Set Of Parameters Research Articles

Modelling of Constructed Wetland Using Numerical Methods

Purpose: This research intends to compare two numerical method models, which are Runge-Kutta and by using HYDRUS software. Theoretical reference: One of the consequences of human population that grows rapidly is the increase of human activity, along with the increase of pollutant that flows into water bodies. Water pollution is a significant environmental issue that threatens the health of ecosystems and human populations worldwide. Method: To determine a set of parameters of the mechanism in a constructed wetland, a numerical model may be of aid to acquire parameters which represent the mechanism of pollutant removal in it. Result and Conclusion: This study yields different efficiency of pollutant removal when simulated with HYDRUS and with Runge-Kutta. The differences in the effectivity results in HYDRUS and Runge-Kutta might be caused by the detail of simulated mechanism, where HYDRUS considers the mechanism happened in soil column, while Runge-Kutta only focuses on the water column. Implication of research: In order to observe the effectivity of a constructed wetland, a numerical model may be used. The parameters needed to represent the environment and properties of both the plants and soil can be obtained by building a demonstration plot. Originality/ value: to compare two numerical method models, which are Runge-Kutta and by using HYDRUS software.

Urban hydrological model (UHM) developed for an urban flash flood simulation and analysis of the flood intensity sensitivity to urbanization

An urban hydrological model (UHM) forced by radar-observed rainfalls was developed for producing rainstorm-related urban inundation maps for obtaining flash flood forecasts. The parameter sets of the land use and land cover (LULC) and urban drainage capacity derive from satellite multispectral images and high spatial resolution GIS datasets, relating to the urban hydrology and hydraulic properties. The hydrodynamics model was based on the simplified shallow water equation (SWE) neglecting the convective acceleration and pressure terms in the St. Venant equation. The intense convective rainstorm that induced heavy flash floods on 21 Jul. 2012 and the large-scale stratiform precipitation that occurred from 19 Jul. to 21 Jul. 2016 in Beijing were deliberately selected to perform flood-simulation and model-validation case studies. The simulation of severe flooding scenarios in the 2012 event was fairly reproduced and verified using media reports. In the case studies, the sensitivity tests verified that the flood intensity (1) would be enhanced slightly on pure impervious surfaces, (2) would be reduced significantly on pure pervious surfaces, and (3) would increase by 30%–60% without a drainage system. No false alarm could be perceived in the simulation of the 2016 event in a prolonging stratiform precipitation. The case studies confirmed the perspectives of using the hydrological model fed by QPEs/QPFs for flash flood forecasting and concluded that the hourly precipitation surpassing 30 mm/h would be indicative in impending flash floods in Beijing metropolis.

Multiscale microstructure containing nanometer-scale precipitations and stacking faults yields a high-strength Al-5Cu alloy by electron beam freeform fabrication

Electron beam freeform fabrication (EBF3) additive manufacturing technique has been successfully employed to print the non-weldable Al-5Cu thin-walled parts through an optimized set of process parameters. Post-heat treatment is conducted to engineer the desirable microstructure containing multiple nanostructures, i.e., the nanoscale θ" and θ' precipitates, stacking faulted zones and Al-Cu-Cd clusters, leading to a high-strength Al-5Cu alloy with good ductility. The grain formation mechanisms are revealed via a combinatorial simulation and experimentation. It is shown that EBF3 process can produce three different types of grain microstructures: fully equiaxed, fully columnar, and the mixture. In the layerwise melt pool solidification process, wide partially melted zone is observed to favor the formation of equiaxed grains due to its inheritance characteristic. After solution and aging heat treatment, Al-Cu-Cd nanoclusters (∼ 2 nm in Dia.) are generated, which facilitates the formation of nanoscale stacking faulted zones (∼11 nm). Meanwhile, as heterogeneous nucleation sites, Al-Cu-Cd nanoclusters effectively promote θ'-Al2Cu phase (∼ 19 - 114 nm) precipitation to enable the coexistence with fine θ"-Al3Cu precipitates (∼ 7 - 37 nm). The multiscale microstructure, especially incorporating the newly identified nanoscale stacking faulted zones, activates multiple strengthening mechanisms. Consequently, its tensile properties at room temperature reach an ultimate tensile stress of ∼ 496.5 MPa, yield strength of ∼ 435.7 MPa, and elongation of ∼ 9.6%, which are superior to other as-cast and additively manufactured Al-Cu alloys. This work offers a promising processing route to directly fabricate high-strength near-net-shape Al-Cu alloy parts for the aerospace and automotive industries.

Unveiling the Borohydride Ion through Force-Field Development.

The borohydride ion, BH4-, is an essential reducing agent in many technological processes, yet its full understanding has been elusive, because of at least two significant challenges. One challenge arises from its marginal stability in aqueous solutions outside of basic pH conditions, which considerably limits the experimental thermodynamic data. The other challenge comes from its unique and atypical hydration shell, stemming from the negative excess charge on its hydrogen atoms, which complicates the accurate modeling in classical atomistic simulations. In this study, we combine experimental and computer simulation techniques to devise a classical force field for NaBH4 and deepen our understanding of its characteristics. We report the first measurement of the ion's activity coefficient and extrapolate it to neutral pH conditions. Given the difficulties in directly measuring its solvation free energies, owing to its instability, we resort to quantum chemistry calculations. This combined strategy allows us to derive a set of nonpolarizable force-field parameters for the borohydride ion for classical molecular dynamics simulations. The derived force field simultaneously captures the solvation free energy, the hydration structure, as well as the activity coefficient of NaBH4 salt across a broad concentration range. The obtained insights into the hydration shell of the BH4- ion are crucial for accurately modeling and understanding its interactions with other molecules, ions, materials, and interfaces.

Fully Automated Analysis of the Anatomic and Mechanical Axes From Pediatric Standing Lower Limb Radiographs Using Convolutional Neural Networks.

Lower limb alignment is the quantification of a set of parameters that are commonly measured radiographically to test for and track a wide range of skeletal pathologies. Determining limb alignment is a commonly performed yet laborious task in the pediatric orthopaedic setting and is therefore an interesting goal for automation. We employ a machine learning approach using convolutional neural networks (CNNs) to segment pediatric weight-bearing lower limb radiographs. The results are then used with custom Matlab code to extract anatomic landmarks and to determine lower limb alignment parameters. Measurements obtained from the automated workflow proposed here were compared with manual measurements performed by orthopaedic surgery fellows. Mechanical axis deviation was determined within a mean of 2.02mm. Lateral distal femoral angle and medial proximal tibial angle were determined with a mean deviation of 1.73 and 2.90 degrees, respectively. The calculation speed for the full set of mechanical and anatomic axis parameters was found to be ~2 seconds per radiograph. The CNN-based approach proposed in this work was shown to produce results comparable to orthopaedic surgery fellows at fast calculation speed. Although further work is needed to validate these results against radiographs and measurements from other centers, we see this as a promising start and a functional path that can be employed in further research. CNNs are a promising approach to automating commonly performed, repetitive tasks, especially those pertaining to image processing. The time savings are particularly important in clinical research applications where large sets of radiographs are routinely available and require analysis. With further development of these algorithms, we anticipate significantly improved agreement with expert-measured results and the calculation speed.

Temporal alterations in P-wave electrocardiographic metrics post patent foramen ovale closure: a retrospective study

Aims: This study aimed to analyze the electrocardiograms (ECG) of 69 patients before and after patent foramen ovale (PFO) closure, specifically investigating novel ECG parameters, over a 6-month post-procedure period. Methods: ECGs from 69 patients undergoing PFO closure were examined at three time points: before the procedure, and at 1st and 6th months post-procedure. A comprehensive set of ECG parameters, including P-wave (PW) maximum (PWmax), PW minimum (PWmin), PR interval, PW dispersion (PWdis), PW peak time in lead D2 (PWPTD2), PW peak time in lead V1(PWPTV1), P-axis, PW terminal force in the V1 (PWTF) and heart rate, were analyzed using a generalized linear mixed model (GLMM). Results: The GLMM analysis revealed significant changes in novel ECG parameters at 1-month post-procedure compared to baseline values. Parameters including PWmax (OR=8.898, 95% CI 7.521-10.275, p<0.001, PWmin (OR=6.579, 95% CI 5.611-7.548, p<0.001), PR (OR=4.159,95% CI 3.031-5.288, p<0.001), PWdis (OR=2.594, 95% CI 1.607-3.581, p<0.001), PWPTD2 (OR=4.261, 95% CI 2.928-5.593, p<0.001), PWPTV1 (OR=5.261, 95% CI 4.529-5.992, p<0.001), and PWTF (OR= 5.781, 95% CI 2.083-16.044, p<0.001) exhibited notable alterations, indicating a transient impact on cardiac conduction. However, these changes returned to baseline values by the 6-month follow-up. No statistically significant differences were observed in P-axis and heart rate across all time points. Conclusion: The analysis of ECG in patients undergoing PFO closure highlighted dynamic changes in novel ECG parameters in the early post-procedural period, with subsequent normalization by 6 months. Further research is warranted to elucidate the clinical implications of these dynamic electrocardiographic shifts and their potential association with long-term cardiovascular outcomes.

The Effect of Laser Shock Peening (LSP) on the Surface Roughness and Fatigue Behavior of Additively Manufactured Ti-6Al-4V Alloy

Laser shock peening (LSP) uses plasma shock waves to induce compressive residual stress at the surface of a component which has the potential to improve its fatigue properties. For AM parts, the existence of internal defects, surface roughness, and tensile residual stresses leads to noticeably lower fatigue strength compared to materials produced through conventional processes. Furthermore, there is a tendency for greater scatter in the fatigue behavior of these parts when compared to traditionally manufactured components. In this study, the effect of LSP on the roughness and fatigue behavior of Ti-6Al-4V alloy constructed through Laser Powder Bed Fusion (L-PBF) technique was investigated. Two types of samples were designed and tested: as-built surface air foil samples for four-point bending tests and machined surface straight gage samples for uniaxial fatigue testing. Two sets of process parameters, optimized and non-optimized, were also used for the fabrication of each sample type. It was found that LSP had negative effects on the smooth (i.e., machined) surface samples, whereas for as-built surfaces the roughness was enhanced by decreasing the sharpness of the deep valleys and partially remelting the loosely bonded particles on the peaks. It was found that the scatter of the fatigue data decreased for optimized machined samples, while no clear improvement was observed in their lives. However, all non-optimized samples showed improvements in fatigue lives after the LSP process.

A new numerical approach method to solve the Lotka–Volterra predator–prey models with discrete delays

This paper proposes a new approach called Extreme Learning Machine (ELM) to solve the Lotka–Volterra predator–prey models using a novel approximation form. Unlike the traditional methods that involve constructing complex algebraic systems and performing inverse matrix operations, ELM transforms the system of differential equations into a set of nonlinear equations and solves for the parameters through optimization iterations to obtain an approximate solution. Furthermore, we construct a solution form that satisfies the initial conditions, eliminating the need to handle initial conditions during the solving process, making this method more concise. Finally, by comparing with other numerical methods using two sets of models and parameters, ELM can produce high-precision results and further demonstrate the advantages of our method.

Logistic elliptic equation with a nonlinear boundary condition arising from coastal fishery harvesting II

We study positive solutions of a logistic elliptic equation with a nonlinear boundary condition that models coastal fishery harvesting ([18]). An essential role is played by the smallest eigenvalue of the Dirichlet eigenvalue problem, in terms of which, a noncritical case is studied in [32]. In this paper, we extend our analysis to the critical case. We also further study the noncritical case for a more precise description of the positive solution set, including uniqueness and stability analysis for large parameters. Our approach relies on an energy method, sub- and supersolutions, and implicit function analysis.

Anand constitutive modeling of multilayer Silver-Tin transient liquid phase foils using tensile and creep testing

Purpose This paper aims to present two Anand’s model parameter sets for the multilayer silver–tin (AgSn) transient liquid phase (TLP) foils. Design/methodology/approach The AgSn TLP test samples are manufactured using pre-defined optimized TLP bonding process parameters. Consequently, tensile and creep tests are conducted at various loading temperatures to generate stress–strain and creep data to accurately determine the elastic properties and two sets of Anand model creep coefficients. The resultant tensile- and creep-based constitutive models are subsequently used in extensive finite element simulations to precisely survey the mechanical response of the AgSn TLP bonds in power electronics due to different thermal loads. Findings The response of both models is thoroughly addressed in terms of stress–strain relationships, inelastic strain energy densities and equivalent plastic strains. The simulation results revealed that the testing conditions and parameters can significantly influence the values of the fitted Anand coefficients and consequently affect the resultant FEA-computed mechanical response of the TLP bonds. Therefore, this paper suggests that extreme care has to be taken when planning experiments for the estimation of creep parameters of the AgSn TLP joints. Originality/value In literature, there is no constitutive modeling data on the AgSn TLP bonds.

Divergence Reduction in Monte Carlo Neutron Transport with On-GPU Asynchronous Scheduling

While Monte Carlo Neutron Transport (MCNT) is near-embarrasingly parallel, the effectively unpredictable lifetime of neutrons can lead to divergence when MCNT is evaluated on GPUs. Divergence is the phenomenon of adjacent threads in a warp executing different control flow paths; on GPUS, it reduces performance because each work group may only execute one path at a time. The process of Thread Data Remapping (TDR) resolves these discrepancies by moving data across hardware such that data in the same warp will be processed through similar paths. A common issue among prior implementations of TDR is the synchronous nature of its remapping and processing cycles, which exhaustively sort data produced by prior processing passes and exhaustively evaluate the sorted data. In another work, we defined a method of remapping data through an asynchronous scheduler which allows for work to be stored in shared memory and deferred arbitrarily until that work is a viable option for low-divergence evaluation. This article surveys a wider set of cases, with the goal of characterizing performance trends across a more comprehensive set of parameters. These parameters include cross sections of scattering/capturing/fission, use of implicit capture, source neutron counts, simulation time spans, and tuned memory allocations. Across these cases, we have recorded minimum and average execution times, as well as a heuristically tuned near-optimal memory allocation size for both synchronous and asynchronous scheduling. Across the collected data, it is shown that the asynchronous method is faster and more memory efficient in the majority of cases, and that it requires less tuning to achieve competitive performance.

Carbon volatility connectedness and the role of external uncertainties: Evidence from China

This paper investigates the volatility connectedness between China’s carbon pilot markets. Using Diebold and Yilmaz (2014)’s approach based on the time-varying parameter vector autoregression model with a variety of parameter sets, we obtain the average across 40 results to capture the volatility connectedness between the markets. We further use the linear and nonlinear autoregressive distributed lag models to assess the role of external uncertainties in shaping volatility connectedness. Several findings emerge: (1) Guangdong (Chongqing) is the largest net transmitter (receiver) in terms of volatility connectedness; (2) Volatility connectedness shows a declining trend, with its cycle fluctuations caused by compliance-driven trading; (3) Volatility connectedness correlates negatively with external uncertainties. Both economic policy and climate policy indices have impacts on volatility connectedness. We recommend introducing market makers to enhance market liquidity and reduce risk spreading. We also highlight the need for further research to pinpoint idiosyncratic factors that affect different markets.

Mueller-Polarimetry of Barley Leaves II: Mueller Matrix Decompositions

This paper highlights the application of decomposition methods in Mueller polarimetry for the discrimination of three groups of barley leaf samples from Hordeum vulgare: Chlorina mutant, Chlorina etiolated mutant and Cesaer varieties in the visible wavelength at λ = 632.8 nm. To obtain the anisotropic and depolarizing properties of the samples under study, the additive and multiplicative decompositions of experimental Mueller matrices were used. We show how a rich set of anisotropy and depolarization parameters obtained from decompositions can be used as effective observables for the discrimination between different varieties of the same plant species.

Multi-objective optimization of a pipe energy pile with heat exchanger pipes subjected to chaotic advection

Shallow geothermal energy extraction through an energy pile system relies on the constant temperature available below the earth's surface at shallow depths. The energy pile system transfers the heat stored in the earth to a place of utility for space heating and cooling through fluid circulation, thus extracting geothermal energy. In this study, a 3D energy pile model comprising a helical heat exchanger pipe (HHEP) was developed and validated with the existing experimental data using a FEM-based software package. Further, it was hypothesized that replacing HHEP with a chaotic configured helical heat exchanger pipe (CHEP) would enhance thermal performance. The numerical analyses supported the hypothesis, where there was about a 15 % increase in heat release rate for energy piles containing CHEP. The above results form a basis for exploring the simulation results through laboratory scale model tests. Thus, an experimental setup was fabricated to conduct geothermal energy tests on a scaled pipe energy pile (PEP) model. The investigation was carried out by considering three design-input parameters: flow rate, pitch, and inlet temperature at three different levels with water and ethylene glycol as heat exchanger fluid (HEF) in a 4:1 ratio. The heat release rate of the PEP (Qp), heat flux per unit length of HEP (qpl), soil excess temperature (θs), total thermal resistance (TTR), and energy consumed (EC) were analyzed. Furthermore, multi-objective optimization using the MO-Jaya algorithm and multiple attribute decision-making (MADM) R-method were used to predict an optimal set of design-input parameters. Finally, a confirmation test was conducted to test the competence of the predicted parameters. The percentage variation between the predicted design input parameters and the experimental values was below 10 %.

Contrasting leaf-scale photosynthetic low-light response and its temperature dependency are key to differences in crop-scale radiation use efficiency.

Radiation use efficiency (RUE) is a key crop adaptation trait that quantifies the potential amount of aboveground biomass produced by the crop per unit of solar energy intercepted. But it is unclear why elite maize and grain sorghum hybrids differ in their RUE at the crop level. Here, we used a non-traditional top-down approach via canopy photosynthesis modelling to identify leaf-level photosynthetic traits that are key to differences in crop-level RUE. A novel photosynthetic response measurement was developed and coupled with use of a Bayesian model fitting procedure, incorporating a C4 leaf photosynthesis model, to infer cohesive sets of photosynthetic parameters by simultaneously fitting responses to CO2 , light, and temperature. Statistically significant differences between leaf photosynthetic parameters of elite maize and grain sorghum hybrids were found across a range of leaf temperatures, in particular for effects on the quantum yield of photosynthesis, but also for the maximum enzymatic activity of Rubisco and PEPc. Simulation of diurnal canopy photosynthesis predicted that the leaf-level photosynthetic low-light response and its temperature dependency are key drivers of the performance of crop-level RUE, generating testable hypotheses for further physiological analysis and bioengineering applications.

Parametric Analysis on Formability of SS304 Metal Bipolar Plate with Wave Flow Field Structure

The main production method of metal bipolar plate is stamping, and the size parameters of the flow field structure can not only change the performance of the fuel cell, but also affect the stamping quality of the metal bipolar plate. In the case of ensuring the performance of the fuel cell, it is necessary to study the stamping of the metal bipolar plate with different flow field structures. In the past, there were few studies on the wave flow field stamping forming. This paper analyzed the wave flow field, formulated several sets of bipolar plate structural dimension parameters, and evaluated the stamping forming quality of the reaction zone of SS304 metal bipolar plate through finite element simulation. By reducing the rib height ΔH=0.075mm in the corner part, the maximum thinning rate can be reduced from 34.833% to 24.689% and the thickness can reach 0.0753mm when the flow channel width S=1.0mm, the rib width W=1.5mm and the forming depth H=0.5mm. It provides some guidance for optimizing the structural dimension parameters of metal bipolar plates.

Optimization of Tooth Profile Modification Amount and Manufacturing Tolerance Allocation for Rotate Vector Reducer Under Reliability Constraint

Abstract Rotate vector (RV) reducer is mainly applied in high-precision transmission. Its manufacturing errors strongly influence transmission accuracy. In order to select the optimal gear profile parameters and manufacturing accuracy to meet the transmission conditions in actual machining, a model of gear tooth contact analysis of involute and cycloid transmission considering tooth profile modification and manufacturing errors was established according to the principle of coordinate transformation and conjugate contact of gear. The sensitivity of different manufacturing errors to transmission errors was analyzed. At the same time, the optimization method of gear tooth profile modification parameters and manufacturing accuracy configuration under transmission reliability constraints was designed. The results showed that based on the optimization calculation of samples by the DIRECT algorithm, it was possible to obtain a set of RV reducer modification parameters and manufacturing tolerance bands with the lowest manufacturing cost under the condition of transmission accuracy. The selected parameters had high-reliability characteristics. Therefore, this method provides a theoretical optimization reference for manufacturing high-reliability RV reducer.

Uniaxial ratcheting and ductile damage in structural steel with a stochastic spreading of experimental data

AbstractThis paper provides new experimental data regarding inelastic strain accumulation in structural steel samples. The samples are subjected to stress‐controlled cyclic loading with positive mean stress within each cycle; the loading blocks are combined in a complex pattern to study the effect of load history. A large‐strain ratcheting model is constructed based on a material law with a nonlinear kinematic and isotropic hardening. A new rule of ductile damage accumulation is suggested and a class of models with hardening stagnation is introduced. We show that models which include the hardening stagnation describe the experimental data more accurately. Unfortunately, due to a large spread of mechanical properties from sample to sample, it is impossible to describe the entire set of experiments using a single set of parameters. To address this challenge, we have developed novel mathematical tools for parameter identification in the context of stochastic variations in mechanical properties. These tools include the concept of a collar of model responses and the notion of protrusion. We propose an extended parameter identification problem that rethinks the standard error functional. We demonstrate that the extended identification problem effectively accounts for the large spread observed in experimental data when applied to the developed ratcheting model.

Multi-layered physical factors govern mercury release from soil: Implications for predicting the environmental fate of mercury

Despite the recognised risks of human exposure to mercury (Hg), the drivers of gaseous elemental mercury (GEM) emissions from the soil remain understudied. In this study, we aimed to identify the environmental parameters that affect the GEM flux from soil and derive the correlations between environmental parameters and GEM flux. Principal component analysis (PCA), factor analysis (FA), and structural equation modelling (SEM) were performed on samples from forest and non-forest sites. The associated results revealed the impact of each environmental parameter on GEM flux, either due to the interaction between the parameters or as a coherent set of parameters. An introductory correlation matrix examining the relationship between two components showed a negative correlation between GEM flux and atmospheric pressure at the two sites, as well as strong correlations between atmospheric pressure and soil temperature. In cases of non-forest open sites with no trees, the PCA and FA results were consistent, indicating that atmospheric pressure, solar irradiance, and soil moisture—defined as primary causality—are largely independent drivers of GEM flux. In contrast, the PCA and FA results for the forest areas with high humidity, tree coverage, and shade were inconsistent, confirming the hypothesis that primary causality affects GEM flux rather than consequent parameters driven by primary causality, such as air and soil temperature and atmospheric humidity. The SEM results provided further evidence for primary and consequent causality as crucial drivers of the GEM flux. This study demonstrates the importance of key primary parameters, such as atmospheric pressure, solar irradiance, and soil moisture content, that can be used to predict mercury release from soils, as well as the importance of consequent parameters, such as air and soil temperature and atmospheric humidity. Monitoring the magnitude of these environmental parameters alone may facilitate the estimation of mercury release from soils and be useful for detailed modelling of soil–air Hg exchange.

Lattice aggregations of boxes and symmetric functions

We introduce two lattice growth models: aggregation of l-dimensional boxes and aggregation of partitions with l parts. We describe properties of the models: the parameter set of aggregations, the moments of the random variable of the number of growth directions, asymptotical behavior of proportions of the most frequent transitions of two- and three-dimensional self-aggregations.