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- Modeling of PV Water Pumping Performance using Multi-Parallel Pump
Switching for an Optimal Hydraulic Power Point Tracking-
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Abstract: Abstract Performance of photovoltaic water pumping systems (PVWPS) directly coupled to pressured irrigation systems is mainly affected by irradiance fluctuations during days and seasons. Considerable efforts have been done to improve the photovoltaic outputs using maximum power point tracking (MPPT) concept. However, the improvement on the hydraulic side of PVWPS kept limited. For optimizing the hydraulic performance, a new concept of hydraulic power point tracking (HPPT) was, previously implemented and tested for yielding energy at the hydraulic network using parallel switching of irrigated sectors. Otherwise, the hydraulic performance can be yielded using similar concept of switching parallel pumps. The present research work aims for developing the HPPT approach based on multi-parallel pumps switching to enhance the performance of PVWPS. After that, the dataset taken from this HPPT approach was used for modeling the performance trends using the Python™ and R interfaces in order to evaluate and predict the system's optimal hydraulic performance. Results showed that use of the HPPT concept by switching multi-parallel pumps can effectively improves PVWPS performance. It can potentially increase the daily hydraulic energy by up to 50% during low irradiance periods and up to 20% during the day. The modeling of the growth performance is used to predict the daily optimal performances. The predictions were compared with the experimentally results to show that the modeling of the HPPT concept is promising for use as decisional tool to manage hydraulic performance (RMSE < 08.37% and MAE < 06.93%). PubDate: 2024-08-27
- Numerical modeling of flow dynamics around L-shaped and T-shaped dikes
with varying geometric configurations and wing arrangements-
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Abstract: Abstract Impermeable dikes are crucial for riverbank protection and significantly affect flow dynamics, including velocity and turbulence characteristics. The spacing between the dikes, their shape, submergence level, overall geometry, and permeability all have a profound impact on river morphology. This study investigated the effects of dike shape and geometry on flow velocity and turbulence using a numerical approach. FLUENT (ANSYS), a CFD software program, was applied to simulate the steady flow around an impermeable dike. Sensitivity analyses were conducted to determine the appropriate turbulence model and mesh resolution. Based on these analyses, the rigid lid assumption (RLA) method with the \(k-\varepsilon\) turbulence model was selected to capture the flow characteristics. The accuracy of the model was confirmed through a physical experiment conducted in a rectangular open channel. The findings indicated that the numerical model accurately replicated flow dynamics in both the mainstream and the dike field. Notably, the highest mean velocity and turbulence were observed around the impermeable dikes (I-shaped, L-shaped, T-shaped). The position of the inflection points varied based on the shape of the impermeable dike and its wing type. The T-shaped dike with a full-length cylindrical wing was the most effective, reducing flow velocity by 75% and turbulent kinetic energy by 26%. These results have practical implications for flood management strategies, enhancing public safety, environmental protection, and economic efficiency. By providing a deeper understanding of optimal dike configurations, this research supports informed decision-making in river management. PubDate: 2024-08-27
- Modeling the impact of climate change on wheat yield in Morocco based on
stacked ensemble learning-
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Abstract: Abstract Climate change increases the frequency and intensity of extreme events such as droughts, heat waves, and floods, posing a significant challenge to Morocco’s agriculture and food security. Understanding the future impact of climate on crop yield is crucial for long-term agricultural planning. However, this area has been underexplored due to various challenges, including data constraints. This study aimed to project wheat yield in Morocco at a provincial scale from 2021 to 2040 by using multiple climate model datasets, and advanced Machine Learning (ML) algorithms. An ensemble of five global climate models (MIROC6, CanESM5, IPSL-CM6A-LR, INM-CM5-0, NESM3) was employed to project changes in temperature (Tmax, Tmin) and precipitation (Pr). The climate projections were bias corrected using quantile-quantile approach. Four advanced ML algorithms: Random Forest, XGBoost, LightGBM, and Gradient Boosting Regressor, were utilized to develop a stacked ensemble learning model for wheat yield prediction at provincial scale in Morocco. The stacked ensemble learning model was calibrated and validated using historical wheat yield data. Results show that the stacked ensemble learning approach significantly reduced prediction errors compared to individual models, achieving high coefficient of determination of 0.82 and low root mean square error (RMSE) of 300.51 kg/ha. Wheat yields are projected to decline by an average of 10% by 2040 under the modest shared socioeconomic pathways (SSP2-4.5) scenario while under high emission scenario (SSP5-8.5), yield could decrease by up to 60% across some provinces such as Essaouira, Youssoufia, Ouezzane, Rehamna, and Sidi Kacem. Temperature (Tmax and Tmin) and precipitation (Pr) were identified as the critical climate variables influencing wheat yield, with Tmax being the most impactful. Regional projections revealed that provinces inland and in southern Morocco may experience a significant yield reduction of up to 60%. This study highlights the need for implementing effective climate change mitigation measures to avert food insecurity in Morocco and other northern African countries. The primary findings indicate that climate variables, particularly Tmax, play a crucial role in wheat yield projections, emphasizing the importance of detailed climate data and advanced modeling techniques in agricultural planning. PubDate: 2024-08-24
- Modeling the vulnerability of water resources to pollution in a typical
mining area, SE Nigeria using speciation, geospatial, and multi-path human health risk modeling approaches-
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Abstract: Abstract Mining activities in developing countries, particularly Nigeria, have become a significant public health concern due to contamination of water resources. This study integrates geochemical, spatiotemporal, and probabilistic health risk assessment models to evaluate pollution vulnerability in surface and groundwater and its public health implications. Twenty-one water samples were analyzed for their physicochemical properties. Both the groundwater and surface water were moderately acidic (pH 5.8–6.1), fresh, and hard. Potentially toxic elements (PTEs) concentration occurred as Mn > Pb > Fe > Cd > Zn > Ba and Mn > Pb > Cd > Zn > Fe > Ba for the surface and groundwater respectively. All the PTEs (except Fe, Ba, and Zn) exceeded their permissible limits. The water quality index (WQI) and the overall index of pollution (OIP) revealed that 52.3% and 66.6% of the water samples, respectively, are unsuitable for drinking. The trend of distribution of PTEs in the water increases towards the southwestern direction. The PHREEQC model revealed that some heavy metals (e.g., Cd, Ba, Pb) occurred in their hydrated mineral phases at the current water pH range of 5.82–6.03, this reduces their bioavailability in water. Hierarchical cluster analysis identified NO3, pH, NO2, Cd, BOD, and Salinity in five water samples (STRM6, PND3, PND6, BH3, BH4) as the major contributors to the water contamination while Mn, Fe, and Zn were identified to have the least contribution to contamination. Meanwhile, principal component analysis showed very high principal component loading (p > 0.9) for Temperature and pH in groundwater; indicating that these parameters had more influence on the enrichment of contaminants in groundwater than in surface water. The total cancer risk (CRtot) for oral ingestion of water occurred above the U.S Environmental Protection allowable limits of 1.0E−6 and 1.0E−04. The children population showed greater exposure risks to cancer compared to adults from the ingestion of Pb and Cd in drinking water. The findings from this study underscores the significance of an integrated approach for the effective prediction of water pollution vulnerability and human health risks, particularly in Nigeria, where data on environmental monitoring is a concern. PubDate: 2024-08-23
- A fractional calculus approach to smoking dynamics with bifurcation
analysis-
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Abstract: Abstract Smoking cigarettes is one of the world’s leading causes of death, with harmful chemicals affecting both smokers and those exposed to secondhand and thirdhand smoke. This study employs a Caputo–Fabrizio fractional derivative within a mathematical model to investigate the dynamics of smoking behavior. The analysis confirms the existence and uniqueness of solutions and examines critical properties such as positivity, boundedness, and the invariant region to ensure the model’s robustness. The reproduction number is calculated using the next-generation matrix method to estimate the potential for smoking transmission, parameterized by \(({\rho }_{3},\vartheta )\) . Comprehensive sensitivity and stability analyses are performed, including an investigation of the local and global stabilities of both smoking-free and smoking-present equilibrium points. Bifurcation analysis is conducted with the bifurcation parameter \({\epsilon }_{1}^{\star }=1.523\) , highlighting the transitions between unstable and stable states. We also apply the system Lagrange interpolation scheme, which yields results validating our theoretical findings. Numerical simulations, executed using Python, further corroborate these results and provide deeper insights into the complex dynamics of smoking behavior. Additionally, the study discusses the implications of these findings for public health strategies and intervention planning. PubDate: 2024-08-23
- Application of Advanced Machine Learning Models for Uplift and Penetration
Resistance in Clay-Embedded Dual Interfering Pipelines-
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Abstract: Abstract This study investigated the uplift and penetration resistance of dual interfering pipelines buried in clay using advanced regression machine learning models, specifically the group method of data handling (GMDH), genetic programming (GP), extreme gradient boosting (XGBoost), and random forest (RF). The dataset comprises 256 numerical FELA data points for uplift conditions and 384 numerical FELA data points for penetration conditions, marking the first application of these models in this context. To train the models, three input parameters are considered: the spacing ratio (S/D), the embedded ratio (w/D), and the normalized unit weight and increasing strength (γ/ρ). The models predict two output parameters: the vertical uplift resistance ( \(\frac{{q}_{t}}{\rho D}\) ) and the vertical penetration resistance \((\frac{{q}_{c}}{\rho D}\) ). Performance metrics were employed to evaluate and compare the effectiveness of each model. The study revealed that the GP model is particularly effective in predicting the uplift and penetration resistance of pipelines. Both external validation and literature validation confirmed the predictive capabilities of the proposed models. Furthermore, the influence of each input parameter was analyzed, resulting in the development of empirical equations for both uplift and penetration conditions. The resulting empirical equations provide dimensionless output parameters, offering practical utility for design practitioners in real-world field conditions. Detailed study results, including comprehensive tables and empirical equations, are presented to facilitate practical applications and enhance the understanding of pipeline-soil interactions in clay environments. These contributions underscore the potential of advanced regression machine learning models in geotechnical engineering and pipeline design. PubDate: 2024-08-22
- An improved digital soil mapping approach to predict total N by combining
machine learning algorithms and open environmental data-
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Abstract: Abstract Digital Soil Mapping (DSM) is fundamental for soil monitoring, as it is limited and strategic for human activities. The availability of high temporal and spatial resolution data and robust algorithms is essential to map and predict soil properties and characteristics with adequate accuracy, especially at a time when the scientific community, legislators and land managers are increasingly interested in the protection and rational management of soil. Proximity and remote sensing, efficient data sampling and open public environmental data allow the use of innovative tools to create spatial databases and digital soil maps with high spatial and temporal accuracy. Applying machine learning (ML) to soil data prediction can improve the accuracy of maps, especially at scales where geostatistics may be inefficient. The aim of this research was to map the nitrogen (N) levels in the soils of the Nurra sub-region (north-western Sardinia, Italy), testing the performance of the Ranger, Random Forest Regression (RFR) and Support Vector Regression (SVR) models, using only open source and open access data. According to the literature, the models include soil chemical-physical characteristics, environmental and topographic parameters as independent variables. Our results showed that predictive models are reliable tools for mapping N in soils, with an accuracy in line with the literature. The average accuracy of the models is high (R2 = 0.76) and the highest accuracy in predicting N content in surface horizons was obtained with RFR (R2 = 0.79; RMSE = 0.32; MAE = 0.18). Among the predictors, SOM has the highest importance. Our results show that predictive models are reliable tools in mapping N in soils, with an accuracy in line with the literature. The results obtained could encourage the integration of this type of approach in the policy and decision-making process carried out at regional scale for land management. PubDate: 2024-08-20
- Enhanced analysis of landslide susceptibility mapping in the proximity of
main roads in the province of Skikda, Algeria: using NAS for efficient performance and faster processing-
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Abstract: Abstract Landslides are complex geological phenomena influenced by various factors such as heavy rainfall, topography, geology, and anthropogenic activities. They can have devastating consequences, leading to prolonged road closures, costly detours, and, in severe cases, loss of human lives. For optimal management and planning of economic projects and construction, accurate and effective mapping of this hazard is essential. Deep learning has been successfully applied to landslide mapping, although this approach can occasionally face precision issues and requires significant time to determine the most suitable model for each data type. The originality of this research resides in the effectiveness and performance of the use of the neuronal architecture research (NAS) technique to optimize landslide susceptibility mapping. NAS proved to be the appropriate solution to these challenges through its ability to automate the architecture of deep learning models by optimizing its principal hyperparameters: the number of hidden layers (NCC), the number of neurons per hidden layer (NNC) and the number of training epochs (NEP). The Simulated Annealing meta-heuristic was employed to optimize the search space, enabling efficient exploration of various combinations in order to identify the best performing configuration for landslide modelling along the main roads in the province of Skikda, covering a 500-m-wide zone. To create a solid database, nine causal factors were selected by analyzing their relationship with landslide occurrence. These factors include slope, aspect, lithology, the Normalized Difference Vegetation Index (NDVI), soil type, elevation, as well as proximity to roads, watercourses, and geological faults. This selection was carried out using linear and non-linear statistical tests, based on Pearson's correlation coefficient and Spearman's ranks. The use of this technique shows an accuracy, recall, and F1-score of 0.9940, a precision of 0.9941, and an RMSE of 0.0772, which proves a better modeling of this phenomenon. The map produced by this approach indicates a good adequacy of the distribution of risk areas with the variability of susceptibility factors in the study area and the results obtained. The map displays different susceptibility classes: 22.1% of the area is categorized as very high susceptibility, 5.63% as moderate susceptibility, and 59.51% as very low susceptibility. Additionally, there are intermediate classes with 6.70% falling between very low and moderate susceptibility, and 6.06% between moderate and high susceptibility. The susceptibility map generated by this model, using the NAS method and the ArcGIS tool, provides vital information for identifying areas at high risk of landslides, which contribute to a better understanding of natural hazards and in the determining of the safety of residents and the preservation of road infrastructure in the Skikda province. PubDate: 2024-08-20
- Assessment of monthly hydroclimatic patterns and rainfall-runoff modeling
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Abstract: Abstract Climate change and consecutive droughts have exacerbated the water resource crisis in many regions, especially in semi-arid Mediterranean areas like Morocco. This study aims to conduct a monthly spatial assessment of the hydroclimatic regime and compare the performance of two different monthly models for runoff forecasting at the hydrological stations of the Upper Inaouene watershed. The models evaluated were the Hydrological Model of Rural Engineering with two monthly parameters (GR2M) and the Artificial Neural Network (ANN) model. Hydrometeorological data were obtained using the Thiessen and averaging methods. The models’ effectiveness was assessed using the Nash–Sutcliffe criterion (NS), mean absolute error (MAE), root mean square error (RMSE), correlation coefficient (r), and coefficient of determination (R2). The results indicate that the basin’s climate is Mediterranean semi-arid, characterized by significant spatial and temporal variability and two distinct annual periods. The runoff deficit is influenced by thermal factors, with evapotranspiration systematically exceeding runoff. Flow variability is attributed to infiltration from karstic aquifers and contributions from domestic sewage. The GR2M model demonstrated efficiencies of 85.45% for the correlation coefficient (r), 71.31% for the coefficient of determination (R2), and 71.08% for the Nash–Sutcliffe criterion (NS). In comparison, the ANN model achieved 89.4% for r and 80% for both R2 and NS, outperforming GR2M in accuracy. These models, particularly the ANN, provide essential hydrological data for water resource management, addressing the lack of flow rate data. PubDate: 2024-08-19
- Analytical study of a modified monkeypox virus model using
Caputo–Fabrizio fractional derivatives-
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Abstract: Abstract Monkeypox virus causes a zoonotic disease known as monkeypox, which poses a significant public health risk. To address this, we developed a novel mathematical model incorporating a hospital class functioning as a control measure. Our comprehensive non-linear compartmental model includes nine distinct compartments for both human and rodent populations, delineating Susceptible, Exposed, Infected, Isolated, Quarantined, Hospitalized, and Recovered humans, as well as Susceptible, Exposed, and Infected rodents. The Caputo–Fabrizio fractional derivative was employed to analyze the model. All interactions potentially leading to disease transmission within the population are accounted for. We examined the stability of the model in a disease-free state, demonstrating that the model is stable when \({R}_{0}<1\) and unstable otherwise. Multiple simulations were conducted with various input values to explore the complex dynamics of monkeypox infection under different conditions. Our study investigates the system’s dynamic behavior to develop effective disease management strategies. The dynamic behavior of the system is illustrated through numerical simulations with various input parameters, providing a critical framework for evaluating monkeypox control measures. The findings of this study are expected to contribute significantly to the understanding and management of monkeypox outbreaks. PubDate: 2024-08-18
- Modeling seasonal typhoon genesis in the North West Pacific using
probabilistic approaches-
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Abstract: Abstract Typhoons are one of the extreme weather phenomena that seriously affect humans and the environment. Modeling typhoon genesis plays a crucial role in evaluating typhoon features, providing valuable information for the early warning systems related to storm hazards. The goal of this work is to establish the probabilistic models for typhoon genesis (TG) across the North West Pacific (NWP) regarding seasonal variations. We examined the congruence between the parametric probability distributions and recorded typhoon using Chi-square and Anderson-Darling tests. In addition, three criteria, consisting of Correlation Coefficient (CC), Mean Absolute Error (MAE), Root Mean Squared Error (RMSE), were used to determine which probability distributions would provide the optimal fit to recorded typhoon dataset. Our findings demonstrated that the majority of typhoons generated during the peak season (PS) spanning between June and November, along the latitudinal belt 5°N − 30°N. Conversely, the low typhoon season (LS), with a lower frequency of typhoons primarily found in the area south of 15°N, was from December to May. The Poisson distribution was the best model to simulate typhoon counts in the two seasons (PS and LS). For modeling TG position in the LS, Loglogistic distribution was chosen to simulate longitude and latitude. For the PS, Gamma distribution was the optimal selection to simulate TG longitude while Nakagami distribution was used to model TG latitude. The geographic patterns of simulated TG in the two seasons exhibited comparability to those of the recorded TG by 1000-year Monte Carlo simulations. This study enhances the understanding of seasonal TG trends and provides a robust framework for future typhoon activity modeling. PubDate: 2024-08-17
- Modelling transmission dynamics of measles: the effect of treatment
failure in complicated cases-
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Abstract: Abstract Measles has emerged as one of the leading causes of child mortality globally, leading to an estimated 142,300 fatalities annually, despite the existence of a reliable and safe vaccine. Moreover, a surge in global measles cases has occurred in recent years, predominantly among children below 5 years old and immunocompromised adults. The escalating incidence of measles can be attributed to the continual decline in vaccination coverage. This phenomenon has attracted considerable attention from both the public and scientific communities. In this work, we develop and analyze a fractional-order model for measles epidemic by incorporating vaccination as control strategy and investigating the effect of treatment failure in complicated cases. The model is analyzed qualitatively and quantitatively to gain robust understanding into control measures required to curb this menace. Stability analysis around the neighbourhood of measles-free steady state is carried out to determine properties of the important threshold called reproduction number, which is necessary to quantitatively analyze the formulated model. Sensitivity analyses of this threshold and the state solutions using the Latin hypercube sampling (LHS) and contour/surface plots reveal the dominance of effective contact rate, progression and transition rates in influencing the general dynamics of measles epidemic. Furthermore, the fractional non-standard discretization scheme using a well defined denominator function is used to numerically solve the designed model. Scenario analyses to assess the impact of vaccination and treatment failure show that an effective and safe vaccination programme could significantly reduce the spread of measles while uncontrolled treatment failure could adversely increase the burden of measles within a population. PubDate: 2024-08-16
- A convolutional neural network model for accurate short-term leaf area
index prediction-
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Abstract: Abstract The leaf area index (LAI) is a crucial biophysical parameter that significantly influences carbon, water, and energy cycles within terrestrial ecosystems. While short-term LAI prediction has been extensively studied, most research has primarily focused on specific ecosystem types. This comprehensive study evaluates the performance of the convolutional neural network (CNN) model across eleven diverse land cover types within global terrestrial ecosystems. Our results reveal the promising predictive capabilities of the CNN model, achieving an overall R² of 0.845 and RMSE of 0.301, outperforming all the other baseline models. Notably, seasonal analysis demonstrates higher prediction accuracy (lower SMAPE) during summer than winter for most studied land cover types. We further identify radiation as a key environmental factor influencing LAI prediction accuracy across various land cover types. Overall, this research contributes to advancements in short-term LAI prediction, highlighting the efficacy of the tested deep learning models in time-series ecological modeling. These findings have broad implications for climate change modeling, resource management, and agricultural planning. PubDate: 2024-08-12
- Exploring vortex dynamic efficiency in hydro-suction system: a combined
experimental and numerical investigation-
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Abstract: Abstract Vortex utilization in hydro-suction sediment removal system can have a notable impact on the ratio of suspended particles in water, leading to an improvement in overall system efficiency. To induce a vortex, water is injected through fan blades to creat helical motion. In order to fully comprehend the functioning of the Vortex utilization in hydro-suction device (VUHS), a sequence of practical experiments using particle image velocimetry (PIV) is conducted. This method analyzes particle motion to obtain velocity distribution in the system. Additionally, a numerical model by computational fluid dynamics (CFD) is created to simulation VUHS. The comparison between PIV and CFD tests was carried out in the laboratory in three cases: I) suction flow at the intake nozzle; II) injection flow through fan blades to create a vortex; and III) a combination of suction and injection flows. The validation of the numerical simulation (CFD) with the experimental test (PIV) cases shows a good agreement between them. The numerical simulation study provides a detailed understanding of the flow phenomena of the fan blade hydro-suction device, including the distribution of velocity and pressure. Furthermore, the crucial parameters, i.e., the injection flow rate, suction flow rate, and depth of the vortex cylindrical Zv, are studied. Then, the relationships between different pressures and velocity vectors with suction and injection flow rate are clarified. The results show that the increasing rotation of water by injection creates a negative pressure in the center of the vortex, leading to an increase in velocity (Uy, Uvo). Additionally, increasing the suction flow rate has a positive effect on the values of velocity if the ratio λ ≤ 0.7 and a negative effect if λ > 1.2. Finally, the optimum ration between suction and injection to achieve high performance from VUHS is 0.7. PubDate: 2024-08-11
- Downscaling future precipitation with shared socioeconomic pathway (SSP)
scenarios using machine learning models in the North-Western Himalayan region-
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Abstract: Abstract The Himalayan region is characterized by its heterogeneous topography and diverse land use/land cover types that significantly influence the weather and climatic patterns in the Indian sub-continent. Predicting future precipitation is crucial for understanding and mitigating the impacts of climate change on water resources, land degradation including soil erosion by water as well as sustainability of the natural resources. The study aimed to downscale future precipitation with Shared Socioeconomic Pathway (SSP) scenarios using machine learning methods in the Tehri Dam catchment area, located in the North-Western Himalayas, India. The study compared the performance of multiple linear regression (MLR), artificial neural network (ANN), and random forest (RF) models for statistical downscaling. During the training and testing phases, RF and ANN demonstrated reasonably satisfactory results in comparison to MLR. In general, models performed best on a monthly time scale compared to daily and yearly scales where RF model performed quite well. Therefore, the RF model was used to generate future climate scenarios for the near (2015–2040), mid (2041–2070), and far (2071–2100) future periods under the shared socioeconomic pathway (SSP) scenarios. An increasing trend in precipitation was observed across the area (grids), with varying magnitudes. The SSP1-2.6 scenario was projected the least change, ranging from 1.4 to 3.3%, while the SSP2-4.5 scenario indicated an average increase of 3.7 to 14.0%. The highest emission scenario (SSP5-8.5) predicted an increase of 8.4 to 27.5% in precipitation during the twenty-first century. In general, the increase in precipitation was higher in the far future compared to the mid and near future period. This projected increase in the precipitation may have the serious implications on food security, hydrological behaviour, land degradation, and accelerated sedimentation in the Himalayan region. PubDate: 2024-08-09
- Fractional order modeling of parasite-produced marine diseases with memory
effect-
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Abstract: Abstract Marine parasites are strongly linked to marine diseases; they negatively impact fish farming by reducing production and hindering sustainable industry development. However, there is less research on infectious diseases in marine environments compared to terrestrial systems. In this paper, we investigate the mathematical modeling and dynamics of marine diseases caused by parasites, incorporating memory effects. We propose a fractional-order Susceptible-Infectious-Parasite (SIP) model featuring a non-linear general incidence rate. The computation of the basic reproduction number is provided, along with discussions on positivity, boundedness, and the existence and uniqueness of solution. We also explore the local stability of equilibrium states and conduct a sensitivity analysis of parameters to identify those most significant in the model. Our findings show that when the basic reproduction number is less than 1, the disease-free equilibrium is locally asymptotically stable, and when it exceeds 1, the endemic equilibrium point is asymptotically stable. Key parameters influencing disease transmission by marine parasites include the rates of parasite production by infected hosts, and transmission from susceptible to infectious individuals. Numerical simulations validate theoretical results, demonstrating that incorporating memory effects provides more realistic disease dynamics, influencing the time required to reach stable states. We find that the number of marine parasites peaks in correlation with the formula of the incidence rate. PubDate: 2024-08-09
- A piecewise nonlinear fractional-order analysis of tumor dynamics:
estrogen effects and sensitivity-
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Abstract: Abstract This study aims to develop a mathematical model to identify key factors influencing the anti-tumor response. We have been proposed a nonlinear fractional-order tumor dynamics model (LPIHE) using a novel piecewise approach. This model incorporates the effects of estrogen, providing a comprehensive understanding of tumor behavior and offering insights into using estrogen to control tumor growth. To validate the model, we establish the existence and uniqueness of solutions for the piecewise derivative system under Arzelà-Ascoli and Schauder conditions. To assess biological feasibility, we have been calculated the reproductive number \(R_0\) and conduct a sensitivity analysis. Key parameters \(\lambda _2, \alpha _2, \beta _3, \gamma _1\) are systematically varied to analyze their impact on \(R_0\) , providing insights into the model’s robustness and vulnerability. Newton’s polynomial approach is used to obtain numerical solutions with real data across various fractional orders. This model have been investigated the effects of classical and modified fractional calculus operators, with a particular focus on the classical Caputo piecewise operator. The interval \([0, m_2]\) , where \(m_2 \in \mathbb {R}\) , is divided into two subintervals: \([0, m_1]\) and \([m_1, m_2]\) . The classical derivative is applied within \([0, m_1]\) , while the modified operator is used in \([m_1, m_2]\) . Results indicate that higher estrogen levels reduce tumor growth rates, underscoring the importance of fractional operators in modeling tumor dynamics. PubDate: 2024-08-09
- Predictive modeling the effect of Local Climate Zones (LCZ) on the urban
meteorology in a tropical andean area-
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Abstract: Abstract The Weather Research & Forecasting Model (WRF, Version 4.4) was applied to simulate meteorological conditions in the city of Quito, Ecuador, located in a tropical Andean landscape. These simulations included the urban canopy into WRF, using the Building Environment Parameterization (BEP) scheme combined with Local Climate Zones (LCZ) land use classification; the innermost domain had a horizontal resolution of 2 km. The simulation results showed that using LCZ + BEP options improved the representation of wind speed and planetary boundary layer height (PBLH), in comparison with WRF counter fact simulations which did not use BEP. For temperature and relative humidity, implementation of LCZ did not improve WRF simulations with respect to those counter fact simulations. This may be ascribed to the use of the default LCZ thermophysical parameters, suggesting the need for gathering local built environment features. The best WRF configuration found for wind speed was the one that combined BEP scheme, LCZ land use and the Yonsei University (YSU) PBL model with topographic option activated; this happened for dry and wet seasons and for the unique meteorological conditions in December. Regarding PBLH modeling, the best configurations were YSU-BEP-LCZ (December), MYJ-BEP-LCZ (April, wet season) and YSU (August, dry season). The findings showed the major influence of urban canopy — described by LCZ — on wind circulation and PBLH simulated within the city at high horizontal resolution (2 km). This effect should be considered when modeling atmospheric pollutant dispersion, choosing urban development strategies, and analyzing prospective climate change scenarios, among other goals. PubDate: 2024-08-08
- Assessing groundwater suitability and nitrate health risk in Edea,
Cameroon: implications for drinking and irrigation purposes-
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Abstract: Abstract Groundwater is the main source of freshwater for drinking and irrigation in Edea. However, growing demands from agriculture, population growth and urbanization have adversely affected groundwater quality in the area. Hence, the objectives of this study were to evaluate the suitability of groundwater for drinking and irrigation uses and quantify the human health risks associated with consuming nitrate-contaminated water in the study area. Groundwater samples were collected and analyzed to evaluate water quality based on thirteen hydrochemical parameters: pH, electrical conductivity (EC), total dissolved solids (TDS), total hardness (TH), calcium (Ca2+), magnesium (Mg2+), sodium (Na+), potassium (K+), bicarbonate (HCO3−), chloride (Cl−), nitrate (NO3−), sulfate (SO42−), and phosphate (PO43−). The study revealed that anion abundances followed the order of HCO3− > NO3− > Cl− > SO42− > PO43− while cation abundances were Ca2+ > Mg2+ > Na+ > K+. According to WHO standards, the pH, HCO3−, PO43− and NO3− levels exceeded their prescribed limits. The main source of nitrate was identified as fertilizer usage in agricultural fields. The groundwater was predominantly characterized as Ca–Mg–HCO3 and Ca–Mg–Cl–SO4 types, which resulted primarily from water–rock interactions. Water Quality Index (WQI) analysis found that the groundwater was generally good for drinking. Regarding irrigation suitability, the groundwater in the region generally met the criteria for %Na, SAR, RSC, KI, PS and PI, making it suitable for irrigation. However, 43.48% of the samples were deemed unsuitable based on the magnesium hazard. Furthermore, the study observed that the corrosivity ratios in 39.13% of samples were high enough to potentially cause corrosion when transporting water through metal pipes. The health risk assessment revealed that children in the study area faced a higher risk compared to teenagers and adults. The study proposed management measures to protect groundwater resources in the area. PubDate: 2024-08-07
- Wintertime source apportionment of PM2.5 pollution in million plus
population cities of India using WRF-Chem simulation-
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Abstract: Abstract Several major Indian cities experience elevated PM2.5 concentrations, particularly during the winter season. Effective air quality management in these densely populated urban areas necessitates a comprehensive understanding of the diverse emission sources contributing to air pollution. This study investigates PM2.5 pollution in 53 million-plus population cities (MPPC’s) across India during the winter of 2015–2016 utilizing the Weather Research and Forecasting model coupled with chemistry (WRF-Chem). Multiple model simulations were employed to study the impact of various source sectors on local PM2.5 pollution and their emissions in these cities. The findings indicate significant contributions to local PM2.5 pollution from major emission source sectors in MPPCs. The influence of PM2.5 pollution plumes originating from these cities on regional PM2.5 pollution in India is evident across all sectors. In MPPCs situated in the east, north, and central regions of India, the primary contributors to local PM2.5 pollution include residential and transportation sectors, alongside energy sectors in specific cities marked by elevated emissions from power plants. In the MPPCs of western India, the industrial and energy sectors are identified as the primary contributors to local PM2.5 pollution. Meanwhile, in the MPPCs of south India, the major contributors are identified as industrial and residential sectors. In a comprehensive overview encompassing 53 MPPCs, the primary contributors to local PM2.5 pollution are identified as follows: the energy sector in 7 cities, the industrial sector in 8 cities, the residential sector in 29 cities, and the transportation sector in 9 cities. The correlation between PM2.5 pollution loadings and meteorological parameters reveals that PM2.5 pollution levels in MPPCs are influenced by both local emissions and meteorological factors. Specifically, wind speed and boundary layer height play critical roles in regulating the dispersion of pollution. Consequently, regulating emissions from these cities effectively requires consideration of both the primary emission source sectors and the prevailing meteorological conditions specific to each city's geographical location. PubDate: 2024-08-07
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