Optimal Priority Research Articles

Response Time Analysis and Optimal Priority Assignment for Global Non-Preemptive Fixed-Priority Rigid Gang Scheduling

Response Time Analysis and Optimal Priority Assignment for Global Non-Preemptive Fixed-Priority Rigid Gang Scheduling

Benchmarking of liquid air energy storage with and without added firing against batteries and gas-fired power plants

Rapid deployment of variable renewables is broadly viewed as the primary mechanism for reducing the carbon intensity of electricity systems, motivating the development and implementation of technologies such as liquid air energy storage (LAES). This study investigates the thermodynamic performance of LAES and evaluates the improvements attained through natural gas added firing (AF), determining the coefficients of structural bonds to identify design optimization priorities. Subsequently, LAES configurations are benchmarked against mid-scale natural gas combined cycles (NGCC) and batteries through an economic optimization based on electricity price profiles from different European markets for the year 2023. At a CO2 emissions tax of 100 €/ton, results reveal that the LAES-AF concept located in Germany could compete with an NGCC plant if natural gas prices exceed 8.3 €/GJ, while for prices above 9.1 €/GJ batteries (with cost reduction projected to 2030) were the preferred option. In this range, the LAES-AF plant reduced fuel consumption by 24.0–27.2 % relative to the NGCC benchmark, which would reduce emissions when using natural gas or costs when using costlier carbon-neutral fuels. The standalone LAES plant did not prove economically viable in any location, being outperformed by batteries and requiring a CO2 price above 300 €/ton to compete with LAES-AF. As electricity price volatility keeps rising with growing wind and solar market shares, the economic attractiveness of LAES should improve as longer periods of near-zero electricity prices negate its lower round-trip efficiency and capitalize on its lower cost for energy storage. Therefore, further work on the development of innovative LAES cycles is recommended.

Which Samples Should Be Learned First: Easy or Hard?

Treating each training sample unequally is prevalent in many machine-learning tasks. Numerous weighting schemes have been proposed. Some schemes take the easy-first mode, whereas others take the hard-first one. Naturally, an interesting yet realistic question is raised. Given a new learning task, which samples should be learned first, easy or hard? To answer this question, both theoretical analysis and experimental verification are conducted. First, a general objective function is proposed and the optimal weight can be derived from it, which reveals the relationship between the difficulty distribution of the training set and the priority mode. Two novel findings are subsequently obtained: besides the easy-first and hard-first modes, there are two other typical modes, namely, medium-first and two-ends-first; the priority mode may be varied if the difficulty distribution of the training set changes greatly. Second, inspired by the findings, a flexible weighting scheme (FlexW) is proposed for selecting the optimal priority mode when there is no prior knowledge or theoretical clues. The four priority modes can be flexibly switched in the proposed solution, thus suitable for various scenarios. Third, a wide range of experiments is conducted to verify the effectiveness of our proposed FlexW and further compare the weighting schemes in different modes under various learning scenarios. On the basis of these works, reasonable and comprehensive answers are obtained for the easy-or-hard question.

Rainfall cycle causes the inter-seasonal instability of ecosystem service trade-offs: A case study in the Tana River Basin, Kenya

As a crucial climate condition, the inter-seasonal rainfall cycle directly impacts the livelihoods and productivity of billions residing in monsoon regions. However, there is still limited understanding regarding its effects on trade-offs among ecosystem services (ESs). This knowledge gap may impede the sustainable development of human well-being in these regions. In this study focused on Tana River Basin, a representative monsoon region, we assessed the seasonal supply and trade-offs of three ESs including water yield (WY), soil conservation (SC), and net primary production (NPP), which are closely linked to local human well-being. To comprehend the driving mechanism of inter-seasonal variation, the influential factors were identified using the Geographical Detector Model. Furthermore, by quantifying the seasonal trade-off intensity among three ESs, the managemental optimization priority areas were identified.The results showed that (1) all three ES supply presented an increasing trend from 2000 to 2020. Seasonally, both the supply and trade-offs of ESs were significantly enhanced in the rainy seasons, with spatial heterogeneity observed in the response to inter-seasonal rainfall cycle. (2) Natural condition factors presented stronger influence on the seasonal variation of ES trade-offs, which is crucial to consider the comprehensive hydrological response resulting from vegetation growth as the foundation for these seasonal variations. For the WY-SC trade-off, precipitation was identified as the primary influential factor, while vegetation coverage impacted on SC-NPP and WY-NPP trade-offs. Higher precipitation and vegetation coverage would intensify the trade-off between corresponding ESs. (3) Regarding the spatial distribution of seasonal instability, several sub-watersheds aggregated in the midstream hill region were identified as the primary optimized zones, more flexible management strategies should be implemented preferentially. The findings contribute to a better understanding of how the inter-seasonal rainfall cycle impact on the ES trade-offs, supporting effective ecosystem management in monsoon regions.

Identification of priority conservation areas in Beijing-Tianjin-Hebei using multi-scenario trade-offs based on different spatial scales and their drivers

Identifying priority conservation areas (PCAs) on the basis of ecosystem services (ESs) trade-offs is a crucial step in achieving optimal ecosystem management in the BTH, noting the complex spatio-temporal and scaling effects inherent in these ecosystems. A cross-scale quantification of spatial and temporal changes in ecosystem services and their correlations was conducted in the BTH. Furthermore, the Ordered Weighting Algorithm (OWA) was introduced to simulate a variety of scenarios in order to determine the PCAs and to explore the drivers of ES in the PCAs. The results indicated that Food supply (FS), Water yield (WY), and Soil retention (SR) in the BTH increased by 16.4 × 104t/hm2, 81.81 mm, and 37081.29 t/hm2, respectively. In contrast, Carbon storage (CS) and Habit quality (HQ) exhibited a decrease of 0.01 t/hm2 and 0.03. Spatially, the high-value areas of ESs at the county scale exhibited greater clustering. At the county scale, there were two trade-offs and eight synergies in 2000, and four trade-offs and six synergies by 2020. In contrast, at the raster scale, there were two trade-offs and eight synergies from 2000 to 2020. The mechanisms of change were similar and exhibited spatial heterogeneity. Scenario 6 represents the optimal priority conservation area, exhibiting the most balanced conservation efficiency of each ecosystem service. This scenario enables the simultaneous conservation of multiple ESs. The natural factors within the PCAs exert a stronger influence on ESs than socio-economic factors. The inclusion of interacting factor effects will enhance the explanatory power of the drivers. The findings of this study can serve as a theoretical foundation for cross-scale ecosystem service management decisions.

Uncovering leveraging and hindering factors in socio-ecological interactions: Agricultural production in the Yellow River Basin as an example

Agricultural production and sustainable human livelihoods in large river basins are threatened by climate change, human activities, and resource constraints. However, due to the complexity of socio-ecological interactions and agricultural sustainability, current studies are still limited by a priori knowledge and systematic analyses, as well as by the lack of quantification and identification of key factors and valuable pathway structures for agricultural production activities. Here, we combined observation-based causal inference and network analysis to quantify and assess the complex interactions in agricultural production in the Yellow River Basin (YRB) based on data from 12 factors relevant to agriculture over 40 years. We quantitatively assessed the leveraging and hindering roles of the factors in the interacting network system and provided managers with optimization priorities and possible causal pathways to achieve sustainable agriculture in the basin. For example, the fruit yield and income of rural households were identified as leveraging factors that positively affect the agricultural economy. Groundwater was seen as a hindering factor in dampening the negative impacts of the system, highlighting the importance of preventing groundwater depletion. Moreover, the findings suggest that spatially diverse causal interaction structures exist in the YRB and have shaped a variety of distinctive agricultural development modes. Our research ideas and results highlight both systemic considerations and the amplifying or dampening role of factors in interaction pathways, providing valuable quantitative insights into the management and intervention of sustainable agriculture in large river basins. Owing to replaceable and extensible network models, the methodology has the potential to be utilized in a variety of study areas and topics with complex socio-ecological interactions.

Optimizing Priority Sequencing Rules in Parallel Machine Scheduling: An Evaluation and Selection Approach using Hybrid MCDM Techniques

Priority sequencing criteria are of utmost importance in the determination of the sequence in which jobs are processed at workstations in parallel machine scheduling. The utilization of diverse priority rules can result in varied sequencing arrangements, hence requiring more experimentation to ascertain the optimal rule. Hence, it is imperative to formulate a thorough approach for the selection of the most suitable priority sequencing rule from the standpoint of management decision-making. The objective of this research is to analyze and compare six different priority sequencing rules in the context of parallel machine scheduling. Additionally, a methodology is proposed for the assessment and selection of the most suitable rule. This methodology combines the full consistency method (FUCOM) with the measurement of alternatives and ranking according to compromise solution (MARCOS) method, which are both multi-criteria decision-making techniques. When reviewing and selecting the optimal priority sequencing rule, seven parameters are taken into consideration. The weights of these criteria are computed using the FUCOM method, while the relative proximity values of all priority sequencing rules are derived by the MARCOS method. The data indicate that the priority sequencing rules are prioritized according to their level of importance. The approach outlined in this study is essential for workstation management to make well-informed decisions regarding the choice of the most advantageous priority sequencing rule for parallel machine scheduling.

Transforming Toxicity Assessment through Microphysiology, Bioprinting, and Computational Modeling

Background: Traditional toxicity testing emphasizes animal models with growing concerns regarding predictive capacity, throughput and ethics. Rapid innovation surrounding human cell platforms, bioengineered tissues, omics techniques and computational tools offers more modern alternatives aligned with expanding knowledge of chemical biological pathways. These disruptive approaches promise immense potential to transform next-generation chemical safety assessment and drug development pipelines. Purpose: This review provides clinical researchers an updated, comprehensive perspective across evolving areas of focus in new toxicity testing methods with analysis of latest advances and translational context. Main Body: We survey progress in two- and three-dimensional human cell cultures recapitulating tissue/organ complexity impossible in conventional assays. Complementing this, computational modeling integrates structure-activity relationships, physicochemical properties and physiological interactions to predict pharmacokinetics and toxicity in silico. Expanding model organisms add further dimensionality and demographic relevance. High-throughput omics and imaging technologies unravel mechanisms and illuminate biomarkers undetectable by standard measures. Specialized techniques show high promise addressing toxicodynamic intricacies within disease contexts like diabetes and NAFLD. Evaluating traditional medicines and expanding phytochemicals likewise represents an area of growth well-suited for contemporary platforms. Future outlook weighs remarkable potential advantages in reducing animal testing demands, enabling precision toxicology links to clinical medicine and overhauling core chemical risk assessment frameworks. Conclusion: This review intends to catalyze discourse on strategic optimization priorities and roadmaps towards fully unlocking the immense yet still emerging public health potential of these disruptive techniques poising transformation in toxicity sciences centered on human-focused models.

Design of the Light Source Layout Optimization Strategy Based on Region Partition and Pre-Bias Compensation for Indoor Visible Light Communication Systems

Optimization of the light source layout is an important issue for indoor visible light communication systems, as it affects the received optical power distribution and user perception. In this paper, we propose a local optimization strategy for the light source layout that balances optimization effectiveness with optimization efficiency. First, we divide the optimization region into multiple sub-regions with different sizes and optimization priorities, where the sizes and optimization priorities of the individual sub-regions are determined based on the effect minimization principle among the sub-regions. We then calculate the pre-bias factor based on the equivalent mapping, which can compensate for the effect of the light sources in the latter optimized sub-region on the source layout optimization in the current sub-region. Finally, we search for the coordinate of a single light source for each sub-region using the variance of the squared distance between the projection of the light source on the receiving plane and the received point as a fitness function. Simulation results show that the proposed optimization strategy performs well when the vertical distance between the ceiling and the receiving plane is not less than 2.85 m.

Multicarrier Spread Spectrum for Mega-Constellation Satellite Networks: Challenges, Opportunities, and Future Trends

With the emerging concept of satellite mega-const-ellations (SatCons), connections are about to become faster and broader, building an engine to boost innovation and productiv-ity for every industry. In the following decades, over a hundred of SatCons with enormous application potentials are on the cusp of deployment, and their long-term success hinges on reliable information exchanges. We identify the renascent multi-carrier spread spectrum (MCSS) paradigm of sufficient spectral flexibility as the most effective and reasonable candidate for such issues. This article first sheds light on possible use cases for MCSS in SatCons, such as navigation-communication-telecontrol integration, physical-layer security, and intelligent anti-jamming. It also enlightens opportunities by virtue of the many benefits that MCSS may offer, and highlights major challenges encountered during its utilization. Finally, insights into future trends are provided. This article could support system designers in finding the right optimization priorities for future SatCons.

Optimal Priority Rule-Enhanced Deep Reinforcement Learning for Charging Scheduling in an Electric Vehicle Battery Swapping Station

For a battery swapping station (BSS) with solar generation, N charging bays, and an inventory of M batteries, we study the charging scheduling problem under random EV arrivals, renewable generation, and electricity prices. To minimize the expected weighted sum of charging cost (sum of electricity and battery degradation costs) and EV owners’ waiting cost, we formulate the problem as a Markov decision process with unknown state transition probability. Under a mild heavy-traffic assumption, we rigorously establish the optimality of the Less Demand First (LDF) priority rule under arbitrary system dynamics: batteries with less demand shall be charged first. The optimality result enables us to integrate the LDF rule into a state-of-the-art deep reinforcement learning (DRL) method, proximal policy optimization (PPO), reducing the dimensionality of its output from O(M+N) to O(1), without loss of optimality in the heavy-traffic scenario. Numerical results (on real-world data) demonstrate that the proposed LDF enhanced PPO approach significantly outperforms classical DRL methods and FCFS (first come, first served) priority rule based DRL counterparts.

Impact of priority assignment on schedule-based attacks in real-time embedded systems

In this paper, we investigate the impact of priority assignment on schedule-based attacks in fixed-priority real-time systems. Through the schedule-based attacks, attackers may manipulate the input and output buffers of victim tasks, cause instability, and/or steal hidden information. The success of those attacks highly depends on the proper positioning of the malicious task with respect to the victim tasks in the actual schedule. Firstly, we develop an optimal priority assignment algorithm that incorporates the user specified priority preferences that reflect task types (anterior task, posterior task, or victim task), while still meeting all the deadlines. Secondly, we propose a dynamic delaying algorithm to further reduce posterior schedule-based attacks at run-time. Finally, we derive and compare the performance of six attack-aware priority-assignment policies as well as their dynamic extensions through comprehensive simulations. Our results suggest that the well-known RMS algorithm can be easily outperformed in terms of reducing schedule-based attacks, by properly specifying the relative priorities of malicious, anterior, and posterior tasks through our optimal algorithm. In particular, the AMP assignment, which assigns the anterior, malicious, and posterior tasks execution priorities in decreasing order, is shown to offer rather robust and consistent performance even at high system utilizations. Our framework explicitly incorporates the logical execution time and attack effective window constraints to model schedule-based attacks in realistic manner.

Study of the Hull Structural Deformation Calculation Using the Matrix Displacement Method and Its Influence on the Shaft Alignment

The analysis of the influence of hull deformation on shaft alignment is predominately conducted using the finite element method (FEM), which is time-consuming, labor-intensive, and challenging to use for iterative hull design optimization. In this paper, hull deformation is separated into two parts—global deformation and local deformation, simplified to a single-span beam model and a grillage beam model, respectively—then solved using the matrix displacement method (MDM). Compared to FEM, the proposed method has a small calculation error, proving its correctness, while the calculation time is greatly reduced. The proposed method has been used to calculate the hull deformation of a ship under various conditions and evaluate its influence on shaft alignment. The results indicate that under certain conditions, the bearing reaction forces are constant, whereas the bearing pressure changes as a consequence of the change in shaft-to-bearing angle. The comparison between local rotation and shaft-to-bearing angle reveals that bearings in various positions follow distinct laws. We suggest that the shaft-to-bearing angle be used as an additional parameter in the evaluation of shaft alignment calculations. Moreover, when optimizing bearing pressure, bearings in different positions are affected differently by global and local deformation, and their optimization priorities are distinct.

Lymphoma Recognition in Histology Image of Gastric Mucosal Biopsy with Prototype Learning.

Lymphomas are a group of malignant tumors developed from lymphocytes, which may occur in many organs. Therefore, accurately distinguishing lymphoma from solid tumors is of great clinical significance. Due to the strong ability of graph structure to capture the topology of the micro-environment of cells, graph convolutional networks (GCNs) have been widely used in pathological image processing. Nevertheless, the softmax classification layer of the graph convolutional models cannot drive learned representations compact enough to distinguish some types of lymphomas and solid tumors with strong morphological analogies on H&E-stained images. To alleviate this problem, a prototype learning based model is proposed, namely graph convolutional prototype network (GCPNet). Specifically, the method follows the patch-to-slide architecture first to perform patch-level classification and obtain image-level results by fusing patch-level predictions. The classification model is assembled with a graph convolutional feature extractor and prototype-based classification layer to build more robust feature representations for classification. For model training, a dynamic prototype loss is proposed to give the model different optimization priorities at different stages of training. Besides, a prototype reassignment operation is designed to prevent the model from getting stuck in local minima during optimization. Experiments are conducted on a dataset of 183 Whole slide images (WSI) of gastric mucosa biopsy. The proposed method achieved superior performance than existing methods.Clinical relevance- The work proposed a new deep learning framework tailored to lymphoma recognition on pathological image of gastric mucosal biopsy to differentiate lymphoma, adenocarcinoma and inflammation.

NOx emission deterioration in modern heavy-duty diesel vehicles based on long-term real driving measurements

NOx emissions from diesel vehicles generally deteriorate with increased durability mileage owing to the wear and deterioration of engines and after-treatment systems. Three China-VI heavy-duty diesel vehicles (HDDVs) were selected for four-phase long-term real driving emission (RDE) tests using the portable emission measurement system (PEMS). After 200,000 km of on-road driving, the maximum NOx emission factor of the test vehicles (387.06 mg/kWh) was found to be significantly lower than the NOx limit of 690 mg/kWh. Under all driving conditions, the NOx conversion efficiency of selected catalytic reduction (SCR) decreased almost linearly as the durability mileage increased. Importantly, the deterioration rate of the NOx conversion efficiency in low-temperature intervals was discernibly higher than that in high-temperature intervals. The NOx conversion efficiency at 200 °C dropped by 16.67–19.82% with higher durability mileage; however, the highest values at 275–400 °C only decreased by 4.11%. Interestingly, the SCR catalyst at 250 °C showed strong NOx conversion efficiency and durability (maximum decline of 2.11%). Overall, the poor de-NOx performance of SCR catalysts at low temperatures significantly challenges the long-term effective control of NOx emissions from HDDVs. Thus, improving the NOx conversion efficiency and durability at low-temperature intervals is the top priority for SCR catalyst optimization; NOx emissions from HDDVs at low velocities and loads should also be monitored by environmental authorities. The linear fitting coefficient for the NOx emission factors of the four-phase RDE tests was 0.90–0.92, indicating that NOx emissions deteriorated linearly with an increase in mileage. Based on the linear fitting results, the NOx emission control of the test vehicles during 700,000 km of on-road driving was highly likely to be qualified. These results can be used by environmental authorities to supervise the NOx emission conformity of in-use HDDVs after validation using other types of vehicles.

Optimizing the Giant Panda National Park's zoning designations as an example for extending conservation from flagship species to regional biodiversity

Informed zoning of protected areas to designate where certain activities can take place is important to their success. The conventional strategy has been to design protected areas based on flagship species; however, this has often proven ineffective for protecting other sympatric wildlife. Here, using China's Giant Panda National Park (GPNP) as an example, we evaluated the effectiveness of integrating multiple conservation parameters for flagship and sympatric endangered species to optimize zoning designations, without compromising or jeopardizing the primary goal of protecting giant pandas. We conducted a stepwise spatial prioritization according to taxonomic, phylogenetic, and functional diversity for 48 endangered bird and 23 mammal species. Conservation priority areas for each taxon were spatially congruent across diversity indices but differed between taxa. Species richness provided only a weak indicator of conservation values. The current designation scheme for giant panda protection performs moderately well in terms of sheltering sympatric endangered birds and mammals, but there is still scope for improvement. After balancing competing land uses, we identified 7731 km2 with the highest conservation values across taxa and diversity indices, with 26 % of this optimal priority area lying outside of current core protection zones of the GPNP. We recommend that GPNP's current zoning designation criteria are modified to conserve a broader range of species alongside giant pandas, especially to better protect sites from human disturbance. Our findings also have the broader potential to inform conservation planning for protected areas worldwide, enabling the protection of flagship species to be integrated with broader regional biodiversity conservation.

Preoperative planning to preserve glenoid subchondral bone in anatomical total shoulder replacement

Glenoid loosening remains a concern in anatomical total shoulder replacement. Preoperative planning software allows optimization of the component positioning, but the target orientation remains unclear due to conflicting optimization priorities. Commonly, the component is aligned to the prescribed version and inclination that reflect the population's average anatomy. The freehand technique attempts to secure strong fixation by aiming to preserve the subchondral bone. This study compared the state of the subchondral plate after reaming and compared the results of these two techniques. Two groups of shoulder computed tomography scans were assessed, 34 normal and 34 osteoarthritic. Preoperative planning software was used to place the glenoid component in prescribed adjusted angles, with neutral (0o,0o), retroverted (-10o,0o) and inclined alignment (0o,10o). The computed tomography Hounsfield values at the virtually reamed surface were assessed to determine the percentage of the intended component-bone interface consisting of cortical bone, here termed "cortical bone seating". This was then compared to positioning the component using a freehand technique. The freehand technique improved cortical bone seating in the osteoarthritic group with a mean (standard deviation) of 53.3% (14.3), while neutral alignment resulted in 36.7% (10.8), retroversion 40.4% (13.1), and inclination 39.3% (13.5), P<.001. A similar trend was observed in the normal group. The freehand method resulted in significantly improved cortical bone seating compared to the prescribed adjusted angles. These findings question the use of a one-size-fits-all-orientation and suggest that applying a technique that aims for maximum cortical fixation (freehand) may reduce the risk of aseptic loosening.

Understanding Customers’ Insights Using Attribution Theory: A Text Mining and Rule-Based Machine Learning Two-Step Multifaceted Method

By looking at complaints made by guests of different star-rated hotels, this study attempts to detect associations between complaint attributions and specific consequences. A multifaceted approach is applied. First, a content analysis is conducted to transform textual complaints into categorically structured data. Furthermore, a web graph analysis and rule-based machine learning method are applied to discover potential relationships among complaint antecedents and consequences. These are validated using a qualitative projective technique. Using an Apriori rule-based machine learning algorithm, optimal priority rules for this study were determined for the respective complaining attributions for both the antecedents and consequences. Based on attribution theory, we found that Customer Service, Room Space, and Miscellaneous Issues received more attention from guests staying at higher star-rated hotels. Conversely, cleanliness was a consideration more prevalent amongst guests staying at lower star-rated hotels. Qualitative research was conducted to corroborate the findings. Other machine learning techniques (i.e., Decision Tree) build rules based on only a single conclusion, while association rules attempt to determine many rules, each of which may lead to a different conclusion. The main contributions of this study lie in the fact that this is one of the first attempts to detect correlations within the online complaining behaviors of guests of different star-rated hotels by utilizing rule-based machine learning.

SUR-Driven Video Coding Rate Control for Jointly Optimizing Perceptual Quality and Buffer Control.

Rate control plays an important role in video coding and has attracted lots of attention from researchers. However, the problems of human visual experience and buffer stability still remain. For scenes with drastic motions, parts of distortions can be masked due to the limitation of the Human Visual System (HVS), while buffers tend to suffer more overflow and underflow cases from the fluctuating bits. In this paper, we propose a novel joint rate control scheme, which is composed of the proposed SUR-based perception modeling and the proposed SUR-based Perception-Buffer Rate Control (PBRC), for HEVC to maximize human visual perception quality while preventing the underflow and overflow of buffers. First of all, to effectively model human visual quality, we introduce the perception-related Satisfied-User-Ratio (SUR) metric into the rate control process. Secondly, a time-efficient video quality prediction method called Fast Visual Multimethod Assessment Fusion (VMAF) Quality Prediction (FVQP) is designed for the generation of SUR curves within an affordable computational complexity. Thirdly, a dual-objective optimization framework is established. By jointly conducting perception modeling and PBRC, we can flexibly adjust the optimization priority between human visual quality and buffer stability, and thus the quality of achieved reconstructed videos can be effectively improved because of the decrease in frame skipping. Experimental results demonstrate that the proposed joint rate control scheme improves the human visual experience when considering frame skipping and more effectively stabilizes buffer stability than existing methods.

An implementation of the SPH method and its application to two-dimensional dam break cases

A basic implementation of the weakly compressible smoothed particle hydrodynamics (W-SPH) method is used to model the open surface flow phenomena of two different types of hydraulic problems. They were both two-dimensional approximations of known hydraulics problems: dam break over dry bed and dam break over wet bed with shallow and deep variants. For all models, the results were compared quantitatively with data from waterfront profiles from previously published experiments. A qualitative approach was used to assess the general profile shape comparing the numerical hydraulic profile results with published photographs of all setups shown. The numerical model converged to a stable solution with relatively low-resolution setups (2 000 to 20 000 particles) and a good correlation was found with the experimental and numerical references, even without the application of correction algorithms. Numerical oscillations in the density field did produce small artificial waves and vortices, but did not interfere with the behavior of the bulk of the fluid except for some delay in the speed of surface waves. The choice of developing an entirely original, self-contained source code, although very insightful, does mean that the neighboring particle search algorithm used considerably increments the associated computational cost. However, using this insight along with the basis of the comparison with experimental results, optimization priorities for the source code were found and some guidelines to choose fluid resolution and boundary density can be applied for future cases where the flow phenomena are similar to those studied. Also, the experience of writing the SPH source code that was used to solve the numerical models, shows the path for future developments on fluid flow modeling using this method.