Search Results (30)

Simulating wave propagation is crucial for forecasting processes offshore and near the coast. Many operational wave models consider only atmospheric and wave forcing as boundary conditions. However, waves and currents are interdependent, and simulating their interaction is crucial for accurately representing wave propagation. This study examines the influences of the current velocity and water levels on waves on the southern coast of the Iberian Peninsula. These forcing elements were simulated by a 3D hydrodynamic model (MOHID) and included in the Simulating WAves Nearshore (SWAN) model. The standalone SWAN model was calibrated and validated by comparing results of significant wave height, mean wave direction, and peak period with in situ observations. Then, the effects of water levels and current velocities on wave propagation were assessed by forcing the SWAN model with water levels as well as current velocities extracted from different depths: the surface layer and depth-averaged velocities from the surface down to 10 m, 20 m, and the full water column. The results revealed that incorporating the current velocity and water levels from MOHID into the SWAN model reduced the root mean square error (RMSE) between 1.6% and 27.6%. The most accurate results were achieved with model runs that included both the current velocity from the surface layer and water levels. Opposing currents resulted in increases in wave height, whereas following currents resulted in decreases in wave height. This work presents novel results on the effects of hydrodynamics on wave propagation along the southern coast of the Iberian Peninsula, a region of key importance for the blue economy. Full article

Background/Objectives: Ready-to-use therapeutic foods (RUTFs) are a common treatment for children under five years diagnosed with acute malnutrition. However, traditional RUTFs are often not locally produced, and the costs of the RUTF can be a barrier to access in India and Pakistan. Our goal was to utilize linear programming (LP) to generate an RUTF formulation based on ingredients locally available in India and Pakistan. We also aim to evaluate the effectiveness of LP in generating such a recipe that is also nutrient-optimized to promote neurocognitive recovery. Methods: The RUTF recipe was generated by applying linear programming to a database of crop ingredients available in India, subject to nutritional constraints. The resulting formulation was produced and evaluated for nutrition content and shelf life. The efficacy of the LP tool was also evaluated based on the formulated product. Results: We demonstrate that the linear programming tool is largely accurate in predicting the true nutritional content of the formulation. Furthermore, the generated formulation, per 100 g, meets many global macronutrient standards for RUTFs while maintaining a predicted cost that is lower than that of industry-standard products. The conducted shelf-life study indicates the viability of the RUTF throughout an accelerated testing period. In addition, the satisfactory consideration of LA and ALA levels provides our RUTF with the potential to address concerns about low DHA levels, and thereby cognitive health, as compared to traditional RUTFs. Conclusions: We use linear programming to generate an affordable and fatty acid-optimized RUTF based on locally available ingredients. Therefore, this formulation holds immense potential to benefit communities in India and Pakistan facing high levels of child malnutrition. Full article

Hydrological modeling is essential for the sustainable management of watershed systems. Physically based models like MOHID-Land simulate soil water dynamics using Richards’ equation, parameterized through the van Genuchten–Mualem (VGM) model. Although the sensitivity of individual VGM parameters—residual water content (${\theta }_r$), saturated water content (${\theta }_s$), pore size distribution ($n$), inverse of air entry pressure ($\alpha$), and saturated hydraulic conductivity ($K_{sat}$)—is well documented, their combined effects remain underexplored. This study assessed both isolated and joint impacts of these parameters through a deterministic ±10% perturbation scheme, resulting in 31 unique parameter combinations. Model performance was evaluated using the Nash–Sutcliffe Efficiency (NSE) and Percent Bias (PBIAS). Full-parameter interaction achieved the best results (NSE = 0.50, PBIAS = 25.32), compared to the uncalibrated baseline (NSE = 0.01, PBIAS = 34.06). The pair ${\theta }_s$ and $n$ emerged as the most influential. Adding secondary parameters to this core pair yielded only marginal performance gains, while removing them from the full set caused similarly marginal declines. These findings reveal a hierarchical sensitivity structure, emphasizing ${\theta }_s$ and $n$ as key targets for calibration. Prioritizing this pair enables a more efficient soil calibration process, preserving model accuracy while reducing computational cost by limiting parameter space exploration. Full article

The heart is essential to human life, so it is important to protect it and understand any kind of damage it can have. All the diseases related to hearts leads to heart failure. To help address this, a tool for predicting survival is needed. This study explores the use of several classification models for forecasting heart failure outcomes using the Heart Failure Clinical Records dataset. The outcome contrasts a deep learning (DL) model known as the Convolutional Neural Network (CNN) with many machine learning models, including Random Forest (RF), K-Nearest Neighbors (KNN), Decision Tree (DT), and Naïve Bayes (NB). Various data processing techniques, like standard scaling and Synthetic Minority Oversampling Technique (SMOTE), are used to improve prediction accuracy. The CNN model performs best by achieving 99%. In comparison, the best-performing ML model, Naïve Bayes, reaches 92.57%. This shows that deep learning provides better predictions of heart failure, making it a useful tool for early detection and better patient care. Full article

The complex lagoon system of Carmen, Pajonal, and Machona in the Southern Gulf of Mexico is characterized by highly active sedimentary dynamics. To reproduce the sedimentary dynamics processes, the MOHID model, coupled with the SWAN wave model, was applied to different scenarios through a climatic analysis of winds. Historical wind data indicate that the region has experienced a significant shift in the principal wind component over the last two decades. Furthermore, hurricanes have impacted the lagoon system on multiple occasions in recent decades. Five numerical experiments were conducted, considering both historical and present-day wind conditions, the impact of Hurricane Larry, and engineering works such as breakwaters, to better understand the sedimentary dynamics of the lagoon system. Model results revealed intense and variable sediment transport depending on the intensity and direction of the prevailing winds, waves, extreme weather events, and breakwater locations. Full article

Recent studies have incorporated tidal elevation into hydrological models, yet they have not focused on simulating or evaluating tidal processes within these frameworks. Integrating tidal dynamics improves the representation of terrestrial–coastal interactions, including groundwater fluctuations, vegetation dynamics, and sediment transport. This study evaluates the capability of MOHID-Land, a physically based hydrological model, to simulate macro-tidal conditions in an Amazonian estuary. MOHID-Land enables tidal simulation by incorporating water-level time series as boundary conditions. A sensitivity analysis was conducted to (i) evaluate two global tidal models as boundary conditions; (ii) verify the impact of hydrological processes on water levels; and (iii) assess the effect of different bathymetries on water dynamics. The model effectively simulated tidal oscillations with good accuracy across eight tidal stations, although the inner stations required improved bathymetry. The Reference, Atmosphere, Porous Media and Vegetation (AtPmVg), and Finite Element Solution (FES) version 2014 (FES2014) simulations yielded similar water levels and goodness-of-fit metrics. While MOHID-Land is robust, and water level modeling is insensitive to meteorological, soil, or vegetation parameters, the model is highly sensitive to bathymetry. This study enhances the understanding of the applicability of hydrological models in terrestrial–coastal modeling. Full article

Soil controls water distribution, which is crucial for accurate hydrological modeling. MOHID-Land is a physically based, spatially distributed model that uses van Genuchten–Mualem (VGM) functions to calculate water content in porous media. The hydraulic soil parameters of VGM are dependent on soil type and are typically estimated from experimental data; however, they are often obtained using pedotransfer functions, which carry significant uncertainty. As a result, calibration is frequently required to account for both the natural spatial variability of soil and uncertainties estimation. This study focuses on a representative Atlantic Forest watershed. It assesses the sensitivity of channel flow to VGM parameters using a mathematical approach based on residuals derivative, aimed at enhancing soil calibration efficiency for MOHID-Land. The model’s performance significantly improved following calibration, considering only five parameters. The NSE improved from 0.16 on the base simulation to 0.53 after calibration. A sensitivity analysis indicated the curve adjustment parameter ($n$) as the most sensitive parameter, followed by saturated water content (${\theta }_s$) considering the 10% variation. Additionally, a combined change in ${\theta }_s$, $n$, residual water content (${\theta }_r$), curve adjustment parameter ($\alpha$), and saturated conductivity ($K_{sat}$) values by 10% significantly improves the model’s performance, by reducing channel flow peaks and increasing baseflow. Full article

Three-dimensional printing technology offers significant advantages in the production of orthopedic casts, providing a promising alternative to conventional plaster and fiberglass materials. Polylactic acid (PLA) is widely used for this purpose; however, its adoption is limited due to poor mechanical properties, including high brittleness, low thermal stability, and limited elongation. These challenges can be mitigated by blending PLA with other biodegradable polymers. This study investigated a blend of PLA with poly(3-hydroxybutyrate-co-3-hydroxyvalerate) (PHBV), a type of polyhydroxyalkanoate (PHA), and polycaprolactone (PCL) for the development of 3D printed orthopedic casts. The key mechanical properties—tensile strength, percent elongation at break, Young’s modulus, flexural strength, flexural modulus, and impact strength—were evaluated as a function of the printing parameters, including nozzle temperature, layer height, and raster angle. The grey relational analysis (GRA) approach was applied to optimize these mechanical properties. The optimal printing parameters were found to be a nozzle temperature of 180 °C, a layer height of 0.18 mm, and a raster angle of 0°, resulting in a tensile strength of 44.4 ± 4.4 MPa, an elongation at break of 68.5 ± 11.6%, a Young’s modulus of 948.7 ± 25.1 MPa, a flexural strength of 54.6 ± 8.9 MPa, a flexural modulus of 1549.3 ± 141 MPa, and an impact strength of 80.77 ± 5.6 J/m. Statistical analysis using analysis of variance (ANOVA) revealed that for tensile strength, 50.18% was influenced by the raster angle, 26.38% by the layer height, and 18.92% by the nozzle temperature; for flexural strength, 69.81% was influenced by the raster angle, 20.67% by the layer height, and 3.53% by the nozzle temperature; and for impact strength, 75.11% was influenced by the raster angle, 13.16% by the layer height, and 4.45% by the nozzle temperature. Full article

This study addresses the challenge of identifying pollutant sources in aquatic coastal environments using inverse problem techniques hampered by particularities in hydrodynamic and Lagrangian models. An approach is presented employing the General Inverse Problem Platform (GRIPP) coupled with a General Simulated Annealing (GenSA) algorithm for robust backtracking. This methodology was applied to a hypothetical case study in Guanabara Bay, Brazil, using the MOHID Water platform for hydrodynamic and Lagrangian simulations. GRIPP significantly improved emission identification and pathway representativeness compared to traditional backtracking methods by exploring multiple potential particle origins and optimizing seeding parameters. The optimization yielded a solution with a mean error of 0.019 degrees between predicted and observed tracer locations. This demonstrates the potential of GRIPP and GenSA for solving practical environmental problems in coastal regions. The use of GRIPP can bypass eventual numerical errors in cases of long-term pollution source identification when compared to traditional methods. This approach contributes with blended models for the identification of potential sources of Lagrangian tracer emissions, as well as a potential solution to determine the pathways of pollutants. Full article

Simulating present status and future changes in water level and water temperature in estuaries is important for the advancement of knowledge and decision-making on subjects such as estuarine dynamics and human activities since these variables impact biochemical variables (e.g., oxygen and pH), fish growth, and disease occurrence. Numerical models such as MOHID—Water Modeling System can be used to predict water temperature and water levels under different force conditions. This study evaluates the effects of sea level rise (SLR), air temperatures, and freshwater discharge on water levels and water temperature in the Ria de Alvor under present and future variations. The results show a significant impact of SLR on water levels in the Ria de Alvor, which is more pronounced during spring tides. Air temperature also impacts water temperature, generating changes higher than 2 °C inside the estuary. The freshwater discharge, despite the relatively low values, contributes significantly to changes in surface water temperature, mainly at the estuary’s upper reaches, where it contributes to a decrease in water temperature in winter conditions while elevating them during the summer. The results contribute to the advancement of knowledge on a small, barely studied estuarine system, giving insights into the establishment of human activities such as aquaculture exploitation within the estuary. Full article

The linkage between the salt wedge, tidal patterns, and the Magdalena River discharge is established by assessing the ensuing parameters: stratification (ϵ), buoyancy frequency (β), potential energy anomaly (φ), Richardson number by layers (RL), and bottom turbulent energy production (P). The salinity, temperature, density, and water velocity data utilized were derived from MOHID 3D, a previously tailored and validated model for the Magdalena River estuary. To grasp the dynamics of the river, a flow regime analysis was conducted during both the wet and dry climatic seasons of the Colombian Caribbean. The utilization of this model aimed to delineate the estuary’s spatial reach, considering flow rates spanning from 2000 to 6500 m³/s across two tidal cycles. This approach facilitates the prediction of the position, stability, and stratification degree of the salt front. Among the conclusions drawn, it is highlighted that: 1. The river flow serves as the principal conditioning agent for the system, inducing a strong estuary response to weather stations; 2. The extent of wedge intrusion and the river discharge exhibit a non-linear, inversely correlation; 3. Tidal waves cause differences of up to 1000 m in the horizontal extent of the wedge; 4. Widespread channel erosion occurs during the rainy season when the salt intrusion does not exceed 2 km; 5. Flocculation processes intensify during the transition between the dry and wet seasons; 6. The stability of the salt layering and the consolidation of the FSI–TMZ are contingent upon the geometric attributes of the channel. Full article

This study aims to examine the dynamic response of a polyvinylidene fluoride (PVDF) piezoelectric sensor which is embedded into an aluminum coupon using ultrasonic additive manufacturing (UAM). Traditional manufacturing techniques used to attach smart materials to metals on the surface have drawbacks, including the potential of exposing the sensor to adverse environments or physical degradation during manufacture. UAM can avoid these issues by integrating solid-state metal joining with subtractive processes to enable the fabrication of smart structures. A commercial PVDF sensor is embedded in aluminum with a compression technique to provide frictional coupling between the sensor and the metallic matrix. The PVDF sensor’s frequency bandwidth and impact detection performance are evaluated by conducting cantilever and axial impact tests, as well as harmonic excitation tests with an electrodynamic shaker. Under axial loading, the embedded sensor displays high linearity with a sensitivity of 43.7 mV/N, whereas impact tests in the cantilever configuration exhibit a steady decay rate of 0.13%. Finally, bending tests show good agreement between theoretical and experimental natural frequencies with percentage errors under 6% in two different clamping positions, and correspond to the maximum voltage output obtained from the embedded PVDF sensor at resonance. Full article

This paper presents the Simulation Management System for Operational Coastal Hydrodynamic Models, or SMS-Coastal, and its novel methodology designed to automate forecast simulations of coastal models. Its working principle features a generic framework that can be easily configured for other applications, and it was implemented with the Python programming language. The system consists of three main components: the Forcing Processor, Simulation Manager, and Data Converter, which perform operations such as the management of forecast runs and the download and conversion of external forcing data. The SMS-Coastal was tested on two model realisations using the MOHID System: SOMA, a model of the Algarve coast in Portugal, and BASIC, a model of the Cartagena Bay in Colombia. The tool proved to be generic enough to handle the different aspects of the models, being able to manage both forecast cycles. Full article

The presence of board members with good governance attributes is value-relevant since it influences investors’ investment decisions. The value relevance is expected to improve with the newly introduced extended audit report to disclose key audit matters (KAMs). KAM disclosure provides information about issues faced by external auditors in the auditing of a company’s financial statement. Since the disclosure of KAM involves discussion and negotiation between the board and external auditor, it gives an indication that board value relevance can be affected by KAM disclosure. Using 931 firm-year observations from firms listed on the Bursa Malaysia between 2016 and 2019, this study re-examined the value relevance of the board and whether such value relevance improves with the disclosure of KAMs. The findings indicated that some board attributes influenced investors’ reactions negatively. The disclosure of KAM served as both an indirect mediator and a complementary mediator to increase the board’s value relevance. Investors reacted less negatively with KAM disclosure and companies’ values improved. The findings provide an insight into the role of KAM disclosure in reducing information asymmetry and assisting investors in making investment decisions. The findings support policymakers’ decisions to mandate the implementation of ISA 701, which requires the disclosure of KAMs. Full article

Urban pluvial floods are the outcome of the incapacity of drainage systems to convey the runoff generated by intense rainfall events. Cities have been struggling to control such hazards due to several pressures, such as urbanization increase, more frequent experiences of extreme rainfall events, and increases in tide levels. Such pressures demand the study of adaptation strategies, which conventional one-dimensional drainage models fall short of simulating. Thus, 1D/2D models have been emerging with the aim of allowing better integration of key processes for flood modeling, namely, runoff interception by stormwater inlet devices and manhole overflows. The current paper presents a 1D/2D urban flood model based on an offline coupling procedure between the 1D model SWMM and the 2D model MOHID Land. The SWMM/Land model is applied to a synthetic street case study and to a real case study in downtown Albufeira, Portugal. The results obtained for the real case study are coherent with local observations of past flooding events, and the model shows potential for better decision-making regarding urban flood risk management. Full article