Search results for: pose estimation

Authors: Amarachukwu B. Isiaka, Vivian N. Anakwenze, Chinyere C. Ezemba, Chiamaka R. Ilodinso, Chikodili G. Anaukwu, Chukwuebuka M. Ezeokoli, Ugonna H. Uzoka

Abstract:

Infectious diseases continue to pose significant threats to global public health, necessitating advanced and timely detection methods for effective outbreak management. This study explores the integration of artificial intelligence (AI) in the early detection and management of infectious disease outbreaks. Leveraging vast datasets from diverse sources, including electronic health records, social media, and environmental monitoring, AI-driven algorithms are employed to analyze patterns and anomalies indicative of potential outbreaks. Machine learning models, trained on historical data and continuously updated with real-time information, contribute to the identification of emerging threats. The implementation of AI extends beyond detection, encompassing predictive analytics for disease spread and severity assessment. Furthermore, the paper discusses the role of AI in predictive modeling, enabling public health officials to anticipate the spread of infectious diseases and allocate resources proactively. Machine learning algorithms can analyze historical data, climatic conditions, and human mobility patterns to predict potential hotspots and optimize intervention strategies. The study evaluates the current landscape of AI applications in infectious disease surveillance and proposes a comprehensive framework for their integration into existing public health infrastructures. The implementation of an AI-driven early detection system requires collaboration between public health agencies, healthcare providers, and technology experts. Ethical considerations, privacy protection, and data security are paramount in developing a framework that balances the benefits of AI with the protection of individual rights. The synergistic collaboration between AI technologies and traditional epidemiological methods is emphasized, highlighting the potential to enhance a nation's ability to detect, respond to, and manage infectious disease outbreaks in a proactive and data-driven manner. The findings of this research underscore the transformative impact of harnessing AI for early detection and management, offering a promising avenue for strengthening the resilience of public health systems in the face of evolving infectious disease challenges. This paper advocates for the integration of artificial intelligence into the existing public health infrastructure for early detection and management of infectious disease outbreaks. The proposed AI-driven system has the potential to revolutionize the way we approach infectious disease surveillance, providing a more proactive and effective response to safeguard public health.

Keywords: artificial intelligence, early detection, disease surveillance, infectious diseases, outbreak management

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Authors: Malik Abid Hussain Khokhar, Muhammad Adnan Tahir, Hisham Bin Hafeez Awan

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Continuous environmental determinism and climatic change in the entire globe due to increasing land surface temperature (LST) has become a vital phenomenon nowadays. LST is accelerating because of increasing greenhouse gases in the environment which results of melting down ice caps, ice sheets and glaciers. It has not only worse effects on vegetation and water bodies of the region but has also severe impacts on monsoon areas in the form of capricious rainfall and monsoon failure extensive precipitation. Environment can be monitored with the help of various geographic information systems (GIS) based algorithms i.e. SC (Single), DA (Dual Angle), Mao, Sobrino and SW (Split Window). Estimation of LST is very much possible from digital image processing of satellite imagery. This paper will encompass extraction of LST of Abbottabad using SW technique of GIS and Remote Sensing over last ten years by means of Landsat 7 ETM+ (Environmental Thematic Mapper) and Landsat 8 vide their Thermal Infrared (TIR Sensor) and Optical Land Imager (OLI sensor less Landsat 7 ETM+) having 100 m TIR resolution and 30 m Spectral Resolutions. These sensors have two TIR bands each; their emissivity and spectral radiance will be used as input statistics in SW algorithm for LST extraction. Emissivity will be derived from Normalized Difference Vegetation Index (NDVI) threshold methods using 2-5 bands of OLI with the help of e-cognition software, and spectral radiance will be extracted TIR Bands (Band 10-11 and Band 6 of Landsat 7 ETM+). Accuracy of results will be evaluated by weather data as well. The successive research will have a significant role for all tires of governing bodies related to climate change departments.

Keywords: environment, Landsat 8, SW Algorithm, TIR

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Authors: Ehsan Pegah, Huabei Liu

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Determination of the internal friction angle (φ) in natural soil deposits is an important issue in geotechnical engineering. The main objective of this study was to examine the evaluation of this parameter in overconsolidated granular soil deposits by using the P-wave velocity and the anisotropic components of S-wave velocity (i.e., both the vertical component (SV) and the horizontal component (SH) of S-wave). To this end, seventeen pairs of P-wave and S-wave seismic refraction profiles were carried out at three different granular sites in Iran using non-invasive seismic wave methods. The acquired shot gathers were processed, from which the P-wave, SV-wave and SH-wave velocities were derived. The reference values of φ and overconsolidation ratio (OCR) in the soil deposits were measured through laboratory tests. By assuming cross-anisotropy of the soils, the P-wave and S-wave velocities were utilized to develop an equation for calculating the coefficient of lateral earth pressure at-rest (K₀) based on the theory of elasticity for a cross-anisotropic medium. In addition, to develop an equation for OCR estimation in granular geomaterials in terms of SH/SV velocity ratios, a general regression analysis was performed on the resulting information from this research incorporated with the respective data published in the literature. The calculated K₀ values coupled with the estimated OCR values were finally employed in the Mayne and Kulhawy formula to evaluate φ in granular soil deposits. The results showed that reliable values of φ could be estimated based on the seismic wave velocities. The findings of this study may be used as the appropriate approaches for economic and non-invasive determination of in-situ φ in granular soil deposits using the surface seismic surveys.

Keywords: angle of internal friction, overconsolidation ratio, granular soils, P-wave velocity, SV-wave velocity, SH-wave velocity

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Authors: Michel Soto Chalhoub

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Building code-related literature provides recommendations on normalizing approaches to the calculation of the dynamic properties of structures. Most building codes make a distinction among types of structural systems, construction material, and configuration through a numerical coefficient in the expression for the fundamental period. The period is then used in normalized response spectra to compute base shear. The typical parameter used in simplified code formulas for the fundamental period is overall building height raised to a power determined from analytical and experimental results. However, reinforced concrete buildings which constitute the majority of built space in less developed countries pose additional challenges to the ones built with homogeneous material such as steel, or with concrete under stricter quality control. In the present paper, the particularities of reinforced concrete buildings are explored and related to current methods of equivalent static analysis. A comparative study is presented between the Uniform Building Code, commonly used for buildings within and outside the USA, and data from the Middle East used to model 151 reinforced concrete buildings of varying number of bays, number of floors, overall building height, and individual story height. The fundamental period was calculated using eigenvalue matrix computation. The results were also used in a separate regression analysis where the computed period serves as dependent variable, while five building properties serve as independent variables. The statistical analysis shed light on important parameters that simplified code formulas need to account for including individual story height, overall building height, floor plan, number of bays, and concrete properties. Such inclusions are important for reinforced concrete buildings of special conditions due to the level of concrete damage, aging, or materials quality control during construction. Overall results of the present analysis show that simplified code formulas for fundamental period and base shear may be applied but they require revisions to account for multiple parameters. The conclusion above is confirmed by the analytical model where fundamental periods were computed using numerical techniques and eigenvalue solutions. This recommendation is particularly relevant to code upgrades in less developed countries where it is customary to adopt, and mildly adapt international codes. We also note the necessity of further research using empirical data from buildings in Lebanon that were subjected to severe damage due to impulse loading or accelerated aging. However, we excluded this study from the present paper and left it for future research as it has its own peculiarities and requires a different type of analysis.

Keywords: seismic behaviour, reinforced concrete, simplified code formulas, equivalent static analysis, base shear, response spectra

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Authors: Thakur Pushpawati, Singh Vinita, Agrawal Sarita, Mohapatra Eli

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Polycystic ovary syndrome (PCOS) is a disease of endocrine and frequently encountered in women in their reproductive period, and it is characterized by clinical features of anovulation, clinical and biochemical features of hyperandrogenism, and PCOS morphology on ultrasonographic examination. In Indian scenario, only a few studies are available on the correlation of serum 25-dihydroxy vitamin D3 level and insulin level. The present study is a prospective case-control study and aims to estimate the concentration of serum 25-dihydroxy vitamin D3 and insulin resistance and determine the association of serum 25-dihydroxy vitamin D3 with insulin resistance in PCOS women of 18-40 years age group. In this study, the primary objective is to estimate the concentration of 25-dihydroxy vitamin D3, insulin, glycaemic status, calcium and phosphorus levels in 18-40 year age women with polycystic ovary syndrome and to compare these parameters with age and BMI matched healthy control of same age group women. The secondary objective is to determine the association between 25-dihydroxy vitamin D3 concentration and insulin resistance among PCOS cases in 18-40 years age group women. This study was carried on at outpatient Department of Obstetrics & Gynaecology, Aiims Raipur. It took one year from the date of approval. In case, 32 women were diagnosed (Diagnosed PCOS cases as per Rotterdoms criteria among women of 18-40 years of age), as control group 32 women of 18-40 years of age were diagnosed As a result, serum insulin level was elevated among PCOS women along with 25-dihydroxy vitamin D3 deficiency.Conclude up, PCOS is more common in the age group of 20-40 years. There is a strong correlation between vitamin D deficiency and insulin resistance among PCOS patients.

Keywords: vitamin D, insulin resistance, PCOS, reproductive age group

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Authors: Ana Flavia Belchior De Andrade

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Forensic laboratories all over the world face a great challenge to overcame waiting time and backlog in many different areas. Many aspects contribute to this situation, such as an increase in drug complexity, increment in the number of exams requested and cuts in funding limiting laboratories hiring capacity. Altogether, those facts pose an essential challenge for forensic chemistry laboratories to keep both quality and time of response within an acceptable period. In this paper we will analyze how the backlog affects test results and, in the end, the whole judicial system. In this study data from marijuana samples seized by the Federal District Civil Police in Brazil between the years 2013 and 2017 were tabulated and the results analyzed and discussed. In the last five years, the number of petitioned exams increased from 822 in February 2013 to 1358 in March 2018, representing an increase of 32% in 5 years, a rise of more than 6% per year. Meanwhile, our data shows that the number of performed exams did not grow at the same rate. Product numbers are stationed as using the actual technology scenario and analyses routine the laboratory is running in full capacity. Marijuana detection is the most prevalence exam required, representing almost 70% of all exams. In this study, data from 7,110 (seven thousand one hundred and ten) marijuana samples were analyzed. Regarding waiting time, most of the exams were performed not later than 60 days after receipt (77%). Although some samples waited up to 30 months before being examined (0,65%). When marijuana´s exam is delayed we notice the enlargement of inconclusive results using thin-layer chromatography (TLC). Our data shows that if a marijuana sample is stored for more than 18 months, inconclusive results rise from 2% to 7% and when if storage exceeds 30 months, inconclusive rates increase to 13%. This is probably because Cannabis plants and preparations undergo oxidation under storage resulting in a decrease in the content of Δ9-tetrahydrocannabinol ( Δ9-THC). An inconclusive result triggers other procedures that require at least two more working hours of our analysts (e.g., GC/MS analysis) and the report would be delayed at least one day. Those new procedures increase considerably the running cost of a forensic drug laboratory especially when the backlog is significant as inconclusive results tend to increase with waiting time. Financial aspects are not the only ones to be observed regarding backlog cases; there are also social issues as legal procedures can be delayed and prosecution of serious crimes can be unsuccessful. Delays may slow investigations and endanger public safety by giving criminals more time on the street to re-offend. This situation also implies a considerable cost to society as at some point, if the exam takes a long time to be performed, an inconclusive can turn into a negative result and a criminal can be absolved by flawed expert evidence.

Keywords: backlog, forensic laboratory, quality management, accreditation

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Authors: Xiaohua Zhu, Lingling Ma, Yongguang Zhao

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Leaf Area Index (LAI) is a key structural characteristic of crops and plays a significant role in precision agricultural management and farmland ecosystem modeling. However, LAI retrieved from different resolution data contain a scaling bias due to the spatial heterogeneity and model non-linearity, that is, there is scale effect during multi-scale LAI estimate. In this article, a typical farmland in semi-arid regions of Chinese Inner Mongolia is taken as the study area, based on the combination of PROSPECT model and SAIL model, a multiple dimensional Look-Up-Table (LUT) is generated for multiple crops LAI estimation from unmanned aerial vehicle (UAV) hyperspectral data. Based on Taylor expansion method and computational geometry model, a scale transfer model considering both difference between inter- and intra-class is constructed for scale effect analysis of LAI inversion over inhomogeneous surface. The results indicate that, (1) the LUT method based on classification and parameter sensitive analysis is useful for LAI retrieval of corn, potato, sunflower and melon on the typical farmland, with correlation coefficient R2 of 0.82 and root mean square error RMSE of 0.43m2/m-2. (2) The scale effect of LAI is becoming obvious with the decrease of image resolution, and maximum scale bias is more than 45%. (3) The scale effect of inter-classes is higher than that of intra-class, which can be corrected efficiently by the scale transfer model established based Taylor expansion and Computational geometry. After corrected, the maximum scale bias can be reduced to 1.2%.

Keywords: leaf area index (LAI), scale effect, UAV-based hyperspectral data, look-up-table (LUT), remote sensing

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Authors: Rajkumar Ghosh

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Earthquakes pose significant risks to human life and infrastructure, highlighting the importance of effective earthquake mitigation strategies. Traditional earthquake modelling and mitigation efforts have largely focused on the primary fault segments and their slip behaviour. However, earthquakes can exhibit complex rupture dynamics, including out-of-sequence thrust (OOST) events, which occur on secondary or subsidiary faults. This abstract examines the impact of OOST dynamics on earthquake mitigation strategies and their implications for hazard assessment and disaster planning. OOST events challenge conventional seismic hazard assessments by introducing additional fault segments and potential rupture scenarios that were previously unrecognized or underestimated. Consequently, these events may increase the overall seismic hazard in affected regions. The study reviews recent case studies and research findings that illustrate the occurrence and characteristics of OOST events. It explores the factors contributing to OOST dynamics, such as stress interactions between fault segments, fault geometry, and mechanical properties of fault materials. Moreover, it investigates the potential triggers and precursory signals associated with OOST events to enhance early warning systems and emergency response preparedness. The abstract also highlights the significance of incorporating OOST dynamics into seismic hazard assessment methodologies. It discusses the challenges associated with accurately modelling OOST events, including the need for improved understanding of fault interactions, stress transfer mechanisms, and rupture propagation patterns. Additionally, the abstract explores the potential for advanced geophysical techniques, such as high-resolution imaging and seismic monitoring networks, to detect and characterize OOST events. Furthermore, the abstract emphasizes the practical implications of OOST dynamics for earthquake mitigation strategies and urban planning. It addresses the need for revising building codes, land-use regulations, and infrastructure designs to account for the increased seismic hazard associated with OOST events. It also underscores the importance of public awareness campaigns to educate communities about the potential risks and safety measures specific to OOST-induced earthquakes. This sheds light on the impact of out-of-sequence thrust dynamics in earthquake mitigation. By recognizing and understanding OOST events, researchers, engineers, and policymakers can improve hazard assessment methodologies, enhance early warning systems, and implement effective mitigation measures. By integrating knowledge of OOST dynamics into urban planning and infrastructure development, societies can strive for greater resilience in the face of earthquakes, ultimately minimizing the potential for loss of life and infrastructure damage.

Keywords: earthquake mitigation, out-of-sequence thrust, seismic, satellite imagery

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Authors: Sara Seddiki, Maazouzi Abdelhak

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The main objective of this modest work is to follow the physicochemical and bacteriological evolution of the wastewater from the town of Bechar subjected to purification by filtration according to various local supports, namely Sable and Terrill by reducing nuisances that undergo the receiving environment (Oued Bechar) and therefore make this water source reusable in different areas. The study first made it possible to characterize the urban wastewater of the Bechar wadi, which presents an environmental threat, thus allowing an estimation of the pollutant load, the chemical oxygen demand COD (145 mg / l) and the biological oxygen demand BOD5 (72 mg / l) revealed that these waters are less biodegradable (COD / BOD5 ratio = 0.62), have a fairly high conductivity (2.76 mS/cm), and high levels of mineral matter presented by chlorides and sulphates 390 and 596.1 mg / l respectively, with a pH of 8.1. The characterization of the sand dune (Beni Abbes) shows that quartz (97%) is the most present mineral. The granular analysis allowed us to determine certain parameters like the uniformity coefficient (CU) and the equivalent diameter, and scanning electron microscope (SEM) observations and X-ray analysis were performed. The study of filtered wastewater shows satisfactory and very encouraging treatment results, with complete elimination of total coliforms and streptococci and a good reduction of total aerobic germs in the sand and clay-sand filter. A good yield has been reported in the sand Terrill filter for the reduction of turbidity. The rates of reduction of organic matter in terms of the biological oxygen demand, in chemical oxygen demand recorded, are of the order of 60%. The elimination of sulphates is 40% for the sand filter.

Keywords: urban wastewater, filtration, bacteriological and physicochemical parameters, sand, Terrill, Oued Bechar

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Authors: Navneet Singh Deora, Aastha Deswal, H. N. Mishra

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Pasta is one of the most widely consumed food products around the world. Rapid determination of the moisture content in pasta will assist food processors to provide online quality control of pasta during large scale production. Rapid Fourier transform near-infrared method (FT-NIR) was developed for determining moisture content in pasta. A calibration set of 150 samples, a validation set of 30 samples and a prediction set of 25 samples of pasta were used. The diffuse reflection spectra of different types of pastas were measured by FT-NIR analyzer in the 4,000-12,000 cm-1 spectral range. Calibration and validation sets were designed for the conception and evaluation of the method adequacy in the range of moisture content 10 to 15 percent (w.b) of the pasta. The prediction models based on partial least squares (PLS) regression, were developed in the near-infrared. Conventional criteria such as the R2, the root mean square errors of cross validation (RMSECV), root mean square errors of estimation (RMSEE) as well as the number of PLS factors were considered for the selection of three pre-processing (vector normalization, minimum-maximum normalization and multiplicative scatter correction) methods. Spectra of pasta sample were treated with different mathematic pre-treatments before being used to build models between the spectral information and moisture content. The moisture content in pasta predicted by FT-NIR methods had very good correlation with their values determined via traditional methods (R2 = 0.983), which clearly indicated that FT-NIR methods could be used as an effective tool for rapid determination of moisture content in pasta. The best calibration model was developed with min-max normalization (MMN) spectral pre-processing (R2 = 0.9775). The MMN pre-processing method was found most suitable and the maximum coefficient of determination (R2) value of 0.9875 was obtained for the calibration model developed.

Keywords: FT-NIR, pasta, moisture determination, food engineering

Procedia PDF Downloads 232

Authors: Nezmin Kayedpour, Arash E. Samani, Siavash Asiaban, Jeroen M. De Kooning, Lieven Vandevelde, Guillaume Crevecoeur

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The growing adoption of renewable energy sources, such as wind power, in electricity generation is a significant step towards a sustainable and decarbonized future. However, the inherent intermittency and uncertainty of wind resources pose challenges to the reliable and stable operation of power grids. To address this, hydrogen storage systems have emerged as a promising and versatile technology to support grid balancing services in wind energy conversion systems. In this study, we propose a supplementary control design that enhances the performance of the hydrogen storage system by integrating wind turbine (WT) pitch and torque control systems. These control strategies aim to optimize the hydrogen production process, ensuring efficient utilization of wind energy while complying with grid requirements. The wind turbine pitch control system plays a crucial role in managing the turbine's aerodynamic performance. By adjusting the blade pitch angle, the turbine's rotational speed and power output can be regulated. Our proposed control design dynamically coordinates the pitch angle to match the wind turbine's power output with the optimal hydrogen production rate. This ensures that the electrolyzer receives a steady and optimal power supply, avoiding unnecessary strain on the system during high wind speeds and maximizing hydrogen production during low wind speeds. Moreover, the wind turbine torque control system is incorporated to facilitate efficient operation at varying wind speeds. The torque control system optimizes the energy capture from the wind while limiting mechanical stress on the turbine components. By harmonizing the torque control with hydrogen production requirements, the system maintains stable wind turbine operation, thereby enhancing the overall energy-to-hydrogen conversion efficiency. To enable grid-friendly operation, we introduce a cascaded controller that regulates the electrolyzer's electrical power-current in accordance with grid requirements. This controller ensures that the hydrogen production rate can be dynamically adjusted based on real-time grid demands, supporting grid balancing services effectively. By maintaining a close relationship between the wind turbine's power output and the electrolyzer's current, the hydrogen storage system can respond rapidly to grid fluctuations and contribute to enhanced grid stability. In this paper, we present a comprehensive analysis of the proposed supplementary control design's impact on the overall performance of the hydrogen storage system in wind energy conversion systems. Through detailed simulations and case studies, we assess the system's ability to provide grid balancing services, maximize wind energy utilization, and reduce greenhouse gas emissions.

Keywords: active power control, electrolyzer, grid balancing services, wind energy conversion systems

Procedia PDF Downloads 53

Authors: Zain Ul Hassan, Muhammad Zain Ul Abadin, Muhammad Zubair Khan

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Unmanned aerial vehicles (UAVs) have now become of predominant importance for various operations, and an immense amount of work is going on in this specific category. The structural stability and life of these UAVs is key factor that should be considered while deploying them to different intelligent operations as their failure leads to loss of sensitive real-time data and cost. This paper presents an applied research on the development of a structural health monitoring system for a UAV designed and fabricated by deploying modular approach. Firstly, a modular UAV has been designed which allows to dismantle and to reassemble the components of the UAV without effecting the whole assembly of UAV. This novel approach makes the vehicle very sustainable and decreases its maintenance cost to a significant value by making possible to replace only the part leading to failure. Then the SHM for the designed architecture of the UAV had been specified as a combination of wings integrated with strain gauges, on-board data logger, bridge circuitry and the ground station. For the research purpose sensors have only been attached to the wings being the most load bearing part and as per analysis was done on ANSYS. On the basis of analysis of the load time spectrum obtained by the data logger during flight, fatigue life of the respective component has been predicted using fracture mechanics techniques of Rain Flow Method and Miner’s Rule. Thus allowing us to monitor the health of a specified component time to time aiding to avoid any failure.

Keywords: fracture mechanics, rain flow method, structural health monitoring system, unmanned aerial vehicle

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Authors: Hayriye Anıl, Görkem Kar

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In addition to being rich in renewable energy resources, Turkey is one of the countries that promise potential in geothermal energy production with its high installed power, cheapness, and sustainability. Increasing imbalance penalties become an economic burden for organizations since geothermal generation plants cannot maintain the balance of supply and demand due to the inadequacy of the production forecasts given in the day-ahead market. A better production forecast reduces the imbalance penalties of market participants and provides a better imbalance in the day ahead market. In this study, using machine learning, deep learning, and, time series methods, the total generation of the power plants belonging to Zorlu Natural Electricity Generation, which has a high installed capacity in terms of geothermal, was estimated for the first one and two weeks of March, then the imbalance penalties were calculated with these estimates and compared with the real values. These modeling operations were carried out on two datasets, the basic dataset and the dataset created by extracting new features from this dataset with the feature engineering method. According to the results, Support Vector Regression from traditional machine learning models outperformed other models and exhibited the best performance. In addition, the estimation results in the feature engineering dataset showed lower error rates than the basic dataset. It has been concluded that the estimated imbalance penalty calculated for the selected organization is lower than the actual imbalance penalty, optimum and profitable accounts.

Keywords: machine learning, deep learning, time series models, feature engineering, geothermal energy production forecasting

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Authors: Mai. M. Toughan, Ahmed A. A. Sallam, Ashraf O. Abd El-Latif

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Termites are eusocial insects that are found on all continents except Antarctica. Termites have serious destructive impact, damaging local huts and crops of poor subsistence. The annual cost of termite damage and its control is determined in the billions globally. In Egypt, most of these damages are due to the subterranean termite species especially the sand termite, P. hypostoma. Pyrethroids became the primary weapon for subterranean termite control, after the use of chlorpyrifos as a soil termiticide was banned. Despite the important role of pyrethroids in termite control, its extensive use in pest control led to the eventual rise of insecticide resistance which may make many of the pyrethroids ineffective. The ability to diagnose the precise mechanism of pyrethroid resistance in any insect species would be the key component of its management at specified location for a specific population. In the present study, detailed toxicological and biochemical studies was conducted on the mechanism of pyrethroid resistance in P. hypostoma. The susceptibility of field populations of P. hypostoma against deltamethrin, α-cypermethrin and ƛ-cyhalothrin was evaluated. The obtained results revealed that the workers of P. hypostoma have developed high resistance level against the tested pyrethroids. Studies carried out through estimation of detoxification enzyme activity indicated that enhanced esterase and cytochrome P450 activities were probably important mechanisms for pyrethroid resistance in field populations. Elevated esterase activity and also additional esterase isozyme were observed in the pyrethroid-resistant populations compared to the susceptible populations. Strong positive correlation between cytochrome P450 activity and pyrethroid resistance was also reported. |Deltamethrin could be recommended as a resistance-breaking pyrethroid that is active against resistant populations of P. hypostoma.

Keywords: Psammotermes hypostoma, pyrethroid resistance, esterase, cytochrome P450

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Authors: Samy A. Khalil, U. Ali Rahoma

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The measurements of solar radiation, satellite data has been routinely utilize to estimate solar energy. However, the temporal coverage of satellite data has some limits. The reanalysis, also known as "retrospective analysis" of the atmosphere's parameters, is produce by fusing the output of NWP (Numerical Weather Prediction) models with observation data from a variety of sources, including ground, and satellite, ship, and aircraft observation. The result is a comprehensive record of the parameters affecting weather and climate. The effectiveness of reanalysis datasets (ERA-5) for North Africa was evaluate against high-quality surfaces measured using statistical analysis. Estimating the distribution of global solar radiation (GSR) over five chosen areas in North Africa through ten-years during the period time from 2011 to 2020. To investigate seasonal change in dataset performance, a seasonal statistical analysis was conduct, which showed a considerable difference in mistakes throughout the year. By altering the temporal resolution of the data used for comparison, the performance of the dataset is alter. Better performance is indicate by the data's monthly mean values, but data accuracy is degraded. Solar resource assessment and power estimation are discuses using the ERA-5 solar radiation data. The average values of mean bias error (MBE), root mean square error (RMSE) and mean absolute error (MAE) of the reanalysis data of solar radiation vary from 0.079 to 0.222, 0.055 to 0.178, and 0.0145 to 0.198 respectively during the period time in the present research. The correlation coefficient (R2) varies from 0.93 to 99% during the period time in the present research. This research's objective is to provide a reliable representation of the world's solar radiation to aid in the use of solar energy in all sectors.

Keywords: solar energy, ERA-5 analysis data, global solar radiation, North Africa

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Authors: Mehmet Akif Bütüner, İlhan Koşalay

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Hydroelectric power plants (HEPPs) are renewable energy power plants with the highest installed power in the world. While the control systems operating in these power plants ensure that the system operates at the desired operating point, it is also responsible for stopping the relevant unit safely in case of any malfunction. While these control systems are expected not to miss signals that require stopping, on the other hand, it is desired not to cause unnecessary stops. In traditional control systems including modern systems with SCADA infrastructure, alarm conditions to create warnings or trip conditions to put relevant unit out of service automatically are usually generated with predefined limits regardless of different operating conditions. This approach results in alarm/trip conditions to be less likely to detect minimal changes which may result in serious malfunction scenarios in near future. With the methods proposed in this research, routine behavior of the oil circulation of hydraulic governor of a HEPP will be modeled with machine learning methods using historical data obtained from SCADA system. Using the created model and recently gathered data from control system, oil pressure of hydraulic accumulators will be estimated. Comparison of this estimation with the measurements made and recorded instantly by the SCADA system will help to foresee failure before becoming worse and determine remaining useful life. By using model outputs, maintenance works will be made more planned, so that undesired stops are prevented, and in case of any malfunction, the system will be stopped or several alarms are triggered before the problem grows.

Keywords: hydroelectric, governor, anomaly detection, machine learning, regression

Procedia PDF Downloads 60

Authors: Aniruddha Mitra, Abbas Rashidi, Shane Lewis, Jefferson Doehling, Alexis Pawlak, Jacob Schwartz, Imaobong Ekpo, Atin Adhikari

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Working or living close to demolition sites can increase risks of dust-related health problems. Demolition of concrete buildings may produce crystalline silica dust, which can be associated with a broad range of respiratory diseases including silicosis and lung cancers. Previous studies demonstrated significant associations between demolition dust exposure and increase in the incidence of mesothelioma or asbestos cancer. Dust is a generic term used for minute solid particles of typically <500 µm in diameter. Dust particles in demolition sites vary in a wide range of sizes. Larger particles tend to settle down from the air. On the other hand, the smaller and lighter solid particles remain dispersed in the air for a long period and pose sustained exposure risks. Submicron ultrafine particles and nanoparticles are respirable deeper into our alveoli beyond our body’s natural respiratory cleaning mechanisms such as cilia and mucous membranes and are likely to be retained in the lower airways. To our knowledge, how various demolition tasks release nanoparticles are largely unknown and previous studies mostly focused on course dust, PM2.5, and PM10. General belief is that the dust generated during demolition tasks are mostly large particles formed through crushing, grinding, or sawing of various concrete and wooden structures. Therefore, little consideration has been given to the generated submicron ultrafine and nanoparticles and their exposure levels. These data are, however, critically important because recent laboratory studies have demonstrated cytotoxicity of nanoparticles on lung epithelial cells. The above-described knowledge gaps were addressed in this study by a novel newly developed nanoparticle monitor, which was used for nanoparticle monitoring at two adjacent indoor and outdoor building demolition sites in southern Georgia. Nanoparticle levels were measured (n = 10) by TSI NanoScan SMPS Model 3910 at four different distances (5, 10, 15, and 30 m) from the work location as well as in control sites. Temperature and relative humidity levels were recorded. Indoor demolition works included acetylene torch, masonry drilling, ceiling panel removal, and other miscellaneous tasks. Whereas, outdoor demolition works included acetylene torch and skid-steer loader use to remove a HVAC system. Concentration ranges of nanoparticles of 13 particle sizes at the indoor demolition site were: 11.5 nm: 63 – 1054/cm³; 15.4 nm: 170 – 1690/cm³; 20.5 nm: 321 – 730/cm³; 27.4 nm: 740 – 3255/cm³; 36.5 nm: 1,220 – 17,828/cm³; 48.7 nm: 1,993 – 40,465/cm³; 64.9 nm: 2,848 – 58,910/cm³; 86.6 nm: 3,722 – 62,040/cm³; 115.5 nm: 3,732 – 46,786/cm³; 154 nm: 3,022 – 21,506/cm³; 205.4 nm: 12 – 15,482/cm³; 273.8 nm: Keywords: demolition dust, industrial hygiene, aerosol, occupational exposure

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Authors: Mohamed Ali Ben Halima, Malik Koubi, Joseph Lanfranchi, Yohan Wloczysiak

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This article assesses the consequences on the frequency and duration of ordinary sick leave of the January 2012 and 2018 reforms modifying the scope of sick leave reimbursement in the French civil service. These reforms introduce a one-day waiting period which removes the compensation for the first day of ordinary sick leave. In order to evaluate these reforms, we use an administrative database from the National Pension Fund for local public employees (FPT). The first important result of our data analysis is that the one-day waiting period was not introduced at the same time in the French Local Public Service establishments, or even never in some. This peculiarity allows for an identification strategy using a difference-in-differences method based on the definition at each date of groups of employees treated and not treated by the reform, since establishments that apply the one-day waiting period coexist with establishments that do not apply it. Two types of estimators are used for this evaluation: individual and time fixed effects estimators and DIDM estimators which correct for the biases of the Two Way Fixed Effects one. The results confirm that the change in the sick pay system decreases the probability of having at least one ordinary sick leave as well as the number and duration of these episodes. On the other hand, the estimates show that longer leave episodes are not less affected than shorter ones. Finally, the validity tests of the estimators support the results obtained for the second period of 2018-2019, but suggest estimation biases for the period 2012-2013. The extent to which the endogeneity of the choices of implementation of the reform at the local level impact these estimates needs to be further tested.

Keywords: sick leave, one-day waiting period, territorial civil service, public policy evaluation

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Authors: Marko Batic, Nikola Tomasevic, Sanja Vranes

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One of the strongholds in the ubiquitous efforts related to the energy conservation and energy efficiency improvement is represented by the retrofit of high energy consumers in buildings. In general, HVAC systems represent the highest energy consumers in buildings. However they usually suffer from mal-operation and/or malfunction, causing even higher energy consumption than necessary. Various Fault Detection and Diagnosis (FDD) systems can be successfully employed for this purpose, especially when it comes to the application at a single device/unit level. In the case of more complex systems, where multiple devices are operating in the context of the same building, significant energy efficiency improvements can only be achieved through application of comprehensive FDD systems relying on additional higher level knowledge, such as their geographical location, served area, their intra- and inter- system dependencies etc. This paper presents a comprehensive FDD system that relies on the utilization of common knowledge repository that stores all critical information. The discussed system is deployed as a test-bed platform at the two at Fiumicino and Malpensa airports in Italy. This paper aims at presenting advantages of implementation of the knowledge base through the utilization of ontology and offers improved functionalities of such system through examples of typical queries and reasoning that enable derivation of high level energy conservation measures (ECM). Therefore, key SPARQL queries and SWRL rules, based on the two instantiated airport ontologies, are elaborated. The detection of high level irregularities in the operation of airport heating/cooling plants is discussed and estimation of energy savings is reported.

Keywords: airport ontology, knowledge management, ontology modeling, reasoning

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Authors: P. S. Thein, S. Pramumijoyo, K. S. Brotopuspito, J. Kiyono, W. Wilopo, A. Furukawa, A. Setianto

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In this study, we estimated the seismic ground motion parameters based on microtremor measurements at Palu City. Several earthquakes have struck along the Palu-Koro Fault during recent years. The USGS epicenter, magnitude Mw 6.3 event that occurred on January 23, 2005 caused several casualties. We conducted a microtremor survey to estimate the strong ground motion distribution during the earthquake. From this survey we produced a map of the peak ground acceleration, velocity, seismic vulnerability index and ground shear strain maps in Palu City. We performed single observations of microtremor at 151 sites in Palu City. We also conducted 8-site microtremors array investigation to gain a representative determination of the soil condition of subsurface structures in Palu City. From the array observations, Palu City corresponds to relatively soil condition with Vs ≤ 300 m/s, the predominant periods due to horizontal vertical ratios (HVSRs) are in the range of 0.4 to 1.8 s and the frequency are in the range of 0.7 to 3.3 Hz. Strong ground motions of the Palu area were predicted based on the empirical stochastic green’s function method. Peak ground acceleration and velocity becomes more than 400 gal and 30 kine in some areas, which causes severe damage for buildings in high probability. Microtremor survey results showed that in hilly areas had low seismic vulnerability index and ground shear strain, whereas in coastal alluvium was composed of material having a high seismic vulnerability and ground shear strain indication.

Keywords: Palu-Koro fault, microtremor, peak ground acceleration, peak ground velocity, seismic vulnerability index

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Authors: Rajkumar Ghosh

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Fluoride contamination is a growing concern in various regions across the globe, including North 24 Parganas in West Bengal, India. The presence of excessive fluoride in the environment can have detrimental effects on crops, soil quality, and water resources. This note aims to shed light on the implications of fluoride contamination and its impact on the agricultural sector in North 24 Parganas. The agricultural lands in North 24 Parganas have been significantly affected by fluoride contamination, leading to adverse consequences for crop production. Excessive fluoride uptake by plants can hinder their growth, reduce crop yields, and impact the quality of agricultural produce. Certain crops, such as paddy, vegetables, and fruits, are more susceptible to fluoride toxicity, resulting in stunted growth, leaf discoloration, and reduced nutritional value. Fluoride-contaminated water, often used for irrigation, contributes to the accumulation of fluoride in the soil. Over time, this can lead to soil degradation and reduced fertility. High fluoride levels can alter soil pH, disrupt the availability of essential nutrients, and impair microbial activity critical for nutrient cycling. Consequently, the overall health and productivity of the soil are compromised, making it increasingly challenging for farmers to sustain agricultural practices. Fluoride contamination in North 24 Parganas extends beyond the soil and affects water resources as well. The excess fluoride seeps into groundwater, making it unsafe for consumption. Long-term consumption of fluoride-contaminated water can lead to various health issues, including dental and skeletal fluorosis. These health concerns pose significant risks to the local population, especially those reliant on contaminated water sources for their daily needs. Addressing fluoride contamination requires concerted efforts from various stakeholders, including government authorities, researchers, and farmers. Implementing appropriate water treatment technologies, such as defluoridation units, can help reduce fluoride levels in drinking water sources. Additionally, promoting alternative irrigation methods and crop diversification strategies can aid in mitigating the impact of fluoride on agricultural productivity. Furthermore, creating awareness among farmers about the adverse effects of fluoride contamination and providing access to alternative water sources are crucial steps toward safeguarding the health of the community and sustaining agricultural activities in the region. Fluoride contamination poses significant challenges to crop production, soil health, and water resources in North 24 Parganas, West Bengal. It is imperative to prioritize efforts to address this issue effectively and implement appropriate measures to mitigate fluoride contamination. By adopting sustainable practices and promoting awareness, the community can work towards restoring the agricultural productivity, soil quality and ensuring access to safe drinking water in the region.

Keywords: fluoride contamination, drinking water, toxicity, soil health

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Authors: Salem Alghamdi

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Genotypes collected from farmers at a different region of Saudi Arabia as well as from Egyptian cultivar and a new line from Yamen. Seeds of these genotypes were grown in Dirab Agriculture Research Station, (Middle Region) and Al-Ahsa Palms and Dates Research Center (East region), during summer of 2015. Field experiments were laid out in randomized complete block design on the first week of June with three replications. Each experiment plot contained 6 rows 3m in length. Inter- and intra-row spacing was 60 and 25cm, respectively. Seed yield and its components were estimated in addition to qualitative characters on cowpea plants grown only in Dirab using cowpea descriptor from IPGRI, 1982. Seeds for chemical composite and antioxidant contents were analyzed. Highly significant differences were detected between genotypes in both locations and the combined of two locations for seed yield and its components. Mean data clearly show exceeded determine genotypes in seed yield while indeterminate genotypes had higher biological yield that divided cowpea genotypes to two main groups 1- forage genotypes (KSU-CO98, KSU-CO99, KSU-CO100, and KSU-CO104) that were taller and produce higher branches, biological yield and these are suitable to feed on haulm 2- food genotypes (KSU-CO101, KSU-CO102, and KSU-CO103) that produce higher seed yield with lower haulm and also these genotypes characters by high seed index and light seed color. Highly significant differences were recorded for locations in all studied characters except the number of branches, seed index, and biological yield, however, the interaction of genotype x location was significant only for plant height, the number of pods and seed yield per plant.

Keywords: Cowpea, genotypes, antioxidant contents, yield

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Authors: Kumar Kanudkuri, A. Sandhya

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Typically, in a MRI clinical setting, there are several protocols run, each indicated for a specific anatomy and disease condition. However, these protocols or parameters within them can change over time due to changes to the recommendations by the physician groups or updates in the software or by the availability of new technologies. Most of the time, the changes are performed by the MRI technologist to account for either time, coverage, physiological, or Specific Absorbtion Rate (SAR ) reasons. However, giving properly guidelines to MRI technologist is important so that they do not change the parameters that negatively impact the image quality. Typically a standard American College of Radiology (ACR) MRI phantom is used for Quality Control (QC) in order to guarantee that the primary objectives of MRI are met. The visual evaluation of quality depends on the operator/reviewer and might change amongst operators as well as for the same operator at various times. Therefore, overcoming these constraints is essential for a more impartial evaluation of quality. This makes quantitative estimation of image quality (IQ) metrics for MRI quality control is very important. So in order to solve this problem, we proposed that there is a need for a robust, open-source, and automated MRI image control tool. The Designed and developed an automatic analysis tool for measuring MRI image quality (IQ) metrics like Signal to Noise Ratio (SNR), Signal to Noise Ratio Uniformity (SNRU), Visual Information Fidelity (VIF), Feature Similarity (FSIM), Gray level co-occurrence matrix (GLCM), slice thickness accuracy, slice position accuracy, High contrast spatial resolution) provided good accuracy assessment. A standardized quality report has generated that incorporates metrics that impact diagnostic quality.

Keywords: ACR MRI phantom, MRI image quality metrics, SNRU, VIF, FSIM, GLCM, slice thickness accuracy, slice position accuracy

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Authors: My Linh Hoang, Van Dung Hoang

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Environmental, Social, and Governance (ESG) factors have become a focus for companies globally, as businesses are now focusing on long-term sustainable goals rather than only operating for the goals of profit maximization. According to recent research, in several countries, companies have shown positive results in their financial performance by improving their ESG performance. The construction industry is one of the most crucial components of social and economic development; as a result, considerations for ESG factors are becoming more and more essential for companies in this sector. In Vietnam, the construction industry has been growing rapidly in recent years; however, it has yet to be discussed and studied extensively in Vietnam how ESG factors create impacts on corporate financial performance in general and construction corporations’ financial performance in particular. This research aims to examine the relationship between ESG factors and financial indicators in construction companies from 2011 to 2021 through panel data analysis of 75 listed construction companies in Vietnam and to provide insights into how these companies can better integrate ESG considerations into their operations to enhance their financial performance. The data was analyzed through 3 main methods: descriptive statistics, correlation coefficient analysis applied to all dependent, explanatory and control variables, and panel data analysis method. In panel data analysis, the study uses the fixed effects model (FEM) and random effects model (REM). The Hausman test will be used to select which model is suitable to be used. The findings indicate that maintaining a strong commitment to ESG principles can have a positive impact on financial performance. Finally, FGLS estimation will be performed when the problem of autocorrelation and variable variance appears in the model. This is significant for all parties involved, including investors, company managers, decision-makers, and industry regulators.

Keywords: ESG, financial performance, construction company, Vietnam

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Authors: Ashirgul Kozhagulova, Ainash Shabdirova, Galym Tokazhanov, Minh Nguyen

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The synthetic rocks have been advantageous over the natural rocks in terms of availability and the consistent studying the impact of a particular parameter. The artificial rocks can be fabricated using variety of techniques such as mixing sand and Portland cement or gypsum, firing the mixture of sand and fine powder of borosilicate glass or by in-situ precipitation of calcite solution. In this study, sodium silicate solution has been used as the cementing agent for the quartz sand. The molded soft cylindrical sandstone samples are placed in the gas-tight pressure vessel, where the hardening of the material takes place as the chemical reaction between carbon dioxide and the silicate solution progresses. The vessel allows uniform disperse of carbon dioxide and control over the ambient gas pressure. Current paper shows how the bonding material is initially distributed in the intergranular space and the surface of the sand particles by the usage of Electron Microscopy and the Energy Dispersive Spectroscopy. During the study, the strength of the cement bond as a function of temperature is observed. The impact of cementing agent dosage on the micro and macro characteristics of the sandstone is investigated. The analysis of the cement bond at micro level helps to trace the changes to particles bonding damage after a potential yielding. Shearing behavior and compressional response have been examined resulting in the estimation of the shearing resistance and cohesion force of the sandstone. These are considered to be main input values to the mathematical prediction models of sand production from weak clastic oil reservoir formations.

Keywords: artificial sanstone, cement bond, microstructure, SEM, triaxial shearing

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Authors: Ashis Mallick, Rajeev Ranjan

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The closed form solution for thermal stresses in an annular fin with hyperbolic profile is derived using Adomian decomposition method (ADM). The conductive-convective fin with variable thermal conductivity is considered in the analysis. The nonlinear heat transfer equation is efficiently solved by ADM considering insulated convective boundary conditions at the tip of fin. The constant of integration in the solution is to be estimated using minimum decomposition error method. The solution of temperature field is represented in a polynomial form for convenience to use in thermo-elasticity equation. The non-dimensional thermal stress fields are obtained using the ADM solution of temperature field coupled with the thermo-elasticity solution. The influence of the various thermal parameters in temperature field and stress fields are presented. In order to show the accuracy of the ADM solution, the present results are compared with the results available in literature. The stress fields in fin with hyperbolic profile are compared with those of uniform thickness profile. Result shows that hyperbolic fin profile is better choice for enhancing heat transfer. Moreover, less thermal stresses are developed in hyperbolic profile as compared to rectangular profile. Next, Nelder-Mead based simplex search method is employed for the inverse estimation of unknown non-dimensional thermal parameters in a given stress fields. Owing to the correlated nature of the unknowns, the best combinations of the model parameters which are satisfying the predefined stress field are to be estimated. The stress fields calculated using the inverse parameters give a very good agreement with the stress fields obtained from the forward solution. The estimated parameters are suitable to use for efficient and cost effective fin designing.

Keywords: Adomian decomposition, inverse analysis, hyperbolic fin, variable thermal conductivity

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Authors: Omar Bouledroua, Djamel Zelmati, Zahreddine Hafsi, Milos B. Djukic

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Transporting hydrogen gas through the existing natural gas pipeline network offers an efficient solution for energy storage and conveyance. Hydrogen generated from excess renewable electricity can be conveyed through the API 5L steel-made pipelines that already exist. In recent years, there has been a growing demand for the transportation of hydrogen through existing gas pipelines. Therefore, numerical and experimental tests are required to verify and ensure the mechanical integrity of the API 5L steel pipelines that will be used for pressurized hydrogen transportation. Internal pressure loading is likely to accelerate hydrogen diffusion through the internal pipe wall and consequently accentuate the hydrogen embrittlement of steel pipelines. Furthermore, pre-cracked pipelines are susceptible to quick failure, mainly under a time-dependent cyclic pressure loading that drives fatigue crack propagation. Meanwhile, after several loading cycles, the initial cracks will propagate to a critical size. At this point, the remaining service life of the pipeline can be estimated, and inspection intervals can be determined. This paper focuses on the hydrogen embrittlement of API 5L steel-made pipeline under cyclic pressure loading. Pressurized hydrogen gas is transported through a network of pipelines where demands at consumption nodes vary periodically. The resulting pressure profile over time is considered a cyclic loading on the internal wall of a pre-cracked pipeline made of API 5L steel-grade material. Numerical modeling has allowed the prediction of fatigue crack evolution and estimation of the remaining service life of the pipeline. The developed methodology in this paper is based on the ASME B31.12 standard, which outlines the guidelines for hydrogen pipelines.

Keywords: hydrogen embrittlement, pipelines, transient flow, cyclic pressure, fatigue crack growth

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Authors: Rahele Mesbah, Nic Van Der Wee, Manja Koenders, Erik Giltay, Albert Van Hemert, Max De Leeuw

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Introduction: Patients with bipolar disorder (BD), characterized by depressive and manic episodes, often suffer from cognitive dysfunction. An up-to-date meta-analysis of functional Magnetic Resonance Imaging (fMRI) studies examining cognitive function in BD is lacking. Objective: The aim of the current fMRI meta-analysis is to investigate brain functioning of bipolar patients compared with healthy subjects within three domains of emotion processing, reward processing, and working memory. Method: Differences in brain regions activation were tested within whole-brain analysis using the activation likelihood estimation (ALE) method. Separate analyses were performed for each cognitive domain. Results: A total of 50 fMRI studies were included: 20 studies used an emotion processing (316 BD and 369 HC) task, 9 studies a reward processing task (215 BD and 213 HC), and 21 studies used a working memory task (503 BD and 445 HC). During emotion processing, BD patients hyperactivated parts of the left amygdala and hippocampus as compared to HC’s, but showed hypoactivation in the inferior frontal gyrus (IFG). Regarding reward processing, BD patients showed hyperactivation in part of the orbitofrontal cortex (OFC). During working memory, BD patients showed increased activity in the prefrontal cortex (PFC) and anterior cingulate cortex (ACC). Conclusions: This meta-analysis revealed evidence for activity disturbances in several brain areas involved in the cognitive functioning of BD patients. Furthermore, most of the found regions are part of the so-called fronto-limbic network which is hypothesized to be affected as a result of BD candidate genes' expression.

Keywords: cognitive functioning, fMRI analysis, bipolar disorder, fronto-limbic network

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Authors: P. S. Dhananjaya Swamy, B. Chinnappa, G. B. Ramesh, Naveen P. Kumar

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Kodagu district is one of the most densely forested districts in the India as around sixty five per cent of geographical areas under tree cover. Nearly 53 per cent of the flora of Kodagu is endemic. The district is also a hotspot of endemic orchids found mainly in the Thadiandamol. Shade grown, eco-friendly coffee farms are perhaps a selected few places on this planet where nature runs wild. The Kodagu accounts for more than 8.8 per cent of floral diversity of Karnataka state. Estimation of unit cost of cultivation plays a vital role in determining the governmental program their market intervention policies. On an average, planters incurred around Rs. 17041 per acre. The extent of production risk was highest among small category of planters (66 %) compared to other two exhibiting production instability. The result shows that, the coffee productivity in medium plantations was 1051.2 kg per acre as against 758.5 and 789.2 kg in the case of small and large plantations. An annual net return per acre was highest in the case of medium planters (Rs. 26109.3) as against Rs. 20566.7 and Rs. 18572.7 in the case of small and large planters. Cost of production was lowest in the case of small planters (Rs. 18.9 per kg of output) followed by medium planters (Rs. 21.2 per kg of output) and large planters (Rs. 22.5 per kg of output). The productivity of coffee is less whenever it is grown under high shade and native tree cover; it is around 6 quintals per acre when compared with low shade conditions, which is around 8.9 quintals per acre, without a significant difference in the amount invested for growing coffee. Net gain was lower by Rs. 15.5 per kg for the planters growing under high shade and native trees cover when compared with low shade and exotic trees cover.

Keywords: coffee, cultivation, economics, Kodagu

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Authors: Kechnit Djamel, Ammarri Abdelhadi, Raphael Tshimang, Mark Trrig

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One of the most important aspects of monitoring rivers is navigation. The variation of discharge in the river generally produces a change in available draft for a vessel, particularly in the low flow season, which can impact the navigable water path, especially when the water depth is less than the normal one, which allows safe navigation for boats. The water depth is related to the bathymetry of the channel as well as the discharge. For a seasonal update of the navigation maps, a daily discharge value is required. Many novel approaches based on earth observation and remote sensing have been investigated for large rivers. However, it should be noted that most of these approaches are not currently able to directly estimate river discharge. This paper discusses the application of remote sensing tools using the analysis of the reflectance value of MODIS imagery and is combined with field measurements for the estimation of discharge. This approach is applied in the lower reach of the Congo River (Pool Malebo) for the period between 2019 and 2021. The correlation obtained between the observed discharge observed in the gauging station and the reflectance ratio time series is 0.81. In this context, a Discharge Reflectance Model (DRM) was developed to express discharge as a function of reflectance. This model introduces a non-contact method that allows discharge monitoring using earth observation. DRM was validated by field measurements using ADCP, in different sections on the Pool Malebo, over two different periods (dry and wet seasons), as well as by the observed discharge in the gauging station. The observed error between the estimated and measured discharge values ranges from 1 to 8% for the ADCP and from (1% to 11%) for the gauging station. The study of the uncertainties will give us the possibility to judge the robustness of the DRM.

Keywords: discharge monitoring, navigation, MODIS, empiric, ADCP, Congo River

Procedia PDF Downloads 56