, constraints due to limited capacity and updates of personalized demand information. This study will provide optimal decisions for companies to develop personalized markets, resulting in promoting business transformation and improving business competitiveness.

Keywords: demand forecast updating, limited capacity, personalized products, optimization

Procedia PDF Downloads 228

Authors: Valeria Bondarenko

Abstract:

We proposed algorithms for: estimation of parameters fBm (volatility and Hurst exponent) and for the approximation of random time series by functional of fBm. We proved the consistency of the estimators, which constitute the above algorithms, and proved the optimal forecast of approximated time series. The adequacy of estimation algorithms, approximation, and forecasting is proved by numerical experiment. During the process of creating software, the system has been created, which is displayed by the hierarchical structure. The comparative analysis of proposed algorithms with the other methods gives evidence of the advantage of approximation method. The results can be used to develop methods for the analysis and modeling of time series describing the economic, physical, biological and other processes.

Keywords: mathematical model, random process, Wiener process, fractional Brownian motion

Procedia PDF Downloads 323

Authors: Lei Ren, Michael Hartnett, Stephen Nash

Abstract:

The abundant measured surface current from HF radar system in coastal area is assimilated into model to improve the modeling forecasting ability. A simple sequential data assimilation scheme, Direct Insertion (DI), is applied to update model forecast states. The influence of Direct Insertion data assimilation over time is analyzed at one reference point. Vector maps of surface current from models are compared with HF radar measurements. Root-Mean-Squared-Error (RMSE) between modeling results and HF radar measurements is calculated during the last four days with no data assimilation.

Keywords: data assimilation, CODAR, HF radar, surface current, direct insertion

Procedia PDF Downloads 541

Authors: Alexander Gretchenko, Liudmila Goncharenko, Sergey Sybachin

Abstract:

This article summarizes the history of the discovery of N.D. Kondratiev of large cycles of economic conditions, as well as the creation and justification of the theory of innovation-cyclical economic development of Kondratiev-Schumpeter. An analysis of it in modern conditions is providing. The main conclusion in this article is that in general terms today it can be argued that the Kondratiev-Schumpeter theory is sufficiently substantiated. Further, the possibility of making a forecast of the development of the economic situation in the direction of applying this theory in practice, which demonstrate its effectiveness, is considered.

Keywords: Kondratiev's big cycles of economic conjuncture, Schumpeter's theory of innovative economic development, long-term cyclical forecasting, dating of Kondratiev cycles

Procedia PDF Downloads 126

Authors: Jeisson A. Estrella, Laura C. Herrera, Cristian A. Arenas

Abstract:

The Colombian electricity sector has been transforming through the insertion of new energy sources to generate electricity, one of them being solar energy, which is being promoted by companies interested in photovoltaic technology. The study of this technology is important for electricity generation in general and for the planning of the sector from the perspective of energy complementarity. Precisely in this last approach is where the project is located; we are interested in answering the concerns about the reliability of the electrical system when climatic phenomena such as El Niño occur or in defining whether it is viable to replace or expand thermoelectric plants. Reliability of the electrical system when climatic phenomena such as El Niño occur, or to define whether it is viable to replace or expand thermoelectric plants with renewable electricity generation systems. In this regard, some difficulties related to the basic information on renewable energy sources from measured data must first be solved, as these come from automatic weather stations. Basic information on renewable energy sources from measured data, since these come from automatic weather stations administered by the Institute of Hydrology, Meteorology and Environmental Studies (IDEAM) and, in the range of study (2005-2019), have significant amounts of missing data. For this reason, the overall objective of the project is to complete the global solar irradiance datasets to obtain time series to develop energy complementarity analyses in a subsequent project. Global solar irradiance data sets to obtain time series that will allow the elaboration of energy complementarity analyses in the following project. The filling of the databases will be done through numerical and statistical methods, which are basic techniques for undergraduate students in technical areas who are starting out as researchers technical areas who are starting out as researchers.

Keywords: time series, global solar irradiance, imputed data, energy complementarity

Procedia PDF Downloads 37

Authors: H. H. Goh, K. C. Goh, W. N. Z. S. Wan Sukri, Q. S. Chua, S. W. Lee, B. C. Kok

Abstract:

Endowed of renewable energy sources (RES) are the advantages of ASEAN, but they are using a low amount of RES only to generate electricity because their primary energy sources are fossil and coal. The cost of purchasing fossil and coal is cheaper now, but it might be expensive soon, as it will be depleted sooner and after. ASEAN showed that the RES are convenient to be implemented. Some country in ASEAN has huge renewable energy sources potential and use. The primary aim of this project is to assist ASEAN countries in preparing the renewable energy and to guide the policies for RES in the more upright direction. The Green-Y model will help ASEAN government to study and forecast the economic concept, including feed-in tariff.

Keywords: ASEAN RES, Renewable Energy, RES Policies, RES Potential, RES Utilization

Procedia PDF Downloads 471

Authors: Adil Rasheed, Mandar Tabib

Abstract:

The paper describes the ability of a high-resolution Computational Fluid Dynamics model to predict terrain-induced turbulence and wind shear close to the ground. Various sensitivity studies to choose the optimal simulation setup for modeling the flow characteristics in a complex terrain are presented. The capabilities of the model are demonstrated by applying it to the Sandnessjøen Airport, Stokka in Norway, an airport that is located in a mountainous area. The model is able to forecast turbulence in real time and trigger an alert when atmospheric conditions might result in high wind shear and turbulence.

Keywords: aviation safety, terrain-induced turbulence, atmospheric flow, alert system

Procedia PDF Downloads 384

Authors: Monika Saha, A. T. M. Hasan Zobeyer, Nasreen Jahan

Abstract:

El Nino-Southern Oscillation (ENSO) is the interaction between atmosphere and ocean in tropical Pacific which causes inconsistent warm/cold weather in tropical central and eastern Pacific Ocean. Due to the impact of climate change, ENSO events are becoming stronger in recent times, and therefore it is very important to study the influence of ENSO in climate studies. Bangladesh, being in the low-lying deltaic floodplain, experiences the worst consequences due to flooding every year. To reduce the catastrophe of severe flooding events, non-structural measures such as flood forecasting can be helpful in taking adequate precautions and steps. Forecasting seasonal flood with a longer lead time of several months is a key component of flood damage control and water management. The objective of this research is to identify the possible strength of teleconnection between ENSO and river flow of Surma and examine the potential possibility of long lead flood forecasting in the wet season. Surma is one of the major rivers of Bangladesh and is a part of the Surma-Meghna river system. In this research, sea surface temperature (SST) has been considered as the ENSO index and the lead time is at least a few months which is greater than the basin response time. The teleconnection has been assessed by the correlation analysis between July-August-September (JAS) flow of Surma and SST of Nino 4 region of the corresponding months. Cumulative frequency distribution of standardized JAS flow of Surma has also been determined as part of assessing the possible teleconnection. Discharge data of Surma river from 1975 to 2015 is used in this analysis, and remarkable increased value of correlation coefficient between flow and ENSO has been observed from 1985. From the cumulative frequency distribution of the standardized JAS flow, it has been marked that in any year the JAS flow has approximately 50% probability of exceeding the long-term average JAS flow. During El Nino year (warm episode of ENSO) this probability of exceedance drops to 23% and while in La Nina year (cold episode of ENSO) it increases to 78%. Discriminant analysis which is known as 'Categoric Prediction' has been performed to identify the possibilities of long lead flood forecasting. It has helped to categorize the flow data (high, average and low) based on the classification of predicted SST (warm, normal and cold). From the discriminant analysis, it has been found that for Surma river, the probability of a high flood in the cold period is 75% and the probability of a low flood in the warm period is 33%. A synoptic parameter, forecasting index (FI) has also been calculated here to judge the forecast skill and to compare different forecasts. This study will help the concerned authorities and the stakeholders to take long-term water resources decisions and formulate policies on river basin management which will reduce possible damage of life, agriculture, and property.

Keywords: El Nino-Southern Oscillation, sea surface temperature, surma river, teleconnection, cumulative frequency distribution, discriminant analysis, forecasting index

Procedia PDF Downloads 114

Authors: Daniil Karzanov

Abstract:

This paper studies the effect of quarterly earnings reports on the stock price. The profitability of the stock is modeled by geometric Brownian diffusion and the Constant Elasticity of Variance model. We fit several variations of stochastic differential equations to the pre-and after-report period using the Maximum Likelihood Estimation and Grid Search of parameters method. By examining the change in the model parameters after reports’ publication, the study reveals that the reports have enough evidence to be a structural breakpoint, meaning that all the forecast models exploited are not applicable for forecasting and should be refitted shortly.

Keywords: stock market, earnings reports, financial time series, structural breaks, stochastic differential equations

Procedia PDF Downloads 162

Authors: Keiwoma Mark Yila, Mathew Lamrana Siaffa Gboku, Mohamed Sahr Lebbie, Lamin Ibrahim Kamara

Abstract:

Rainfall and temperature are the important variables which are often used to trace climate variability and change. A perception study and analysis of climatic data were conducted to assess the changes in rainfall and temperature and their impact on crop production in Moyamba district, Sierra Leone. For the perception study, 400 farmers were randomly selected from farmer-based organizations (FBOs) in 4 chiefdoms, and 30 agricultural extension workers (AWEs) in the Moyamba district were purposely selected as respondents. Descriptive statistics and Kendall’s test of concordance was used to analyze the data collected from the farmers and AEWs. Data for the analysis of variability and trends of rainfall and temperature from 1991 to 2020 were obtained from the Sierra Leone Meteorological Agency and Njala University and grouped into monthly, seasonal and annual time series. Regression analysis was used to determine the statistical values and trend lines for the seasonal and annual time series data. The Mann-Kendall test and Sen’s Slope Estimator were used to analyze the trends' significance and magnitude, respectively. The results of both studies show evidence of climate change in the Moyamba district. A substantial number of farmers and AEWs perceived a decrease in the annual rainfall amount, length of the rainy season, a late start and end of the rainy season, an increase in the temperature during the day and night, and a shortened harmattan period over the last 30 years. Analysis of the meteorological data shows evidence of variability in the seasonal and annual distribution of rainfall and temperature, a decreasing and non-significant trend in the rainy season and annual rainfall, and an increasing and significant trend in seasonal and annual temperature from 1991 to 2020. However, the observed changes in rainfall and temperature by the farmers and AEWs partially agree with the results of the analyzed meteorological data. The majority of the farmers perceived that; adverse weather conditions have negatively affected crop production in the district. Droughts, high temperatures, and irregular rainfall are the three major adverse weather events that farmers perceived to have contributed to a substantial loss in the yields of the major crops cultivated in the district. In response to the negative effects of adverse weather events, a substantial number of farmers take no action due to their lack of knowledge and technical or financial capacity to implement climate-sensitive agricultural (CSA) practices. Even though few farmers are practising some CSA practices in their farms, there is an urgent need to build the capacity of farmers and AEWs to adapt to and mitigate the negative impacts of climate change. The most priority support needed by farmers is the provision of climate-resilient crop varieties, whilst the AEWs need training on CSA practices.

Keywords: climate change, crop productivity, farmer’s perception, rainfall, temperature, Sierra Leone

Procedia PDF Downloads 48

Authors: Syed Ahzam Tariq, Atharva Modi

Abstract:

This paper presents a probabilistic analysis of the safety of water tube boilers using fault tree analysis (FTA). A fault tree has been constructed by considering all possible areas where a malfunction could lead to a boiler accident. Boiler accidents are relatively rare, causing a scarcity of data. The fuzzy approach is employed to perform a quantitative analysis, wherein theories of fuzzy logic are employed in conjunction with expert elicitation to calculate failure probabilities. The Fuzzy Fault Tree Analysis (FFTA) provides a scientific and contingent method to forecast and prevent accidents.

Keywords: fault tree analysis water tube boiler, fuzzy probability score, failure probability

Procedia PDF Downloads 87

Authors: Ella Tyuryumina, Alexey Neznanov

Abstract:

This study is an attempt to obtain reliable data on the natural history of breast cancer growth. We analyze the opportunities for using classical mathematical models (exponential and logistic tumor growth models, Gompertz and von Bertalanffy tumor growth models) to try to describe growth of the primary tumor and the secondary distant metastases of human breast cancer. The research aim is to improve predicting accuracy of breast cancer progression using an original mathematical model referred to CoMPaS and corresponding software. We are interested in: 1) modelling the whole natural history of the primary tumor and the secondary distant metastases; 2) developing adequate and precise CoMPaS which reflects relations between the primary tumor and the secondary distant metastases; 3) analyzing the CoMPaS scope of application; 4) implementing the model as a software tool. The foundation of the CoMPaS is the exponential tumor growth model, which is described by determinate nonlinear and linear equations. The CoMPaS corresponds to TNM classification. It allows to calculate different growth periods of the primary tumor and the secondary distant metastases: 1) ‘non-visible period’ for the primary tumor; 2) ‘non-visible period’ for the secondary distant metastases; 3) ‘visible period’ for the secondary distant metastases. The CoMPaS is validated on clinical data of 10-years and 15-years survival depending on the tumor stage and diameter of the primary tumor. The new predictive tool: 1) is a solid foundation to develop future studies of breast cancer growth models; 2) does not require any expensive diagnostic tests; 3) is the first predictor which makes forecast using only current patient data, the others are based on the additional statistical data. The CoMPaS model and predictive software: a) fit to clinical trials data; b) detect different growth periods of the primary tumor and the secondary distant metastases; c) make forecast of the period of the secondary distant metastases appearance; d) have higher average prediction accuracy than the other tools; e) can improve forecasts on survival of breast cancer and facilitate optimization of diagnostic tests. The following are calculated by CoMPaS: the number of doublings for ‘non-visible’ and ‘visible’ growth period of the secondary distant metastases; tumor volume doubling time (days) for ‘non-visible’ and ‘visible’ growth period of the secondary distant metastases. The CoMPaS enables, for the first time, to predict ‘whole natural history’ of the primary tumor and the secondary distant metastases growth on each stage (pT1, pT2, pT3, pT4) relying only on the primary tumor sizes. Summarizing: a) CoMPaS describes correctly the primary tumor growth of IA, IIA, IIB, IIIB (T1-4N0M0) stages without metastases in lymph nodes (N0); b) facilitates the understanding of the appearance period and inception of the secondary distant metastases.

Keywords: breast cancer, exponential growth model, mathematical model, metastases in lymph nodes, primary tumor, survival

Procedia PDF Downloads 322

Authors: Felipe Henao, Yeny Rodriguez, Juan P. Viteri, Isaac Dyner

Abstract:

Colombia is rich in natural resources and greatly focuses on the exploitation of water for hydroelectricity purposes. Alternative cleaner energy sources, such as solar and wind power, have been largely neglected despite: a) its abundance, b) the complementarities between hydro, solar and wind power, and c) the cost competitiveness of renewable technologies. The current limited mix of energy sources creates considerable weaknesses for the system, particularly when facing extreme dry weather conditions, such as El Niño event. In the past, El Niño have exposed the truly consequences of a system heavily dependent on hydropower, i.e. loss of power supply, high energy production costs, and loss of overall competitiveness for the country. Nonetheless, it is expected that the participation of hydroelectricity will increase in the near future. In this context, this paper proposes a stochastic lineal programming model to optimize the insertion of renewable energy systems (RES) into the Colombian electricity sector. The model considers cost-based generation competition between traditional energy technologies and alternative RES. This work evaluates the financial, environmental, and technical implications of different combinations of technologies. Various scenarios regarding the future evolution of costs of the technologies are considered to conduct sensitivity analysis of the solutions – to assess the extent of the participation of the RES in the Colombian power sector. Optimization results indicate that, even in the worst case scenario, where costs remain constant, the Colombian power sector should diversify its portfolio of technologies and invest strongly in solar and wind power technologies. The diversification through RES will contribute to make the system less vulnerable to extreme weather conditions, reduce the overall system costs, cut CO2 emissions, and decrease the chances of having national blackout events in the future. In contrast, the business as usual scenario indicates that the system will turn more costly and less reliable.

Keywords: energy policy and planning, stochastic programming, sustainable development, water management

Procedia PDF Downloads 266

Authors: Emily Injete Amondo, Alisher Mirzabaev, Emmanuel Rukundo

Abstract:

Children in rural farming households are often vulnerable to a multitude of risks, including health risks associated with climate change and variability. Cognizant of this, this study empirically traced the relationship between climate variability and nutritional health outcomes in rural children while identifying the cause-and-effect transmission mechanisms. We combined four waves of the rich Uganda National Panel Survey (UNPS), part of the World Bank Living Standards Measurement Studies (LSMS) for the period 2009-2014, with long-term and high-frequency rainfall and temperature datasets. Self-reported drought and flood shock variables were further used in separate regressions for triangulation purposes and robustness checks. Panel fixed effects regressions were applied in the empirical analysis, accounting for a variety of causal identification issues. The results showed significant negative outcomes for children’s anthropometric measurements due to the impacts of moderate and extreme droughts, extreme wet spells, and heatwaves. On the contrary, moderate wet spells were positively linked with nutritional measures. Agricultural production and child diarrhea were the main transmission channels, with heatwaves, droughts, and high rainfall variability negatively affecting crop output. The probability of diarrhea was positively related to increases in temperature and dry spells. Results further revealed that children in households who engaged in ex-ante or anticipatory risk-reducing strategies such as savings had better health outcomes as opposed to those engaged in ex-post coping such as involuntary change of diet. These results highlight the importance of adaptation in smoothing the harmful effects of climate variability on the health of rural households and children in Uganda.

Keywords: extreme weather events, undernutrition, diarrhea, agricultural production, gridded weather data

Procedia PDF Downloads 76

Authors: Li-hsing Shih, Wei-Jen Hsu

Abstract:

Recently, the sale of electrical vehicles (EVs) has increased dramatically due to maturing technology development and decreasing cost. Governments of many countries have made regulations and policies in favor of EVs due to their long-term commitment to net zero carbon emissions. However, due to uncertain factors such as the future price of EVs, forecasting the future market share of EVs is a challenging subject for both the auto industry and local government. This study tries to forecast the market share of EVs using conjoint models and Monte Carlo simulation. The research is conducted in three phases. (1) A conjoint model is established to represent the customer preference structure on purchasing vehicles while five product attributes of both EV and internal combustion engine vehicles (ICEV) are selected. A questionnaire survey is conducted to collect responses from Taiwanese consumers and estimate the part-worth utility functions of all respondents. The resulting part-worth utility functions can be used to estimate the market share, assuming each respondent will purchase the product with the highest total utility. For example, attribute values of an ICEV and a competing EV are given respectively, two total utilities of the two vehicles of a respondent are calculated and then knowing his/her choice. Once the choices of all respondents are known, an estimate of market share can be obtained. (2) Among the attributes, future price is the key attribute that dominates consumers’ choice. This study adopts the assumption of a learning curve to predict the future price of EVs. Based on the learning curve method and past price data of EVs, a regression model is established and the probability distribution function of the price of EVs in 2030 is obtained. (3) Since the future price is a random variable from the results of phase 2, a Monte Carlo simulation is then conducted to simulate the choices of all respondents by using their part-worth utility functions. For instance, using one thousand generated future prices of an EV together with other forecasted attribute values of the EV and an ICEV, one thousand market shares can be obtained with a Monte Carlo simulation. The resulting probability distribution of the market share of EVs provides more information than a fixed number forecast, reflecting the uncertain nature of the future development of EVs. The research results can help the auto industry and local government make more appropriate decisions and future action plans.

Keywords: conjoint model, electrical vehicle, learning curve, Monte Carlo simulation

Procedia PDF Downloads 42

Authors: Hasik Lee, Seung-Young Kho

Abstract:

Nowadays, automatic fare collection (AFC) system is widely used in many countries. However, smart card data from many of cities does not contain alighting information which is necessary to build OD matrices. Therefore, in order to utilize smart card data, destinations of passengers should be estimated. In this paper, kernel density estimation was used to forecast probabilities of alighting stations of bus passengers and applied to smart card data in Seoul, Korea which contains boarding and alighting information. This method was also validated with actual data. In some cases, stochastic method was more accurate than deterministic method. Therefore, it is sufficiently accurate to be used to build OD matrices.

Keywords: destination estimation, Kernel density estimation, smart card data, validation

Procedia PDF Downloads 324

Authors: Jiun-Horng Tsai, Shi-Jie, Nieh

Abstract:

Fine particulate matter (PM₂.₅) has become an important issue all over the world over the last decade. Annual mean PM₂.₅ concentration has been over the ambient air quality standard of PM₂.₅ (annual average concentration as 15μg/m³) which adapted by Taiwan Environmental Protection Administration (TEPA). TEPA, therefore, has developed a number of air pollution control measures to improve the ambient concentration by reducing the emissions of primary fine particulate matter and the precursors of secondary PM₂.₅. This study investigated the potential improvement of ambient PM₂.₅ concentration by the TEPA program and the other scenario for further emission reduction on various sources. Four scenarios had been evaluated in this study, including a basic case and three reduction scenarios (A to C). The ambient PM₂.₅ concentration was evaluated by Community Multi-scale Air Quality modelling system (CMAQ) ver. 4.7.1 along with the Weather Research and Forecasting Model (WRF) ver. 3.4.1. The grid resolutions in the modelling work are 81 km × 81 km for domain 1 (covers East Asia), 27 km × 27 km for domain 2 (covers Southeast China and Taiwan), and 9 km × 9 km for domain 3 (covers Taiwan). The result of PM₂.₅ concentration simulation in different regions of Taiwan shows that the annual average concentration of basic case is 24.9 μg/m³, and are 22.6, 18.8, and 11.3 μg/m³, respectively, for scenarios A to C. The annual average concentration of PM₂.₅ would be reduced by 9-55 % for those control scenarios. The result of scenario C (the emissions of precursors reduce to allowance levels) could improve effectively the airborne PM₂.₅ concentration to attain the air quality standard. According to the results of unit precursor reduction contribution, the allowance emissions of PM₂.₅, SOₓ, and NOₓ are 16.8, 39, and 62 thousand tons per year, respectively. In the Kao-Ping air basin, the priority for reducing precursor emissions is PM₂.₅ > NOₓ > SOₓ, whereas the priority for reducing precursor emissions is PM₂.₅ > SOₓ > NOₓ in others area. The result indicates that the target pollutants that need to be reduced in different air basin are different, and the control measures need to be adapted to local conditions.

Keywords: airborne PM₂.₅, community multi-scale air quality modelling system, control measures, weather research and forecasting model

Procedia PDF Downloads 113

Authors: Hussein Lujuo Mohamed

Abstract:

Introduction: Public health has always been influenced by climate and weather. Changes in climate and climate variability, particularly changes in weather extremes affect the environment that provides people with clean air, food, water, shelter, and security. Tanzania is not an exception to the threats of climate change. The health sector is mostly affected due to emergence and proliferation of infectious diseases, thereby affecting health of the population and thus impacting achievement of sustainable development goals. Methodology: A desk review on documented issues pertaining to climate change and health in Tanzania was done using Google search engine. Keywords included climate change, link, health, climate initiatives. In cases where information was not available, documents from Ministry of Health, Vice Presidents Office-Environment, Local Government Authority, Ministry of Water, WHO, research, and training institutions were reviewed. Some of the reviewed documents from these institutions include policy brief papers, fieldwork activity reports, training manuals, and guidelines. Results: Six main climate resilience activities were identified in Tanzania. These were development and implementation of climate resilient water safety plans guidelines both for rural and urban water authorities, capacity building of rural and urban water authorities on implementation of climate-resilient water safety plans, and capacity strengthening of local environmental health practitioners on mainstreaming climate change and health into comprehensive council health plans. Others were vulnerability and adaptation assessment for the health sector, mainstreaming climate change in the National Health Policy, and development of risk communication strategy on climate. In addition information, education, and communication materials on climate change and to create awareness were developed aiming to sensitize and create awareness among communities on climate change issues and its effect on public health. Conclusion: Proper implementation of these interventions will help the country become resilient to many impacts of climate change in the health sector and become a good example for other least developed countries.

Keywords: climate, change, Tanzania, health

Procedia PDF Downloads 89

Authors: A. Brunel, A. Kernevez, F. Leclere, J. Trenteseaux

Abstract:

Today, wine quality is only evaluated by wine experts with their own different personal tastes, even if they may agree on some common features. So producers do not have any unbiased way to independently assess the quality of their products. A tool is here proposed to evaluate wine quality by an objective ranking based upon the variables entering wine elaboration, and analysed through principal component analysis (PCA) method. Actual climatic data are compared by measuring the relative distance between each considered wine, out of which the general ranking is performed.

Keywords: wine, grape, weather conditions, rating, climate, principal component analysis, metric analysis

Procedia PDF Downloads 284

Authors: Harold P. Villota, Alejandro Osorio B.

Abstract:

The energy meteorology is an area for study energy complementarity and the use of renewable sources in interconnected systems. Due to diversify the energy matrix in Colombia with wind sources, is necessary to know the data bases about this one. However, the time series given by 260 automatic weather stations have empty, and no apply data, so the purpose is to fill the time series selecting two years to characterize, impute and use like base to complete the data between 2005 and 2020.

Keywords: complementarity, wind speed, renewable, colombia, characteri, characterization, imputation

Procedia PDF Downloads 134

Authors: Aswathi Thrivikraman, S. Advaith

Abstract:

The study is aimed at designing encoders for deciphering intricacies in time series data by redescribing the dynamics operating on a lower-dimensional manifold. A semi-supervised LSTM autoencoder is devised and investigated to see if the latent representation of the time series data can better forecast the data. End-to-end training of the LSTM autoencoder, together with another LSTM network that is connected to the latent space, forces the hidden states of the encoder to represent the most meaningful latent variables relevant for forecasting. Furthermore, the study compares the predictions with those of a traditional seq2seq model.

Keywords: LSTM, autoencoder, forecasting, seq2seq model

Procedia PDF Downloads 122

Authors: Masoud Fathi Kazerouni, Jens Schlemper, Klaus-Dieter Kuhnert

Abstract:

Green planet is one of the Earth’s names which is known as a terrestrial planet and also can be named the fifth largest planet of the solar system as another scientific interpretation. Plants do not have a constant and steady distribution all around the world, and even plant species’ variations are not the same in one specific region. Presence of plants is not only limited to one field like botany; they exist in different fields such as literature and mythology and they hold useful and inestimable historical records. No one can imagine the world without oxygen which is produced mostly by plants. Their influences become more manifest since no other live species can exist on earth without plants as they form the basic food staples too. Regulation of water cycle and oxygen production are the other roles of plants. The roles affect environment and climate. Plants are the main components of agricultural activities. Many countries benefit from these activities. Therefore, plants have impacts on political and economic situations and future of countries. Due to importance of plants and their roles, study of plants is essential in various fields. Consideration of their different applications leads to focus on details of them too. Automatic recognition of plants is a novel field to contribute other researches and future of studies. Moreover, plants can survive their life in different places and regions by means of adaptations. Therefore, adaptations are their special factors to help them in hard life situations. Weather condition is one of the parameters which affect plants life and their existence in one area. Recognition of plants in different weather conditions is a new window of research in the field. Only natural images are usable to consider weather conditions as new factors. Thus, it will be a generalized and useful system. In order to have a general system, distance from the camera to plants is considered as another factor. The other considered factor is change of light intensity in environment as it changes during the day. Adding these factors leads to a huge challenge to invent an accurate and secure system. Development of an efficient plant recognition system is essential and effective. One important component of plant is leaf which can be used to implement automatic systems for plant recognition without any human interface and interaction. Due to the nature of used images, characteristic investigation of plants is done. Leaves of plants are the first characteristics to select as trusty parts. Four different plant species are specified for the goal to classify them with an accurate system. The current paper is devoted to principal directions of the proposed methods and implemented system, image dataset, and results. The procedure of algorithm and classification is explained in details. First steps, feature detection and description of visual information, are outperformed by using Scale invariant feature transform (SIFT), HARRIS-SIFT, and FAST-SIFT methods. The accuracy of the implemented methods is computed. In addition to comparison, robustness and efficiency of results in different conditions are investigated and explained.

Keywords: SIFT combination, feature extraction, feature detection, natural images, natural plant recognition, HARRIS-SIFT, FAST-SIFT

Procedia PDF Downloads 243

Authors: Maria C. Mariani, Md Al Masum Bhuiyan, Osei K. Tweneboah, Hector G. Huizar

Abstract:

This work is devoted to the study of modeling geophysical time series. A stochastic technique with time-varying parameters is used to forecast the volatility of data arising in geophysics. In this study, the volatility is defined as a logarithmic first-order autoregressive process. We observe that the inclusion of log-volatility into the time-varying parameter estimation significantly improves forecasting which is facilitated via maximum likelihood estimation. This allows us to conclude that the estimation algorithm for the corresponding one-step-ahead suggested volatility (with ±2 standard prediction errors) is very feasible since it possesses good convergence properties.

Keywords: Augmented Dickey Fuller Test, geophysical time series, maximum likelihood estimation, stochastic volatility model

Procedia PDF Downloads 273