The sanitary sewer unit hydrograph model: A comprehensive tool for wastewater flow modeling and inflow-infiltration simulations
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Pulp is the most important raw material in paper in paper industries, whose Fiber Length Stochastic Distribution (FLSD) shaping directly determines the energy consumption and paper quality of the subsequent papermaking processes. However, the mean and variance are insufficient to describe the output FLSD shaping, which display non-Gaussian distribution properties. Therefore, the traditional control, method based on the mean and variance of fiber length is difficult to control the FLSD shaping effectively. In this paper, a novel data-driven predictive probability density function (PDF) control method is proposed for output FLSD shaping in refining process. Primarily, in order to improve the approximation accuracy of the PDF employing RBF neural network, the parameters (center value and width) of each RBF basis functions are tuned via utilizing iterative learning control (ILC) law, and the corresponding estimations of weights law can be obtained. Secondly, considering that the conventional linear model of weights vector has drawbacks of low accuracy and weak generalization ability, random vector functional link networks (RVFLNs) based data driven nonlinear modelling method is employed to characterize the prediction model between the input variables and weights vector. Finally, in order to reduce the randomness of the output FLSD, a minimum entropy control method under mean constraint is employed to design the predictive PDF controller for the output FLSD shaping with the help of the established nonlinear stochastic distribution model. Both simulations and experiments are given to illustrate the effectiveness and practicability of the proposed method.
Abstract The present study aimed to determine the allometric scaling among a selection of morphological traits in European sea bass ( Dicentrarchus labrax ) to estimate fish body weight. A set of morphological traits (fish body weight, length, height, and width) were directly measured in 146 fish of a recirculating aquaculture system, with body weights ranging from 17.11 to 652.21 g. In addition, a collection of digital imagery of each anesthetized fish from the side and top views were used to estimate other traits (indirect measures). Multiple regression analysis and regression coefficients were calculated using all possible combinations of biometric data (predictors) to estimate fish body weight, applying different numerical fitting models (linear, log‐linear, quadratic, exponential). The results showed that the best combination of traits for estimating fish body weight were fish body width, length and height, collected from direct measure ( R 2 = 0.995), for a log‐linear model fitting, which revealed more accurate determinations than the most commonly used length–weight relationship. Nevertheless, other combinations of morphological traits and fitting models were also found to be suitable in successfully predict fish body weight, with variability ranging between 92.5% and 98.5%. For indirect measures, the best predictor was a combination of traits from top view (width, eye distance and area without fins) fitted with a log‐linear function. These results comprise a relevant baseline in supporting the high potential of noninvasive methods to accurately follow the growth of European sea bass juveniles, recurring to imagery analysis of anesthetized fish. It has major potential applications in feeding consumption trials and fish growth models, as it allows for continuously following up fish growth under different experimental conditions without therein distress derived from manipulation.
We have developed new measures of errors for numerical shock. The new approach is based on analysis of the structure function, and separation of the errors related to oscillations and shock width, which also include error in the position of the ”center” of the numerical shock. We have demonstrated that those measures correctly characterize the numerical solution. We introduced an objective function in, which both types of errors are weighted, and presented optimal values of the coefficients of the linear and quadratic viscosity for different weights and different Mach numbers.
Abstract With the onset and rapid growth of smart manufacturing, there is a constant increase in the demand for automation technologies to enhance productivity while providing uninterrupted, cost‐efficient, and resilient machining. Traditional manufacturing systems, however, suffer from several losses due to machine faults and degradation. Specifically, tool wear directly impacts the precision and quality of the milled parts, which causes an increase in the scrap production. Hence, more attempts are required to meet the desired quota of successful parts, which in turn results in wasted material, longer delays, further tool degradation, and higher energy, machining, and labor costs. As such, this paper develops a multi‐objective optimization framework to generate the optimal control set points (e.g., feed rate and width of cut) that minimize the total cost of machining operations resulting from multiple contradictory cost functions (e.g., material, energy, tardiness, machining, labor, and tool) in the presence of tool wear. Notably, we estimate the total expected cost in dollars, which provides automatic and intuitive weighting in this multi‐objective formulation. The optimization framework is tested on a high‐fidelity face milling model that has been validated on real data from industry. Results show significant dollar savings of up to as compared to the default control scheme.
In this paper, we propose a set of simple benchmarks for the evaluation of data-based models for real-time streamflow forecasting, such as those developed with sophisticated Artificial Intelligence (AI) algorithms. The benchmarks are also data-based and provide context to judge incremental improvements in the performance metrics from the more complicated approaches. The benchmarks include temporal and spatial persistence, persistence corrected for baseflow and streamflow, as well as river distance weighted runoff obtained from space-time distributed rainfall. In the development of the benchmarks, we use basic hydrologic insights such as flow aggregation by the river network, scale-dependence in basin response, streamflow partitioning into quick flow and baseflow, water travel time, and rainfall averaging by the basin width function. The study uses 140 streamflow gauges in Iowa that cover a range of basin scales between 7 and 37,000 km 2 . The data cover 17 years. This work demonstrates that the proposed benchmarks can provide good performance according to several commonly used metrics. For example, streamflow forecasting at half of the test locations across years achieves a Kling-Gupta Efficiency (KGE) score of 0.6 or higher at one-day ahead lead time, and 20% of cases reach the KGE of 0.8 or higher. The proposed benchmarks are easy to implement and should prove useful for developers of data-based as well as physics-based hydrologic models and real-time data assimilation techniques.
In time-delay cosmography, three of the key ingredients are (1) determining the velocity dispersion of the lensing galaxy, (2) identifying galaxies and groups along the line of sight with sufficient proximity and mass to be included in the mass model, and (3) estimating the external convergence κ_ext from less massive structures that are not included in the mass model. We present results on all three of these ingredients for two time-delay lensed quad quasar systems, DES J0408–5354 and WGD 2038–4008 . We use the Gemini, Magellan, and VLT telescopes to obtain spectra to both measure the stellar velocity dispersions of the main lensing galaxies and to identify the line-of-sight galaxies in these systems. Next, we identify 10 groups in DES J0408–5354 and two groups in WGD 2038–4008 using a group-finding algorithm. We then identify the most significant galaxy and galaxy-group perturbers using the ‘flexion shift’ criterion. We determine the probability distribution function of the external convergence κ_ext for both of these systems based on our spectroscopy and on the DES-only multiband wide-field observations. Using weighted galaxy counts, calibrated based on the Millennium Simulation, we find that DES J0408–5354 is located in a significantly underdense environment, leading to a tight (width |$\sim 3{{\ \rm per\ cent}}$|), negative-value κ_ext distribution. On the other hand, WGD 2038–4008 is located in an environment of close to unit density, and its low source redshift results in a much tighter κ_ext of |$\sim 1{{\ \rm per\ cent}}$|, as long as no external shear constraints are imposed.
This work describes two methods to fit the inelastic neutron-scattering spectrum S(q, ω) with wavevector q and frequency ω. The common and well-established method extracts the experimental spin-wave branches ω n (q) from the measured spectra S(q, ω) and then minimizes the difference between the observed and predicted frequencies. When n branches of frequencies are predicted but the measured frequencies overlap to produce only m < n branches, the weighted average of the predicted frequencies must be compared to the observed frequencies. A penalty is then exacted when the width of the predicted frequencies exceeds the width of the observed frequencies. The second method directly compares the measured and predicted intensities S(q, ω) over a grid {q i , ω j } in wavevector and frequency space. After subtracting background noise from the observed intensities, the theoretical intensities are scaled by a simple wavevector-dependent function that reflects the instrumental resolution. Furthermore, the advantages and disadvantages of each approach are demonstrated by studying the open honeycomb material Tb 2 Ir 3 Ga 9 .
Exciton-polaritons are quasi-particles formed by the quantum mechanical hybridization of electronic and photonic excitations. Despite extensive investigations, a fundamental understanding of molecular polariton spectra and the polariton delocalization from an ab initio theoretical perspective remains elusive. We simulate experimentally measured linear transmission spectroscopy of many Zinc(II) tetraphenylporphyrin (ZnTPP) molecules collectively coupled to a cavity from first principles. Our theoretical approach incorporates many low-lying electronic excitations in ZnTPP molecules, as well as collective light-matter couplings between ZnTPP and the quantized radiation modes, both of which are shown to be the key to accurately recovering the experimental spectra. We further analyzed to what extent the polariton and dark states are delocalized over many molecules, for the first time, using fully ab initio descriptions of the molecules. We finally investigate the line width as a function of detuning, providing new theoretical insights into the experimentally observed motional narrowing behavior. Our work presents first-ofits- kind theoretical studies on molecular polariton spectra, offering a new perspective on molecular polariton formation in realistic ab initio molecular systems whose rich, many-state nature provides spectral features enabled by the high density of electronic states beyond simple quantum optics models.
We propose a method to convert the channeling Rutherford backscattering spectrum yield map of a single crystal to a polycrystal through a matrix rotation technique. The rotation matrix is determined by the deviation of the crystal axial direction from the original z axis. The final yield map is created after averaging the rotated yields using the texture function as the weight factor. For highly oriented pyrolytic graphite (HOPG) exhibiting mosaic spread, the method leads to a Gaussian kernel averaging of the map obtained from a single crystal. The yield map of a single crystal is obtained by a simulation of ion trajectories in a potential field described by Moliere screened Coulomb potentials. Yield maps are calculated under various σ values (standard deviations of mosaic spread). The simulated results are compared with experimental results obtained using 1.2 MeV alpha particle. σ is extracted through the best fitting, demonstrating that the method can be used to obtain texture details. The effects of mosaic spread on minimum yield χ min and the half-width at half maximum of angular scans ψ ½ are systematically modeled and compared with previous theoretical equations. The study also shows that previous theoretical equations are valid only at small σ values. The proposed method can be applied to any type of polycrystal and is not limited to HOPG. It provides near-surface mosaic spread and crystallography information with a longitudinal depth resolution of tens of nanometers and is not influenced by grain shapes.
The optimization of ionic conductivity and lithium-ion battery stability can be achieved by independently tuning the ion transport and mechanical robustness of block polymer (BP) electrolytes. However, the ionic conductivity of BP electrolytes is inherently limited by the covalent attachment of the ionically conductive block to the mechanically robust block, among other factors. Herein, the BP electrolyte polystyrene-block-poly(oligo-oxyethylene methacrylate) [PS-b-POEM] was blended with POEM homopolymers of varying molecular weights. The incorporation of a higher molecular weight homopolymer additive (α > 1 state) promoted a “dry brush-like” homopolymer distribution within the BP self-assembly and led to higher lithium salt concentrations in the more mobile homopolymer-rich region, increasing overall ionic conductivity relative to the “wet brush-like” (α < 1 state) and unblended composites, where α is the molecular weight ratio between the POEM homopolymer and the POEM block in the copolymer. Here, neutron and X-ray reflectometry (NR and XRR, respectively) provided additional details on the lithium salt and polymer distributions. From XRR, the α > 1 blends showed increased interfacial widths in comparison to their BP (unblended) or α < 1 counterparts because of the more central distribution of the homopolymer. This result, paired with NR data that suggested even salt concentrations across the POEM domains, implied that there was a higher salt concentration in the homopolymer POEM-rich regions in the dry brush blend than in the wet brush blend. Furthermore, using 7 Li solid-state nuclear magnetic resonance spectroscopy, we found a temperature corresponding to a transition in lithium mobility (T Li mobility ) that was a function of blend type. T Li mobility was found to be 39 °C above T g in all cases. Interestingly, the ionic conductivity of the blended BPs was highest in the α > 1 composites, even though these composites had higher T g s than the α < 1 composites, demonstrating that homopolymer-rich conducting pathways formed in the α > 1 assemblies had a larger influence on conductivity than the greater lithium ion mobility in the α < 1 blends.
We examine the UV/X-ray properties of 1378 quasars in order to link empirical correlations to theoretical models of the physical mechanisms dominating quasars as a function of mass and accretion rate. The clarity of these correlations is improved when (1) using C IV broad emission line equivalent width (EQW) and blueshift (relative to systemic) values calculated from high signal-to-noise ratio reconstructions of optical/UV spectra and (2) removing quasars expected to be absorbed based on their UV/X-ray spectral slopes. In addition to using the traditional C IV parameter space measures of C IV EQW and blueshift, we define a "C IV ∥ distance" along a best-fit polynomial curve that incorporates information from both C IV parameters. We find that the C IV ∥ distance is linearly correlated with both the optical-to-X-ray slope, α ox , and broad-line He II EQW, which are known spectral energy distribution indicators, but does not require X-ray or high spectral resolution UV observations to compute. The C IV ∥ distance may be a better indicator of the mass-weighted accretion rate, parameterized by L/L Edd , than the C iv EQW or blueshift alone, as those relationships are known to break down at the extrema. Conversely, there is only a weak correlation with the X-ray energy index (Γ), an alternate L/L Edd indicator. We find no X-ray or optical trends in the direction perpendicular to the C IV distance that could be used to reveal differences in accretion disk, wind, or corona structure that could be widening the C IV EQW–blueshift distribution. A different parameter (such as metallicity) not traced by these data must come into play.
Plant RNA virus–mediated guide RNA (gRNA) delivery represents a transformative advance in genome editing technologies. Unlike conventional transformation methods that rely on labor-intensive tissue culture and regeneration for each individual gRNA delivery, viral vectors can rapidly and systemically transmit gRNAs into pre-established Cas-expressing plants, providing an accelerated route for functional genomics and trait discovery directly in planta . However, key design parameters, including subgenomic promoter choice, transcript architecture, and their effects on viral fitness and editing outcomes, remain to be elucidated for most viral platforms. We developed five Barley stripe mosaic virus (BSMV) vectors, each with distinct subgenomic promoter elements to drive single gRNA expression. These were initially evaluated in Cas9-expressing transgenic Nicotiana benthamiana plants targeting the Phytoene desaturase ( PDS ) gene to compare their editing efficiencies. Single gRNAs expressed under the duplicated γb subgenomic promoter or when fused directly to the γb genome achieved the highest mutation frequencies (up to 90% at 60 days post-inoculation), whereas β1- and β2-driven sgRNAs produced delayed and reduced editing. Thus, promoter selection critically determines gRNA accumulation and the efficacy of BSMV-mediated genome editing. The top-performing design was then applied to Cas9-expressing barley ( Hordeum vulgare ) targeting HvCMF7 (conferring green-white variegation) and HvGW2.1 (impacts grain width and weight). BSMV spread systemically throughout barley, inducing somatic and heritable mutations at frequencies up to 100%, with virus-free edited progeny. In contrast, despite robust somatic editing in N. benthamiana, no heritable mutations were detected indicating species-dependent limitations in germline transmission. Our systematic comparison of subgenomic promoter architectures establishes clear design principles for optimizing viral vector–mediated delivery. Promoter choice and transcript structure critically shape editing efficiency and viral stability. The host-specific boundary for germline editing, defined by efficient heritable editing in barley but not N. benthamiana , highlights where BSMV offers advantages and where alternative vectors or hybrid strategies are required, guiding rational platform selection for diverse crop species and applications. Collectively, these findings establish BSMV as a promising next-generation vector for rapid, tissue culture–free, and transformation-independent genome editing in cereals and other recalcitrant monocots.
The recovery and separation of rare earth elements (REEs) are of national importance owing to the specific usages, high demand, and low supply of these elements. In this research, we have investigated the adsorption of rare earth elements onto DNA-functionalized mesoporous carbons with a BET surface area of 605 m2/g and a median mesopore width of 48 Å. Additionally, three types of single-stranded DNA, one with 100 base units of thymine, another with 20 units of thymine, and the third, a 2000 unit long DNA from salmon milt were grafted on the carboxylated mesoporous carbon surface. All of the DNA-functionalized mesoporous carbons demonstrated higher adsorption of REEs compared to pristine mesoporous carbon and DNA grafted with 100 units of thymine demonstrated slightly higher adsorbed amounts compared to others. Pure neodymium (Nd(III)) adsorption in the aqueous phase demonstrated an adsorbed amount of 110.4 mg/g with respect to the initial concentration of 500 mg/g. A pH variation study with pure Nd(III) demonstrated that the adsorbed amount is higher at elevated pH compared to that at lower pH, thereby suggesting possible recovery at lower pH. Adsorption of a mixture of 16 REEs, including Sc, Lu, Tm, Yb, Er, Ho, Tb, Dy, Y, Eu, Gd, Sm, Ce, Nd, Pr, and La revealed that the adsorbed amount increased with an increase in the atomic weight and metallic radii of elements within the lanthanides. The calculation of the distribution coefficients for all of the equilibrium adsorption amounts suggested that adsorption is more effective in the lower concentration region. The Nd L3-edge X-ray absorption near edge structure (XANES) confirmed a 3+ oxidation state of Nd in the adsorbed phase. The extended X-ray absorption fine structure (EXAFS) confirmed the local atomic structure relaxation of Nd complexes in the adsorbed phase and shortening of the Nd–O bond distance by about 0.03–0.04 Å, which may be associated with their local complexation at the carbon surface.
We examine the evolution of the phase diagram of the low-density intergalactic medium during the Epoch of Reionization in simulation boxes with varying reionization histories from the Cosmic Reionization on Computers project. The probability density function (PDF) of gas temperature at fixed density exhibits two clear modes: a warm and a cold temperature mode, corresponding to the gas inside and outside of ionized bubbles. We find that the transition between the two modes is ‘universal’ in the sense that its timing is accurately parametrized by the value of the volume-weighted neutral fraction for any reionization history. This ‘universality’ is more complex than just a reflection of the fact that ionized gas is warm and neutral gas is cold: it holds for the transition at a fixed value of gas density, and gas at different densities transitions from the cold to the warm mode at different values of the neutral fraction, reflecting a non-trivial relationship between the ionization history and the evolving gas density PDF. Furthermore, the ‘emergence’ of the tight temperature–density relation in the warm mode is also approximately ‘universally’ controlled by the volume-weighted neutral fraction for any reionization history. In particular, the ‘emergence’ of the temperature–density relation (as quantified by the rapid decrease in its width) occurs when the neutral fraction is 10 –4 ≲ X HI ≲ 10 –3 for any reionization history. Our results indicate that the neutral fraction is a primary quantity controlling the various properties of the temperature–density relation, regardless of reionization history.
Block copolymer (BCP) directed self-assembly (DSA) is a promising route to enhance lithography resolution by multiplying nanopattern density and reducing feature roughness. Eliminating kinetically trapped self-assembly defects requires fast self-assembly. However, acceleration strategies like solvent vapor annealing or homopolymer blending broaden domain interfaces, implying a trade-off in increased feature roughness. In this work, we experimentally investigate this apparent dilemma between self-assembly kinetics and line roughness for solvent vapor-annealed thin films of a lamellar poly(styrene-block-2-vinylpyridine) (PS-b-P2VP) BCP blended with PS and P2VP homopolymers. Binary blends with PS or P2VP homopolymers and ternary blends incorporating both in equal weight fractions were solvent vapor annealed using acetone, a near-neutral solvent for PS and P2VP, followed by P2VP-selective vapor-phase infiltration with alumina (AlOx) and polymer etching. Binary blends with P2VP exhibit a modest kinetic enhancement but also higher line-edge and -width roughness due to the increased frequency of P2VP protrusions and bridge defects in the alumina line patterns. In contrast, binary blends with PS self-assemble noticeably faster, while domain asymmetry from the added PS homopolymer reduces roughness by curbing the number of alumina protrusions and bridge defects. Ternary blends maintain DSA line patterns across a wider composition window and, at higher homopolymer loadings, reduce roughness at length scales near the lamellar period, consistent with a reduced impact of intradomain compositional fluctuations. These findings provide important insights for codesigning blend compositions and process flows to achieve high-resolution, defect free patterns with minimal roughness through BCP DSA.