Search results for: predict precipitation and (FUTA) standard device

Authors: Yang Jian-Hong, Zhang Ren-Cheng, Huang Li

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Arc fault is one of the main inducements of electric fires. Arc Fault Detection Device (AFDD) can detect arc fault effectively. Arc fault detections and unhooking standards are the keys to AFDD practical application. First, an arc fault continuous production system was developed, which could count the arc half wave number. Then, Combining with the UL1699 standard, ignition probability curve of cotton and unhooking time of various currents intensity were obtained by experiments. The combustion degree of arc fault could be expressed effectively by arc area. Experiments proved that electric fires would be misjudged or missed only using arc half wave number as AFDD unhooking basis. At last, Practical tests were carried out on the self-developed AFDD system. The result showed that actual AFDD unhooking time was the sum of arc half wave cycling number, Arc wave identification time and unhooking mechanical operation time And the first two shared shorter time. Unhooking time standard depended on the shortest mechanical operation time.

Keywords: arc fault detection device, arc area, arc half wave, unhooking time, arc fault

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Authors: Kukuri Tavartkiladze, Nana Bolashvili

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The purpose of this paper was separation and study of the part of structure regime, which directly affects the process of desertification. A simple scheme was prepared for the assessment of desertification process; surface air temperature and precipitation for the years of 1936-2009 were analyzed.  The map of distribution of the Desertification Contributing Coefficient in the territory of Georgia was compiled. The simple scheme for identification of the intensity of the desertification contributing process has been developed and the illustrative example of its practical application for the territory of Georgia has been conducted.

Keywords: aridity, climate change, desertification, precipitation

Procedia PDF Downloads 336

Authors: Lucas B. Naves, Maria José Abreu

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Reprocessing single-use device has attracted interesting on the medical environment over the last decades. The reprocessing technique was sought in order to reduce the cost of purchasing the new medical device, which can achieve almost double of the price of the reprocessed product. In this manuscript, we have done a literature review, aiming the reuse of medical device that was firstly designed for single use only, but has become, more and more, effective on its reprocessing procedure. We also show the regulation, the countries which allows this procedure, the classification of these device and also the most important issue concerning the re-utilization of medical device, how to minimizing the risk of gram positive and negative bacteria, avoid cross-contamination, hepatitis B (HBV), and C (HCV) virus, and also human immunodeficiency virus (HIV).

Keywords: reusing, reprocessing, single-use medical device, HIV, hepatitis B and C

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Authors: Cenk Sezen, Turgay Partal

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North Sea Caspian Pattern Index (NCP) refers to an atmospheric teleconnection between the North Sea and North Caspian at the 500 hPa geopotential height level. The aim of this study is to search for effects of NCP on annual and seasonal mean temperature and also annual and seasonal precipitation totals in the Aegean region of Turkey. The study contains the data that consist of 46 years obtained from nine meteorological stations. To determine the relationship between NCP and the climatic parameters, firstly the Pearson correlation coefficient method was utilized. According to the results of the analysis, most of the stations in the region have a high negative correlation NCPI in all seasons, especially in the winter season in terms of annual and seasonal mean temperature (statistically at significant at the 90% level). Besides, high negative correlation values between NCPI and precipitation totals are observed during the winter season at the most of stations. Furthermore, the NCPI values were divided into two group as NCPI(-) and NCPI(+), and then mean temperature and precipitation total values, which are grouped according to the NCP(-) and NCP(+) phases, were determined as annual and seasonal. During the NCPI(-), higher mean temperature values are observed in all of seasons, particularly in the winter season compared to the mean temperature values under effect of NCP(+). Similarly, during the NCPI(-) in winter season precipitation total values have higher than the precipitation total values under the effect of NCP(+); however, in other seasons there no substantial changes were observed between the precipitation total values. As a result of this study, significant proof is obtained with regards to the influences of NCP on the temperature and precipitation regime in the Aegean region of Turkey.

Keywords: Aegean region, NCPI, precipitation, temperature

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Authors: Nabilah Filzah Mohd Radzuan, Andi Putra, Zalinda Othman, Azuraliza Abu Bakar, Abdul Razak Hamdan

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Precipitation forecast is important to avoid natural disaster incident which can cause losses in the involved area. This paper reviews three techniques logistic regression, decision tree, and random forest which are used in making precipitation forecast. These combination techniques through the vector auto-regression (VAR) model help in finding the advantages and strengths of each technique in the forecast process. The data-set contains variables of the rain’s domain. Adaptation of artificial intelligence techniques involved in rain domain enables the forecast process to be easier and systematic for precipitation forecast.

Keywords: logistic regression, decisions tree, random forest, VAR model

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Authors: Miguel Cañon, Darwin Mena, Ivan Cabeza

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In this work was compared and evaluated the applicability of statistical methods for the estimation of missing precipitations data in the basin of the river Lenguazaque located in the departments of Cundinamarca and Boyacá, Colombia. The methods used were the method of simple linear regression, distance rate, local averages, mean rates, correlation with nearly stations and multiple regression method. The analysis used to determine the effectiveness of the methods is performed by using three statistical tools, the correlation coefficient (r2), standard error of estimation and the test of agreement of Bland and Altmant. The analysis was performed using real rainfall values removed randomly in each of the seasons and then estimated using the methodologies mentioned to complete the missing data values. So it was determined that the methods with the highest performance and accuracy in the estimation of data according to conditions that were counted are the method of multiple regressions with three nearby stations and a random application scheme supported in the precipitation behavior of related data sets.

Keywords: statistical comparison, precipitation data, river subbasin, Bland and Altmant

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Authors: James Rigor Camacho, Wansu Lim

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Over the last few years, the development of new wearable and processing technologies has accelerated in order to harness physiological data such as electroencephalograms (EEGs) for EEG-based applications. EEG has been demonstrated to be a source of emotion recognition signals with the highest classification accuracy among physiological signals. However, when emotion recognition systems are used for real-time classification, the training unit is frequently left to run offline or in the cloud rather than working locally on the edge. That strategy has hampered research, and the full potential of using an edge AI device has yet to be realized. Edge AI devices are computers with high performance that can process complex algorithms. It is capable of collecting, processing, and storing data on its own. It can also analyze and apply complicated algorithms like localization, detection, and recognition on a real-time application, making it a powerful embedded device. The NVIDIA Jetson series, specifically the Jetson Nano device, was used in the implementation. The cEEGrid, which is integrated to the open-source brain computer-interface platform (OpenBCI), is used to collect EEG signals. An EEG-based real-time emotion recognition system on Edge AI is proposed in this paper. To perform graphical spectrogram categorization of EEG signals and to predict emotional states based on input data properties, machine learning-based classifiers were used. Until the emotional state was identified, the EEG signals were analyzed using the K-Nearest Neighbor (KNN) technique, which is a supervised learning system. In EEG signal processing, after each EEG signal has been received in real-time and translated from time to frequency domain, the Fast Fourier Transform (FFT) technique is utilized to observe the frequency bands in each EEG signal. To appropriately show the variance of each EEG frequency band, power density, standard deviation, and mean are calculated and employed. The next stage is to identify the features that have been chosen to predict emotion in EEG data using the K-Nearest Neighbors (KNN) technique. Arousal and valence datasets are used to train the parameters defined by the KNN technique.Because classification and recognition of specific classes, as well as emotion prediction, are conducted both online and locally on the edge, the KNN technique increased the performance of the emotion recognition system on the NVIDIA Jetson Nano. Finally, this implementation aims to bridge the research gap on cost-effective and efficient real-time emotion recognition using a resource constrained hardware device, like the NVIDIA Jetson Nano. On the cutting edge of AI, EEG-based emotion identification can be employed in applications that can rapidly expand the research and implementation industry's use.

Keywords: edge AI device, EEG, emotion recognition system, supervised learning algorithm, sensors

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Authors: Nokwethaba Makhanya, Babatunde J. Abiodun, Piotr Wolski

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Climate change possibly intensifies hydrological droughts and reduces water availability in river basins. Despite this, most research on climate change effects in southern Africa has focused exclusively on meteorological droughts. This thesis projects the potential impact of climate change on the future characteristics of hydrological droughts in the Limpopo River Basin (LRB). The study uses regional climate model (RCM) measurements (from the Coordinated Regional Climate Downscaling Experiment, CORDEX) and a combination of hydrological simulations (using the Soil and Water Assessment Tool Plus model, SWAT+) to predict the impacts at four global warming levels (GWLs: 1.5℃, 2.0℃, 2.5℃, and 3.0℃) under the RCP8.5 future climate scenario. The SWAT+ model was calibrated and validated with a streamflow dataset observed over the basin, and the sensitivity of model parameters was investigated. The performance of the SWAT+LRB model was verified using the Nash-Sutcliffe efficiency (NSE), Percent Bias (PBIAS), Root Mean Square Error (RMSE), and coefficient of determination (R²). The Standardized Precipitation Evapotranspiration Index (SPEI) and the Standardized Precipitation Index (SPI) have been used to detect meteorological droughts. The Soil Water Index (SSI) has been used to define agricultural drought, while the Water Yield Drought Index (WYLDI), the Surface Run-off Index (SRI), and the Streamflow Index (SFI) have been used to characterise hydrological drought. The performance of the SWAT+ model simulations over LRB is sensitive to the parameters CN2 (initial SCS runoff curve number for moisture condition II) and ESCO (soil evaporation compensation factor). The best simulation generally performed better during the calibration period than the validation period. In calibration and validation periods, NSE is ≤ 0.8, while PBIAS is ≥ ﹣80.3%, RMSE ≥ 11.2 m³/s, and R² ≤ 0.9. The simulations project a future increase in temperature and potential evapotranspiration over the basin, but they do not project a significant future trend in precipitation and hydrological variables. However, the spatial distribution of precipitation reveals a projected increase in precipitation in the southern part of the basin and a decline in the northern part of the basin, with the region of reduced precipitation projected to increase with GWLs. A decrease in all hydrological variables is projected over most parts of the basin, especially over the eastern part of the basin. The simulations predict meteorological droughts (i.e., SPEI and SPI), agricultural droughts (i.e., SSI), and hydrological droughts (i.e., WYLDI, SRI) would become more intense and severe across the basin. SPEI-drought has a greater magnitude of increase than SPI-drought, and agricultural and hydrological droughts have a magnitude of increase between the two. As a result, this research suggests that future hydrological droughts over the LRB could be more severe than the SPI-drought projection predicts but less severe than the SPEI-drought projection. This research can be used to mitigate the effects of potential climate change on basin hydrological drought.

Keywords: climate change, CORDEX, drought, hydrological modelling, Limpopo River Basin

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Authors: Hai Zhou, Wenzhi Zhao

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Water is one of the most important factors limiting plant growth and development in desert ecosystems. In order to understand how desert shrubs cope with variation in water sources over time, it is important to understand plant–water relations in desert-oasis ecotone. We selected the typical desert shrubs: Nitraria sibirica, Calligonum mongolicum and Haloxylon ammodendron of 5-, 10-, 20- and 40-year old as the research species, to study the seasonal variation of plant water sources and response to precipitation in the desert-oasis ecotone of Linze, Northwestern China. We examined stable isotopic ratios of oxygen (δ18O) in stem water of desert shrubs as well as in precipitation, groundwater, and soil water in different soil layers and seasons to determine water sources for the shrubs. We found that the N. sibirica and H. ammodendron of 5-, 10-year old showed significant seasonal variation characteristics of δ18O value of stem water and water sources. However, the C. mongolicum and 20- and 40-year H. ammodendron main water sources were from deep soil water and groundwater, and less response to precipitation pulse. After 22.4 mm precipitation, the contribution of shallow soil water (0-50cm) to the use of N. sibirica increased from 6.7% to 36.5%; the C. mongolicum rarely use precipitation that were about 58.29% and 23.51%, absorbed from the deep soil water and groundwater; the contribution of precipitation to use of H. ammodendron had significantly differences among the four ages. The H. ammodendron of 5- and 10-year old about 86.3% and 42.5% water sources absorbed from the shallow soil water after precipitation. However, the contribution to 20- and 40-year old plant was less than 15%. So, the precipitation was one of the main water sources for desert shrubs, but the species showed different water utilization. We conclude that the main water source of the N. sibirica and H. ammodendron of 5-, 10-year was soil water recharged by precipitation, but the deeply rooted H. ammodendron of 20‐ and 40‐year‐old and the C. mongolicum have the ability to exploit a deep and reliable water source.

Keywords: water use pattern, water resource, stable isotope, seasonal change, precipitation pulse

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Authors: Willy Mbenza, Sho Kenjiro

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Increasing efforts are directed towards a better understanding and foreknowledge of extreme precipitation likelihood, given the adverse effects associated with their occurrence. This knowledge plays a crucial role in long-term planning and the formulation of effective emergency response. However, predicting extreme events reliably presents a challenge to conventional empirical/statistics due to the involvement of numerous variables spanning different time and space scales. In the recent time, Machine Learning has emerged as a promising tool for predicting the dynamics of extreme precipitation. ML techniques enables the consideration of both local and regional physical variables that have a strong influence on the likelihood of extreme precipitation. These variables encompasses factors such as air temperature, soil moisture, specific humidity, aerosol concentration, among others. In this study, we develop an ML model that incorporates both local and regional variables while establishing a robust relationship between physical variables and precipitation during the downscaling process. Furthermore, the model provides valuable information on the frequency and duration of a given intensity of precipitation.

Keywords: machine learning (ML), predictions, rainfall events, regional variables

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Authors: Mustapha Aminu Bagiwa, Ainuddin Wahid Abdul Wahab, Mohd Yamani Idna Idris, Suleman Khan

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Video source device identification has become a problem of concern in numerous domains especially in multimedia security and digital investigation. This is because videos are now used as evidence in legal proceedings. Source device identification aim at identifying the source of digital devices using the content they produced. However, due to affordable processing tools and the influx in digital content generating devices, source device identification is still a major problem within the digital forensic community. In this paper, we discuss source device identification for digital videos by identifying techniques that were proposed in the literature for model or specific device identification. This is aimed at identifying salient open challenges for future research.

Keywords: video forgery, source camcorder, device identification, forgery detection

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Authors: Lv Shizeng, Han Xiao, Zhang Yi, Ding Wenjing

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The lunar sample storage device is mainly used for the preparation of the lunar samples, observation, physical analysis and other work. The lunar samples and operating equipment are placed directly inside the storage device. The inside of the storage device is a high purity nitrogen environment to ensure that the sample is not contaminated by the Earth's environment. In order to ensure that the water and oxygen indicators in the storage device meet the sample requirements, a dynamic gas cycle is required between the storage device and the external purification equipment. However, the internal gas disturbance in the storage device can affect the operation of the sample. In this paper, the storage device model is established, and the tetrahedral mesh is established by Tetra/Mixed method. The influence of different inlet position and gas flow on the internal flow field disturbance is calculated, and the disturbed flow area should be avoided during the sampling operation.

Keywords: lunar samples, gas disturbance, storage device, characteristic analysis

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Authors: Sukovatov K.Yu., Bezuglova N.N.

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Winter precipitation variability over the Altai Krai was investigated by retrieving temporal patterns. The spectral singular analysis was used to describe the variance distribution and to reduce the precipitation data into a few components (modes). The associated time series were related to large-scale atmospheric and oceanic circulation indices by using lag cross-correlation and wavelet-coherence analysis. GPCC monthly precipitation data for rectangular field limited by 50-550N, 77-880E and monthly climatological circulation index data for the cold season were used to perform SSA decomposition and retrieve statistics for analyzed parameters on the time period 1951-2017. Interannual variability of winter precipitation over the Altai Krai are mostly caused by three natural factors: intensity variations of momentum exchange between mid and polar latitudes over the North Atlantic (explained variance 11.4%); wind speed variations in equatorial stratosphere (quasi-biennial oscillation, explained variance 15.3%); and surface temperature variations for equatorial Pacific sea (ENSO, explained variance 2.8%). It is concluded that under the current climate conditions (Arctic amplification and increasing frequency of meridional processes in mid-latitudes) the second and the third factors are giving more significant contribution into explained variance of interannual variability for cold season atmospheric precipitation over the Altai Krai than the first factor.

Keywords: interannual variability, winter precipitation, Altai Krai, wavelet-coherence

Procedia PDF Downloads 188

Authors: Szu-Hua Wang

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Urban areas contain abundant potential biochemical storages and renewable and non-renewable flows. Urban natural environments for breeding natural assets and urban economic development for maintaining urban functions can be analyzed form the concept of ecological economic system. Land use change and ecosystem services change are resulting from the interactions between human activities and environments factually. Land use change due to human activities is the major cause of climate change, leading to serious impacts on urban ecosystem services, including provisioning services, regulating services, cultural services and supporting services. However, it lacks discussion on the interactions among urban land use change, ecosystem services change, and extreme precipitation events. Energy synthesis can use the same measure standard unit, solar energy, for different energy resources (e.g. sunlight, water, fossil fuels, minerals, etc.) and analyze contributions of various natural environmental resources on human economic systems. Therefore, this research adopts the concept of ecological, economic systems and energy synthesis for analyzing dynamic spatial impacts of land use change on ecosystem services, using the Taipei area as a case study. The analysis results show that changes in land use in the Taipei area, especially the conversion of natural lands and agricultural lands to urban lands, affect the ecosystem services negatively. These negative effects become more significant during the extreme precipitation events.

Keywords: urban ecological economic system, extreme precipitation events, ecosystem services, energy

Procedia PDF Downloads 190

Authors: Chakrit Chotamonsak

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This study was analyzed changes in temperature and precipitation extremes in northern Thailand for the period 1981-2011.The study includes an analysis of the average and trends of changes in temperature and precipitation using 22 climate indices, related to the intensity, frequency and duration of extreme climate events. The results showed that the averaged trend of maximum, minimum and mean temperature is likely to increase over the study area in rate of 0.5, 0.9 and 0.7 °C in last 30 years. Changes in temperature at nighttime, then rising at a rate higher daytime is resulting to decline of diurnal temperature range throughout the area. Trend of changes in average precipitation during the year 1981-2011 is expected to increase at an average rate of 21%. The intensity of extreme temperature events is increasing almost all station. In particular, the changes of the night were unusually hot has intensified throughout the region. In some provinces such as Chiang Mai and Lampang are likely be faced with the severity of hot days and hot nights in increasing rate. Frequency of extreme temperature events are likely to increase each station, especially hot days, and hot nights are increasing at a rate of 2.38 and 3.58 days per decade. Changes in the cold days and cold nights are declining at a rate of 0.82 and 3.03 days per decade. The duration of extreme temperature events is expected to increase the events hot in every station. An average of 17.8 days per decade for the number of consecutive cold winter nights likely shortens the rate of 2.90 days per decade. The analysis of the precipitation indices reveals the intensity of extreme precipitation is increasing almost across the region. The intensify expressed the heavy rain in one day (Rx1day) and very heavy rain accumulated in 5 days (RX5day) which is likely to increase, and very heavy rainfall is likely to increase in intensity. Frequency of extreme precipitation events is likely to increase over the station. The average frequency of heavy precipitation events increased xxx days per decade. The duration of extreme precipitation events, such as the consecutive dry days are likely to reduce the numbers almost all station while the consecutive wet days tends to increase and decrease at different numbers in different areas.

Keywords: climate extreme, temperature extreme, precipitation extreme, Northern Thailand

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Authors: Nguyen Tien Thanh, Günter Meon

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Precipitation plays a key role in water cycle, defining the local climatic conditions and in ecosystem. It is also an important input parameter for water resources management and hydrologic models. With spatial continuous data, a certainty of discharge predictions or other environmental factors is unquestionably better than without. This is, however, not always willingly available to acquire for a small basin, especially for coastal region in Vietnam due to a low network of meteorological stations (30 stations) on long coast of 3260 km2. Furthermore, available gridded precipitation datasets are not fine enough when applying to hydrologic models. Under conditions of global warming, an application of spatial interpolation methods is a crucial for the climate change impact studies to obtain the spatial continuous data. In recent research projects, although some methods can perform better than others do, no methods draw the best results for all cases. The objective of this paper therefore, is to investigate different spatial interpolation methods for daily precipitation over a small basin (approximately 400 km2) located in coastal region, Southern Vietnam and find out the most efficient interpolation method on this catchment. The five different interpolation methods consisting of cressman, ordinary kriging, regression kriging, dual kriging and inverse distance weighting have been applied to identify the best method for the area of study on the spatio-temporal scale (daily, 10 km x 10 km). A 30-year precipitation database was created and merged into available gridded datasets. Finally, observed changes in constructed precipitation were performed. The results demonstrate that the method of ordinary kriging interpolation is an effective approach to analyze the daily precipitation. The mixed trends of increasing and decreasing monthly, seasonal and annual precipitation have documented at significant levels.

Keywords: interpolation, precipitation, trend, vietnam

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Authors: Zerarka Hizia, Akchiche Mustapha, Prunier Florent

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The landslide of Ain El Hammam (AEH) is characterized by a complex geology and a high hydrogeology hazard. AEH's perpetual reactivation compels us to look closely at its triggers and to better understand the mechanisms of its evolution in mass and in depth. This study builds a numerical model to simulate the influencing factors such as precipitation, non-saturation, and pore pressure fluctuations, using Plaxis software. For a finer analysis of instabilities, we use Hill's criterion, based on the sign of the second order work, which is the most appropriate material stability criterion for non-associated elastoplastic materials. The results of this type of calculation allow us, in theory, to predict the shape and position of the slip surface(s) which are liable to ground movements of the slope, before reaching the rupture given by the plastic limit of Mohr Coulomb. To validate the numerical model, an analysis of inclinometer measures is performed to confirm the direction of movement and kinematic of the sliding mechanism of AEH’s slope.

Keywords: landslide, second order work, precipitation, inclinometers

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Authors: Kotbia Labiod, Mohamed Mouldi Tlili

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The presence of different mineral salts in natural waters may precipitate and form hard deposits in water distribution systems. In this respect, we have developed numerous works on scaling by Algerian water with a very high hardness of 102 °F. The aim of our work is to study the influence of water dynamics and its composition on mineral salts on the precipitation of calcium carbonate (CaCO3). To achieve this objective, we have adopted two precipitation techniques based on controlled degassing of dissolved CO2. This study will identify the causes and provide answers to this complex phenomenon.

Keywords: calcium carbonate, controlled degassing, precipitation, scaling

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Authors: Malay K. Pramanik

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Climate change is continuously affecting the ecosystem, species distribution as well as global biodiversity. The assessment of the species potential distribution and the spatial changes under various climate change scenarios is a significant step towards the conservation and mitigation of habitat shifts, and species' loss and vulnerability. In this context, the present study aimed to predict the influence of current and future climate on an ecologically vulnerable medicinal species, Garcinia gummi-gutta, of the southern Western Ghats using Maximum Entropy (MaxEnt) modeling. The future projections were made for the period of 2050 and 2070 with RCP (Representative Concentration Pathways) scenario of 4.5 and 8.5 using 84 species occurrence data, and climatic variables from three different models of Intergovernmental Panel for Climate Change (IPCC) fifth assessment. Climatic variables contributions were assessed using jackknife test and AOC value 0.888 indicates the model perform with high accuracy. The major influencing variables will be annual precipitation, precipitation of coldest quarter, precipitation seasonality, and precipitation of driest quarter. The model result shows that the current high potential distribution of the species is around 1.90% of the study area, 7.78% is good potential; about 90.32% is moderate to very low potential for species suitability. Finally, the results of all model represented that there will be a drastic decline in the suitable habitat distribution by 2050 and 2070 for all the RCP scenarios. The study signifies that MaxEnt model might be an efficient tool for ecosystem management, biodiversity protection, and species re-habitation planning under climate change.

Keywords: Garcinia gummi gutta, maximum entropy modeling, medicinal plants, climate change, western ghats, MaxEnt

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Authors: Leila Rashidian

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Temperature rise on Earth has had harmful effects on the Earth's surface and has led to change in precipitation patterns all around the world. The present research was aimed to study the process of climate change according to the data simulation in future and compare these parameters with current situation in the studied stations in Semnan province including Garmsar, Shahrood and Semnan. In this regard, LARS-WG software, HADCM₃ model and A₂ scenario were used for the 2010-2030 period. In this model, climatic parameters such as maximum and minimum temperature, precipitation and radiation were used daily. The obtained results indicated that there will be a 4.4% increase in precipitation in Semnan province compared with the observed data, and in general, there will be a 1.9% increase in temperature. This temperature rise has significant impact on precipitation patterns. Most of precipitation will be raining (torrential rains in some cases). According to the results, from west to east, the country will experience more temperature rise and will be warmer.

Keywords: climate change, Semnan province, Lars.WG model, climate parameters, HADCM₃ model

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Authors: Tahar Abid, Haoues Ghouss, Abdelhamid Boubertakh

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This present work is focused on the hardening precipitation in two AlMgSi(Cu) automotive body sheets. The effect of pre-aging, aging treatment and 0.10 wt % copper addition on the hardening response was investigated using scanning calorimetry (DSC), transmission electron microscopy (TEM), and Vickers microhardness measurements (Hv). The results reveal the apparition of α-AlFeSi, α-AlFe(Mn)Si type precipitates frequently present and witch remain stable at high temperature in Al-Mg-Si alloys. Indeed, the hardening response in both sheets is certainly due to the predominance of very fine typical phases β' and β'' as rods and needles developed during aging with and without pre-aging. The effect of pre ageing just after homogenization and quenching is to correct the undesirable effect of aging at ambient temperature by making faster alloy hardening during artificial aging.The addition of 0.10 wt % copper has allowed to refine and to enhance the precipitation hardening after quenching.

Keywords: AlMgSi alloys, precipitation, hardening, activation energy

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Authors: J. Suwanprateeb, F. Thammarakcharoen

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Calcium phosphate coating (CaP) has been employed for protein delivery, but the typical direct protein adsorption on the coating led to low incorporation content and fast release of the protein from the coating. By using bovine serum albumin (BSA) as a model protein, rapid biomimetic co-precipitation between calcium phosphate and BSA was employed to control the distribution of BSA within calcium phosphate coating during biomimetic formation on titanium surface for only 6 h at 50 oC in an accelerated calcium phosphate solution. As a result, the amount of BSA incorporation and release duration could be increased by using a rapid biomimetic co-precipitation technique. Up to 43 fold increases in the BSA incorporation content and the increase from 6 h to more than 360 h in release duration compared to typical direct adsorption technique were observed depending on the initial BSA concentration used during co-precipitation (1, 10, and 100 microgram/ml). From X-ray diffraction and Fourier transform infrared spectroscopy studies, the coating composition was not altered with the incorporation of BSA by this rapid biomimetic co-precipitation and mainly comprised octacalcium phosphate and hydroxyapatite. However, the microstructure of calcium phosphate crystals changed from straight, plate-like units to curved, plate-like units with increasing BSA content.

Keywords: biomimetic, Calcium Phosphate Coating, protein, titanium

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Authors: Yiyuan Tao

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Under the impact of global warming together with the intensification of human activities, the hydrological regimes may be altered, and the traditional stationary assumption was no longer satisfied. However, most of the current design standards of water infrastructures were still based on the hypothesis of stationarity, which may inevitably result in severe biases. Many critical impacts of climate on ecosystems, society, and the economy are controlled by extreme events rather than mean values. Therefore, it is of great significance to identify the non-stationarity of precipitation extremes and model the precipitation extremes in a nonstationary framework. The Wei River Basin (WRB), located in a continental monsoon climate zone in China, is selected as a case study in this study. Six extreme precipitation indices were employed to investigate the changing patterns and stationarity of precipitation extremes in the WRB. To identify if precipitation extremes are stationary, the Mann-Kendall trend test and the Pettitt test, which is used to examine the occurrence of abrupt changes are adopted in this study. Extreme precipitation indices series are fitted with non-stationary distributions that selected from six widely used distribution functions: Gumbel, lognormal, Weibull, gamma, generalized gamma and exponential distributions by means of the time-varying moments model generalized additive models for location, scale and shape (GAMLSS), where the distribution parameters are defined as a function of time. The results indicate that: (1) the trends were not significant for the whole WRB, but significant positive/negative trends were still observed in some stations, abrupt changes for consecutive wet days (CWD) mainly occurred in 1985, and the assumption of stationarity is invalid for some stations; (2) for these nonstationary extreme precipitation indices series with significant positive/negative trends, the GAMLSS models are able to capture well the temporal variations of the indices, and perform better than the stationary model. Finally, the differences between the quantiles of nonstationary and stationary models are analyzed, which highlight the importance of nonstationary modeling of precipitation extremes in the WRB.

Keywords: extreme precipitation, GAMLSSS, non-stationary, Wei River Basin

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Authors: Bernard Moeketsi Hlalele

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Drought is a complicated natural phenomenon that creates significant economic, social, and environmental problems. An analysis of paleoclimatic data indicates that severe and extended droughts are inevitable part of natural climatic circle. This study characterised drought in Lejweleputswa using both Standardised Precipitation Index (SPI) and neural networks (NN) to quantify and predict respectively. Monthly 37-year long time series precipitation data were obtained from online NASA database. Prior to the final analysis, this dataset was checked for outliers using SPSS. Outliers were removed and replaced by Expectation Maximum algorithm from SPSS. This was followed by both homogeneity and stationarity tests to ensure non-spurious results. A non-parametric Mann Kendall's test was used to detect monotonic trends present in the dataset. Two temporal scales SPI-3 and SPI-12 corresponding to agricultural and hydrological drought events showed statistically decreasing trends with p-value = 0.0006 and 4.9 x 10⁻⁷, respectively. The study area has been plagued with severe drought events on SPI-3, while on SPI-12, it showed approximately a 20-year circle. The concluded the analyses with a seasonal analysis that showed no significant trend patterns, and as such NN was used to predict possible SPI-3 for the last season of 2018/2019 and four seasons for 2020. The predicted drought intensities ranged from mild to extreme drought events to come. It is therefore recommended that farmers, agri-business owners, and other relevant stakeholders' resort to drought resistant crops as means of adaption.

Keywords: drought, risk, neural networks, agri-businesses, project, Lejweleputswa

Procedia PDF Downloads 126

Authors: Chiung-Chin Huang, Jui-Yen Lin, Yao-Hui Huang

Abstract:

Among current techniques of boron removal from wastewater with high boron concentration, chemical oxo-precipitation (COP) is one of the promising methods due to its milder condition. COP uses H2O2 to transform boric acid to perborates which can easily precipitate with barium ions at room temperature. However, the generation of the waste sludge that requires sludge/water separation and sludge dewatering is troublesome. This work presents an innovative technology which integrates chemical oxo-precipitation (COP) with fluidized-bed homogeneous granulation (FBHG) to reclaim boron as homogeneous perborate particles. By conducting COP in a fluidized-bed reactor, the barium perborate can be granulated to form homogeneous particles (>1.0 mm) with low water content (< 10%). Under the suitable condition, more than 70% of boron can be recovered from 600 ppm of boron solution and the residual boron is lower than 100 ppm.

Keywords: barium, perborate, chemical oxo-precipitation, boron removal, fluidized-bed, granulation

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Authors: Hao-Jun Hong, Jung-Tang Huang

Abstract:

The world is gradually becoming an aging society, and with the lack of laboring forces, this phenomenon is affecting the nation’s economy growth. Although nursing centers are booming in recent years, the lack of medical resources are yet to be resolved, thus creating an innovative wearable medical device could be a vital solution. This research is focused on the design and development of a wearable device which obtains a more precise heart failure measurement than products on the market. The method used by the device is based on the sensor fusion and big data algorithm. From the test result, the modified structure of wearable device can significantly decrease the MA (Motion Artifact) and provide users a more cozy and accurate physical monitor experience.

Keywords: big data, heart failure, motion artifact, sensor fusion, wearable medical device

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Authors: Mojtaba Heydarizad, Hamid Ghalibaf Mohammadabadi

Abstract:

Iran is a semi-arid and arid country in south-western Asia in the Middle East facing intense climatological drought from the early times. Therefore, studying the precipitation events and the moisture sources and air masses causing precipitation has great importance in this region. In this study, the moisture sources and stable isotope content of precipitation moisture in three main events in 2015 have been studied in North-Eastern Iran. HYSPLIT model backward trajectories showed that the Caspian Sea and the mixture of the Caspian and Mediterranean Seas are dominant moisture sources for the studied events. This showed the role of cP (Siberian) and Mediterranean (MedT) air masses. Stable isotope studies showed that precipitation events originated from the Caspian Sea with lower Sea Surface Temperature (SST) have more depleted isotope values. However, precipitation events sourced from the mixture of the Caspian and the Mediterranean Seas (with higher SST) showed more enriched isotope values.

Keywords: HYSPLIT, Iran, Northern Khorasan, stable isotopes

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Authors: Mohammad Reza Zamani Kouhpanji

Abstract:

This paper presents a MEMS/NEMS device for fatigue and fracture characterization of nanomaterials. This device can apply static loads, cyclic loads, and their combinations in nanomechanical experiments. It is based on the electromagnetic force induced between paired parallel wires carrying electrical currents. Using this concept, the actuator and sensor parts of the device were designed and analyzed while considering the practical limitations. Since the PWCC device only uses two wires for actuation part and sensing part, its fabrication process is extremely easier than the available MEMS/NEMS devices. The total gain and phase shift of the MEMS/NEMS device were calculated and investigated. Furthermore, the maximum gain and sensitivity of the MEMS/NEMS device were studied to demonstrate the capability and usability of the device for wide range of nanomaterials samples. This device can be readily integrated into SEM/TEM instruments to provide real time study of the mechanical behaviors of nanomaterials as well as their fatigue and fracture properties, softening or hardening behaviors, and initiation and propagation of nanocracks.

Keywords: sensors and actuators, MEMS/NEMS devices, fatigue and fracture nanomechanical testing device, static and cyclic nanomechanical testing device

Procedia PDF Downloads 297

Authors: Chhordaneath Hen, Ty Sok, Ilan Ich, Ratboren Chan, Chantha Oeurng

Abstract:

The damages caused by hydrological extremes such as flooding have been severe globally, and several research studies indicated extreme precipitations play a crucial role. Cambodia is one of the most vulnerable countries exposed to floods and drought as consequences of climate impact. Prek Thnot River Basin in the southwest part of Cambodia, which is in the plate and plateau region and a part of the Mekong Delta, was selected to investigate the changes in extreme precipitation and hydrological extreme. Furthermore, to develop a statistical relationship between these phenomena in this basin from 1995 to 2020 using Multiple Linear Regression. The precipitation and hydrological extreme were assessed via the attributes and trends of rainfall patterns during the study periods. The extreme flow was defined as a dependent variable, while the independent variables are various extreme precipitation indices. The study showed that all extreme precipitations indices (R10, R20, R35, CWD, R95p, R99p, and PRCPTOT) had increasing decency. However, the number of rain days per year had a decreasing tendency, which can conclude that extreme rainfall was more intense in a shorter period of the year. The study showed a similar relationship between extreme precipitation and hydrological extreme and land use change association with hydrological extreme. The direct combination of land use and precipitation equals 37% of the flood causes in this river. This study provided information on these two causes of flood events and an understanding of expectations of climate change consequences for flood and water resources management.

Keywords: extreme precipitation, hydrological extreme, land use, land cover, Prek Thnot river basin

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Authors: Mahtsente Tadese, Lalit Kumar, Richard Koech

Abstract:

The aim of this study was to project and analyze climate change in the Awash River Basin (ARB) using bias-corrected Global and Regional Climate Model simulations. The analysis included a baseline period from 1986-2005 and two future scenarios (the 2050s and 2070s) under two representative concentration pathways (RCP4.5 and RCP8.5). Bias correction methods were evaluated using graphical and statistical methods. Following the evaluation of bias correction methods, the Distribution Mapping (DM) and Power Transformation (PT) were used for temperature and precipitation projection, respectively. The 2050s and 2070s RCP4 simulations showed an increase in precipitation during half of the months with 32 and 10%, respectively. Moreover, the 2050s and 2070s RCP8.5 simulation indicated a decrease in precipitation with 18 and 26%, respectively. The 2050s and 2070s RCP8.5 simulation indicated a significant decrease in precipitation in four of the months (February/March to May) with the highest decreasing rate of 34.7%. The 2050s and 2070s RCP4.5 simulation showed an increase of 0.48-2.6 °C in maximum temperature. In the case of RCP8.5, the increase rate reached 3.4 °C and 4.1 °C in the 2050s and 2070s, respectively. The changes in precipitation and temperature might worsen the water stress, flood, and drought in ARB. Moreover, the critical focus should be given to mitigation strategies and management options to reduce the negative impact. The findings of this study provide valuable information on future precipitation and temperature change in ARB, which will help in the planning and design of sustainable mitigation approaches in the basin.

Keywords: variability, climate change, Awash River Basin, precipitation

Procedia PDF Downloads 174