Rough Estimate Research Articles

Solar wind effect on the multi-fluid plasma expansion in the Venusian upper ionosphere

Inspired by the observations suggesting that at altitudes of about 1000 km the interaction between solar wind streams and Venus’ ionosphere plasma leads to ions acceleration and outflow, the influence of different solar wind physical parameters, such as densities, temperatures and initial streaming velocities, has been studied. The ionosphere plasma system consists of two positive ion populations O+, H+ and electrons along with the solar wind streaming protons and electrons. We calculated the generated oxygen and hydrogen ions flow velocities and the electric fields. In addition, we calculated rough estimates for the escaping flux of ion populations (O+, H+) from Venus’ ionosphere and compared them to observations. To a large extent, we found that the estimates match. We also discuss the relevance of ionospheric ion acceleration and outflow from Venus’ upper.

Comparative analysis of the behaviour of the hot water production system of an indoor swimming pool

In this paper, we analyse the thermal behaviour and the environmental impact of an indoor swimming pool placed in Pozo Estrecho, Cartagena, in the Southeast of Spain. The existing installation is formed by a group of solar thermal collectors supported by two propane heaters which heat the water of the pool and satisfy the needs of hot water in the changing room of the pool. Here we propose an installation based on the use of a heat pump supported by solar thermal collectors. In this work, we first describe the installations. They are modelled with Trnsys and the models employed briefly described. Later, we compare their primary energy consumption and its environmental impact in terms of their CO2 emissions. A rough estimate of its economic impact is also given. Some conclusions are finally drawn.

Improving texture integrity through second-order constraints on warping maps

Pose Transfer has recently gained significant attention, particularly for its user-friendly applications in the animation industry. The primary objective is to transform a given RGB image into a new target pose. This process involves two consecutive tasks: initially, warping the image to approximately align with the target pose, and subsequently using this rough estimation to generate a photorealistic image of the input in the desired pose.The primary challenge lies in the first task, where the image undergoes a rough transformation to its new location in the target pose. Current deep learning approaches rely on first-order warping, employing an affine transformation to move all image pixels. Despite yielding promising results, this approach has significant challenges when dealing with complex deformations, mainly due to the simplistic nature of its linear function. In contrast, we suggest transferring patches using a set of correlation layers. In each layer, the warping for each pixel of the image is individually estimated. We additionally introduce a constraint aimed at minimizing the energy of second derivatives across the entire warping map of the pixels. This allows for keeping the integration of local textures following the warping process, a feature already ensured in the affine-based transformation by restricting the transition to a linear function for all the image pixels. Our approach not only preserves the integrity of local textures, akin to the affine transformation, but achieves this by individually estimating the warping for each image pixel, thereby enabling finer adjustments of the input sample to the target pose. We illustrate the superior performance of this technique compared to affine-based strategies on the renowned DeepFashion database.

Rice Counting and Localization in Unmanned Aerial Vehicle Imagery Using Enhanced Feature Fusion

In rice cultivation and breeding, obtaining accurate information on the quantity and spatial distribution of rice plants is crucial. However, traditional field sampling methods can only provide rough estimates of the plant count and fail to capture precise plant locations. To address these problems, this paper proposes P2PNet-EFF for the counting and localization of rice plants. Firstly, through the introduction of the enhanced feature fusion (EFF), the model improves its ability to integrate deep semantic information while preserving shallow spatial details. This allows the model to holistically analyze the morphology of plants rather than focusing solely on their central points, substantially reducing errors caused by leaf overlap. Secondly, by integrating efficient multi-scale attention (EMA) into the backbone, the model enhances its feature extraction capabilities and suppresses interference from similar backgrounds. Finally, to evaluate the effectiveness of the P2PNet-EFF method, we introduce the URCAL dataset for rice counting and localization, gathered using UAV. This dataset consists of 365 high-resolution images and 173,352 point annotations. Experimental results on the URCAL demonstrate that the proposed method achieves a 34.87% reduction in MAE and a 28.19% reduction in RMSE compared to the original P2PNet while increasing R2 by 3.03%. Furthermore, we conducted extensive experiments on three frequently used plant counting datasets. The results demonstrate the excellent performance of the proposed method.

Optical MTF and aberration predictions with the Helmholtz-Lagrange invariant

The paper presents a method for a rough estimation of the aberrations and of the MTF of an optical system by means of the Helmholtz-Lagrange invariant. The method allows for a rough understanding of an optical design and can find application in those cases where approximated performance predictions are needed, for example, before running complex and time-consuming analyses. The simple approach presented here is a rough approximation and should always be followed by the usual tolerance analyses.

Measuring soundscape quality of urban environments using physiological indicators: Construction of physiological assessment dimensions and comparison with subjective dimensions

Residents and designers typically pursue healthy urban acoustic environments. The soundscape community has established internationally recommended standards for subjective questionnaires to evaluate soundscape quality; however, much work is still needed to standardise physiological assessments. This study measures the physiological responses and subjective evaluations of participants in 20 common sound sources (scenarios) in urban public open spaces, and discusses the potential dimensions when assessing the quality of urban soundscapes through physiological indicators. Categorical principal components analysis are employed to reduce the dimensionality of the physiological data. The results indicate that physiological Dimension 1 represents the Natural–Artificial dimension, and physiological Dimension 2 potentially represents the Regular–Irregular dimension, providing a rough estimate of regularity or predictability in soundscapes. Subjective data are used as a control for physiological data, subjective Dimension 1 can be summarised as the Calm (Pleasant)–Chaotic (Annoying) dimension, whereas subjective Dimension 2 can be summarised as Familiar (Congruent)–Unfamiliar (Incongruent). Through comparing and discussing the distributions in physiological and subjective models under different sound sources, it is found that both physiologically and subjectively, the foremost dimension (Dimension 1) represents comfort and pleasure, and natural sounds can be distinctly differentiated from noise through this dimension. Although its physiological significance is not clear, the two-dimensional physiological indicator model reveals distinctions between sound sources of various categories. These findings have implications for the planning and design of liveable and comfortable urban environments from the perspective of physiological indicators.

Satellite and UAV derived seasonal vegetative roughness estimation for flood analysis

Satellite and UAV derived seasonal vegetative roughness estimation for flood analysis

Research on Permeability Characteristics and Gradation of Rockfill Material Based on Machine Learning

The density of rockfill material is an important index to evaluate the quality of rockfill dams. It is of great significance to accurately obtain the densities and permeability coefficients of rockfill material dams quickly and accurately by scientific means. However, it takes a long time to measure the permeability coefficient of rockfill material in practice, which means that such measurements cannot fully reflect all the relevant properties. In this paper, using a convolutional neural network (CNN), a machine learning model was established to predict the permeability coefficient of rockfill material with the full scale (d10~d100), pore ratio, Cu, and Cc as the inputs and the permeability coefficient as the output. Through collecting the permeability coefficient and related data in the literature, the set samples were sorted for model training. The prediction results of the trained CNN model are compared with those of the back propagation (BP) model to verify the accuracy of the CNN model. Laboratory constant head penetration experiments were designed to verify the generalization performance of the model. The results show that compared with the BP model, the CNN model has better applicability to the prediction of the permeability coefficient of rockfill material and that the CNN can obtain better accuracy and meet the requirements of the rough estimation of rockfill materials’ permeability in engineering.

A correlated multi-observable assessment for vanadium redox flow battery state of charge estimation — Empirical correlations and temperature dependencies

The efficient operation of vanadium redox flow batteries requires the half cell specific monitoring of the state of charge (SOC). Monitoring of the SOC is affected by temperature fluctuations, which need to be distinguished from the SOC signal. Several SOC monitoring approaches have been proposed in the literature and either been evaluated individually or compared to one other approach. The aim of this study is to contribute to SOC sensor development for VRFB by providing a first correlated assessment of several established and new SOC monitoring methods. We perform multi observable measurements of: half cell electrolyte potentials, electrolyte densities, electrolyte volumes, electrolyte viscosity related pressure drops, pH related potentials and UV/Vis absorbances. We determine SOC correlations of these observables in full charge/discharge cycles at constant temperature and derive temperature corrections in additional measurements over a range of 12°C to 32°C at 25%, 50% and 75% SOC. We compare the SOC and temperature sensitivity as well as the accuracy of these monitoring methods at constant and varied temperature. We find that the established half cell electrolyte potentials and UV/Vis absorbances can estimate the SOC at constant temperature with measurement errors of 1.7% and 4%, respectively and with slightly higher errors at varying temperatures. The viscosity related pressure drop and electrolyte density are both suitable for SOC estimation if temperature corrections are included. The pH related potentials for the positive half cell and both half cell electrolyte volumes could be used for a rough SOC estimate, but need accurate temperature corrections and further long term stability evaluation.

Sufficient active control of uncertain low-frequency space micro-vibrations near measurement limit of acceleration sensors

High-precision payloads such as ultra-stable optical clocks for next-generation space missions are greatly sensitive to the low-frequency micro-vibrations induced by various flexible appendages equipped on the spacecraft. The frequencies of such micro-vibrations are uncertain and near the measurement limit of acceleration sensors, leading to insufficient vibration control performance of classical feedback control. To solve this problem, a hybrid control algorithm which combines PI controller and the filter-x least-mean-square (FxLMS) algorithm is proposed in the paper. In particular, a frequency estimation algorithm is designed for the active vibration control system to provide the reference signal. In the new system, the rough estimation of disturbance frequency is given by the adaptive notch filter. Then, the frequency is refined by Kalman filter and provided into the autoregressive model, which fine-tunes the estimated frequency and offers the reference signal for the hybrid PI-FxLMS algorithm. Finally, the experimental prototype of active vibration control system is developed and the proposed control algorithm is adopted. The experimental results demonstrate the effectiveness of the frequency estimation algorithm and further validate that the proposed hybrid PI-FxLMS algorithm can effectively suppress the low-frequency micro-vibrations near the measurement limit of acceleration sensors and with uncertain frequency.

The novel pre-rRNA detection workflow "Riboprobing" allows simple identification of undescribed RNA species.

Ribosomes translate mRNA into proteins and are essential for every living organism. In eukaryotes, both ribosomal subunits are rapidly assembled in a strict hierarchical order, starting in the nucleolus with the transcription of a common precursor ribosomal RNA (pre-rRNA). This pre-rRNA encodes three of the four mature rRNAs, which are formed by several, consecutive endonucleolytic and exonucleolytic processing steps. Historically, northern blots are used to analyze the variety of different pre-rRNA species, only allowing rough length estimations. Although this limitation can be overcome with primer extension, both approaches often use radioactivity and are time-consuming and costly. Here, we present "Riboprobing," a linker ligation-based workflow followed by reverse transcription and PCR for easy and fast detection and characterization of pre-rRNA species and their 5' as well as 3' ends. Using standard molecular biology laboratory equipment, "Riboprobing" allows reliable discrimination of pre-rRNA species not resolved by northern blot (e.g., 27SA2, 27SA3, and 27SB pre-rRNA). The method can successfully be used for the analysis of total cell extracts as well as purified pre-ribosomes for a straightforward evaluation of the impact of mutant gene versions or inhibitors. In the course of method development, we identified and characterized a hitherto undescribed aberrant pre-rRNA arising from LiCl inhibition. This pre-rRNA fragment spans from processing site A1 to E, forming a small RNP that lacks most early joining assembly factors. This finding expands our knowledge of how the cell deals with severe pre-rRNA processing defects and demonstrates the strict requirement for the 5'ETS (external transcribed spacer) for the assembly process.

Effect of different material constitutive models in estimating the residual moment capacity of RC beams subjected to natural corrosion

PurposeExisting deterministic models that predict the capacity of corroded reinforced concrete (RC) beams have limited applicability because they were based on accelerated tests that induce general corrosion. This research gap was addressed by performing a combined numerical and statistical analysis on RC beams, subjected to natural corrosion, to achieve a much better forecast.Design/methodology/approachData of 42 naturally corroded beams were collected from the literature and analyzed numerically. Four constitutive models and their combinations were considered: the elastic-semi-plastic and elastic-perfectly-plastic models for steel, and two tensile models for concrete with and without the post-cracking stresses. Meanwhile, Popovics’ model was used to describe the behavior of concrete under compression. Corrosion coefficients were developed as functions of corrosion degree and beam parameters through linear regression analysis to fit the theoretical moment capacities with test data. The performance of the coefficients derived from different combinations of constitutive laws was then compared and validated.FindingsThe results showed that the highest accuracy (R2 = 0.90) was achieved when the tensile response of concrete was modeled without the residual stresses after cracking and the steel was analyzed as an elastic-perfectly-plastic material. The proposed procedure and regression model also showed reasonable agreement with experimental data, even performing better than the current models derived from accelerated tests and traditional procedures.Originality/valueThis study presents a simple but reliable approach for quantifying the capacity of RC beams under more realistic conditions than previously reported. This method is simple and requires only a few variables to be employed. Civil engineers can use it to obtain a quick and rough estimate of the structural condition of corroding RC beams.

Consistent Analytical Second Derivatives of the Kohn-Sham DFT Energy in the Framework of the Conductor-Like Screening Model through Gaussian Charge Distributions.

The use of implicit solvation models such as the conductor-like screening model (COSMO) in quantum chemical calculations is very common, as both a rough estimate of solvation effects as well as a general tool for stabilizing ionic molecular structures. In order to generate a smooth potential energy surface as well as consistent gradients, it is necessary to apply the Gaussian charge model (GCM) for the COSMO charges. This work introduces an efficient implementation for consistent analytical second derivatives of the electronic energy with COSMO-GCM in the framework of the Kohn-Sham density functional theory. This is used to investigate the infrared spectroscopy of amino acids in aqueous solution, where the impact of pH on the molecular structure and vibrational spectra is examined. Furthermore, the structure and stability of selected all-metal aromatic cluster ions are assessed.

Research on Intelligent Computing Methods for Multistep Process Capability

In the context of the deep integration of artificial intelligence and industrial fields, precise calculation of multi-step process capability has become a current research hotspot. In most industrial fields, the estimation of multi-step process capability is mostly in the rough estimation stage, and there are relatively few precise quantitative calculation methods. This paper focuses on multi-step processes in the industrial field, using deep learning models to learn the features of each step step step by step, and then comprehensively estimating the weights between each step, ultimately achieving accurate prediction of multi-step process capabilities. This paper conducts in-depth analysis of the performance and efficiency of different models on such problems by designing a large number of validation experiments, and also provides ideas and suggestions for subsequent research in this field.

Determining stellar accretion rates from Paα and Brβ emission lines with JWST NIRSpec

In this Letter, we present the first systematic spectroscopic measurements of the near-infrared (NIR) hydrogen recombination lines Paschen alpha (Paαλ = 1.875 μm) and Brackett beta (Brβλ = 2.626 μm), produced by pre-main-sequence (PMS) stars. Such stars include T Tauri and Herbig AeBe stars, located in the massive Galactic star-forming region NGC 3603. We used measurements obtained from JWST NIRSpec, using multi-object spectroscopy (MOS) mode. Based on the existing empirical relations between Lacc and LBrγ from the literature, we used our new measurements to formulate, for the first time, an empirical relationship between the accretion luminosity, Lacc, of the stars and the line luminosities, Lline, of both Paα and Brβ. These relationships are: log10(Lacc/L⊙) = 1.42(±0.18) × log10(LPaα/L⊙ + 3.33(±0.42) and log10(Lacc/L⊙) = 1.47(±0.18) × log10(LBrβ/L⊙) + 4.60(±0.57). These new relationships are key to establishing rough estimates of the accretion rates for large samples of PMS stars with JWST.

Galactic disc heating by density granulation in fuzzy dark matter simulations

Abstract Fuzzy dark matter (FDM), an attractive dark matter candidate comprising ultralight bosons (axions) with a particle mass ma ∼ 10−22 eV, is motivated by the small-scale challenges of cold dark matter and features a kpc-size de Broglie wavelength. Quantum wave interference inside an FDM halo gives rise to stochastically fluctuating density granulation; the resulting gravitational perturbations could drive significant disc thickening, providing a natural explanation for galactic thick discs. Here we present the first self-consistent simulations of FDM haloes and stellar discs, exploring ma = 0.2–1.2 × 10−22 eV and halo masses Mh = 0.7–2.8 × 1011 M⊙. Disc thickening is observed in all simulated systems. The disc heating rates are approximately constant in time and increase substantially with decreasing ma, reaching dh/dt ≃ 0.04 (0.4) kpc Gyr−1 and $d\sigma _z^2/dt \simeq 4$ (150) km2s−2Gyr−1 for ma = 1.2 (0.2) × 10−22 eV and $M_{\rm h}=7\times 10^{10} \, \rm {M}_{\odot }$, where h is the disc scale height and σz is the vertical velocity dispersion. These simulated heating rates agree within a factor of two with the theoretical estimates of Chiang et al., confirming that the rough estimate of Church et al. overpredicts the granulation-driven disc heating rate by two orders of magnitude. However, the simulation-inferred heating rates scale less steeply than the theoretically predicted relation $d\sigma ^2_z/dt \propto m_a^{-3}$. Finally, we examine the applicability of the Fokker–Planck approximation in FDM granulation modelling and the robustness of the ma exclusion bound derived from the Galactic disc kinematics.

Regional Setting of Temples in Dakshina Kosala, India: Spatial Distribution and Connections

Introduction: The region of Dakshina Kosala (also known as South Kosala) is mentioned in various ancient Indian texts. The exact geographical boundary has continuously changed over time, and today, only a rough estimate can be made of the Dakshina Kosala region, which approximately covers the northern and central parts of the present-day state of Chhattisgarh, along with the western part of the state of Odisha, India. Excavations and writings show that this was a prosperous region near central India, without any seaport, but with large markets, trade centers, and educational institutions. To access this region, many trade routes were established from various parts of India. This led to the development of cities with markets along these trade routes. Many temples were also seen in this region, serving as examples of the intangible heritage of the place. Purpose of the study: This region made a significant contribution to the temple art and architecture of India. The present study aims to determine if there is a spatial and architectural link between the temples in this region. Methods: The methodology involved an initial phase of reading and analyzing pertinent literature to prepare detailed maps delineating the boundaries of Dakshina Kosala. Subsequently, employing rigorous analysis with Google Earth and GIS tools, ancient trade routes were traced, temples were located on the maps, and a comprehensive analysis was conducted to derive conclusive results. Results: This paper establishes a relationship between the geographical locations and construction materials of the temples within and outside the geographical boundary of Dakshina Kosala.

Predicting the precipitated calcium carbonate and unconfined compressive strength of bio-mediated sands through robust hybrid optimization algorithms

Biological soil stabilization through Microbial-Induced Calcite Precipitation (MICP) demonstrates the potential to be an eco-friendly and sustainable approach to enhance the strength properties of cohesionless granular soils. In this study, based on a comprehensive set of experimental data compiled from literature, the amounts of Precipitated Calcium Carbonate (PCC) and Unconfined Compressive Strength (UCS) of MICP-treated sands are estimated through hybridizing Artificial Neural Network (ANN) and Adaptive Neuro-Fuzzy Inference System (ANFIS) models with metaheuristic algorithms. During the data extraction procedure, a total of 70 and 115 datasets are used to predict the PCC and UCS, respectively. Accordingly, five key features of urea concentration (Mu), calcium chloride (Mcc), optical density of bacterial suspension (OD600), pH and total volume of injection per volume of sample (Vt), are first selected as the inputs of the hybrid models to estimate the amount of calcium carbonate precipitation within the porous structure of MICP-treated granular medium. The experimental data on the PCC along with the basic characteristics of the host sand including mean grain size (D50), uniformity coefficient (Cu) and void ratio (e), are then adopted as the inputs of the numerical models in the second phase whilst the UCS of bio-mediated sands is set as the output. The performance and accuracy of implemented models are rigorously assessed through analyzing various statistical indices and performance criteria. According to the numerical analysis, the ANN-PSO and ANFIS-PSO hybrid models show the best performance in predicting PCC and UCS of bio-mediated sands, respectively. Using the well-established Gene Expression Programming (GEP) algorithm, practical correlations are developed to predict the PCC and UCS values of MICP-mediated soils based on the characteristics of biological binding agents as well as the host material. The Machine Learning (ML)-based models presented in this study provide engineers with a fruitful framework for the rough and preliminary estimation of the amount of calcite precipitation as well as the UCS of bio-mediated sands in the field prior to stabilization and performing complementary tests.

Modifying the gas-oil ratio and water cut of a PVT lookup table using correlations for fluid volatility

PVT lookup tables with fixed fluid composition are widely used by simulation software packages to model complex multiphase flow in petroleum production systems. These tables contain pre-calculated values of various fluid properties, including phase fractions, densities, viscosities, and other properties that are relevant to fluid behaviour. The pre-calculated values are based on a given set of pressures, temperatures, and composition. The tables enable efficient and accurate simulations by reducing computational load. However, they are created for fluids with fixed total composition, which limits their use for modelling fluids with varying gas-oil ratios and water cuts.To address this limitation, this work introduces a novel methodology for adjusting the gas-oil ratio and water cut within a given PVT table without changing the composition of the fluid. The methodology relies on correlations developed for volatility, estimating bubble- and dewpoint pressures when adjusting gas-oil ratio and water cut. The model demonstrates remarkable efficacy, achieving excellent agreement with bubble- and dewpoint pressures, yielding average absolute relative errors of only 0.85% and 0.68% respectively for gas-oil ratio adjustments, and 0.97% for water cut adjustments. It is further demonstrated that the new model accurately predicts phase fractions of typical volatile oils below the critical temperature and black oils when adjusting the gas-oil ratio and water cut. Additionally, the model can provide rough estimates for volatile oils above the critical temperature and for gas condensates. Moreover, model reliability is expected if the gas-oil ratio and water cut adjustments are kept within a range of 20–180% of the original values.The work provides a valuable approach to improve the flexibility of fixed composition PVT lookup tables for simulating multiphase production systems.

Correlation of the Molecular Cross-Sectional Area of Organic Monofunctional Compounds with Topological Descriptors.

The molecular cross-sectional area (σ) has proved to be an interesting molecular measure not only in the field of adsorption phenomena on solids but also in biochemistry, physiology, or surfactant chemistry. The existing methods to estimate the cross-sectional areas are either not readily applicable or can only be applied to a limited number of compounds. The aim of this work was to describe a method, as general as possible, quick and easy to perform. To that end, the molecular cross-sectional areas were correlated with topological indices. The Emmett-Brunauer formula was used to calculate the reference cross-sectional areas (σEB) of 431 compounds. The correlations of the Wiener (W), hyper-Wiener (WW), Balaban (J), and Randić (χ) indices with σEB were compared for n-alkanes as well as branched and cyclic alkanes. Only the Wiener and hyper-Wiener indices correlated well with σEB, with the data being best fitted by power law regression curves. The lower degeneracy of the hyper-Wiener index did not translate into any significant gain of performance when correlated with σEB. Following the parsimony principle, the less complex Wiener index was thus selected to correlate with the σEB of compounds representing 31 other monofunctional and structural families. The integration of all the compound families into a single curve allowed a quick rough estimation of the cross-sectional areas. The specific reference equations σEB = qWp were determined for the 34 selected families, allowing the fast and reliable calculation of the cross-sectional area of any monofunctional compound.