Quantitative Analytical Model Research Articles

The Fastest Capillary Flow in Root-like Networks under Gravity.

Capillary flow has garnered significant attention due to its unique dynamic characteristics that require no external force. Creating a quantitative analytical model to evaluate capillary flow behaviors in root-like networks is essential for enhancing fluid control properties in functional textiles. In this study, we explore the capillary dynamics within root-like networks under the influence of gravity and derive the most rapid capillary flow via structural optimization. The flow time in a capillary is dominated by the capillary pressure, viscous pressure loss, and gravity, each of which exhibits diverse sensitivities to the structures of root-like networks. We scrutinize various structural parameters to understand their impact on capillary flow in root-like networks. Subsequently, optimal structural parameters (namely, the mother tube diameter and diameter ratio) are identified to minimize capillary flow time. Moreover, we discovered that the correlation between flow time and distance for capillary flow in root-like networks does not obey the classical Lucas-Washburn equation. These results affirm that root-like networks can enhance capillary flow, providing critical insights for numerous capillary-flow-dependent engineering applications.

Quantitative assessment whole-rock iron content in magnetite protolith based on terahertz time-domain spectroscopy.

Terahertz time-domain spectroscopy was first used to establish a correlation with the whole-rock iron (TFe) content in different depths of the Bayan Obo protolith. Compared with element content obtained by the traditional method of X-ray fluorescence spectroscopy (XRF), a similar tendency of the absorption coefficient and refractive index is presented. Furthermore, three machine learning algorithms, namely, partial least squares regression (PLSR), random forest (RF), and multi-layer perceptron (MLP), were used to develop a quantitative analytical model for TFe content of the protolith minerals. Among the three algorithms, MLP has the highest detection accuracy, with a model coefficient of determination R 2 reaching up to 0.945. These findings demonstrate that terahertz time-domain spectroscopy can be used to rapidly quantify the TFe elemental content of protolith, providing a method of detecting the content of mineral components.

Guiding Principles for the Design of a Chemical Vapor Deposition Process for Highly Crystalline Transition Metal Dichalcogenides

2D transition metal dichalcogenides (TMDs) for advanced logic transistor technologies are deposited by various modifications of the chemical vapor deposition (CVD) method using a wide variety of precursors. Being a major electrical performance limiter, the TMD crystal grain size strongly differs between the various CVD precursor chemistries from nano‐ to millimeter‐sized crystals. However, it remains unclear how the CVD precursor chemistry affects the nucleation density and resulting TMD crystal grain size. This work postulates guiding principles to design a CVD process for highly crystalline TMD deposition using a quantitative analytical model benchmarked against the literature. The TMD nucleation density reduces favorably under low supersaturation conditions where the metal precursor sorption on the starting surface is reversible and the corresponding metal precursor desorption rate exceeds the overall deposition rate. Such reversible precursor adsorption guarantees efficient long‐range gas‐phase lateral diffusion of precursor species in addition to short‐range surface diffusion, which vitally increases crystal grain size. The proposed model explains the large spread in experimentally observed TMD nucleation densities and crystal grain sizes for state‐of‐the‐art CVD chemistries. Ultimately, it empowers the reader to interpret and modulate precursor adsorption and diffusion reactions through designing CVD precursor chemistries.

High-quality Development Path of Digital Economy and Cultural Industry under the Perspective of Low-carbon Development

Abstract This paper focuses on how the cultural industry affects economic growth through the theory and model of cultural economics. Based on the development of the economic growth model endogenized by the cultural industry, it explains how the arts and entertainment sector and the digital economy play a crucial role in the long-term economic growth after examining the logic and mechanism of the cultural industry impacting economic growth through induction and theoretical derivation. To create a quantitative analytical model of the digital economy and cultural industry and investigate the relationship between them, multiple regression analysis is employed. The impact of individual aspects of each condition variable on the economic performance composition of the cultural industry is evaluated using group effects. The results of the NCA test demonstrate that the digital economy, the scale of the cultural industry, public participation, and resources are all necessary for the sector to perform economically. The effect sizes of these factors are (0.525, 0.540), (0.362, 0.347), (0.315, 0.363), and (0.271, 0.188). The digital economy’s regression coefficients, which are usually positive, are0.470, 0.461, 0.428, 0.422, 0.383, and 0.381, according to the regression study. According to this study, the cultural sector’s growth at a high standard is positively impacted by the digital economy.

Analysis of the joint trading of local green power certificates, carbon emissions rights, and electricity considering demand flexibility

Establishing synergy among distinct yet associated markets through market orientation is crucial because it can facilitate a clean and low-carbon energy transition. However, researchers have not yet developed a quantitative analytical dynamic model for studying market participants in the context of the joint electricity-carbon-green power certificates market. Moreover, the solution algorithm merits further exploration. This article proposes an analytical method for evaluating the joint trading of local green power certificates, carbon emission rights, and electricity, while taking into account the demand flexibility. First, a framework for the joint trading of local green power certificates, carbon emission rights, and electricity is designed. Second, market participants, such as traditional thermal power plants, renewable energy power plants, and load aggregators, are modelled via system dynamics theory. On this basis, a deep distributed reinforcement learning algorithm is applied to solve the problem. Finally, the effectiveness of the proposed theoretical approach is verified through real-life simulations, and the sensitivity of the key factors influencing joint trading is also analysed.

Designing Superlubricious Hydrogels from Spontaneous Peroxidation Gradients.

Hydrogels are hydrated three-dimensional networks of hydrophilic polymers that are commonly used in the biomedical industry due to their mechanical and structural tunability, biocompatibility, and similar water content to biological tissues. The surface structure of hydrogels polymerized through free-radical polymerization can be modified by controlling environmental oxygen concentrations, leading to the formation of a polymer concentration gradient. In this work, 17.5 wt % polyacrylamide hydrogels are polymerized in low (0.01 mol % O2) and high (20 mol % O2) oxygen environments, and their mechanical and tribological properties are characterized through microindentation, nanoindentation, and tribological sliding experiments. Without significantly reducing the elastic modulus of the hydrogel (E* ≈ 200 kPa), we demonstrate an order of magnitude reduction in friction coefficient (from μ = 0.021 ± 0.006 to μ = 0.002 ± 0.001) by adjusting polymerization conditions (e.g., oxygen concentration). A quantitative analytical model based on polyacrylamide chemistry and kinetics was developed to estimate the thickness and structure of the monomer conversion gradient, termed the "surface gel layer". We find that polymerizing hydrogels at high oxygen concentrations leads to the formation of a preswollen surface gel layer that is approximately five times thicker (t ≈ 50 μm) and four times less concentrated (≈ 6% monomer conversion) at the surface prior to swelling compared to low oxygen environments (t ≈ 10 μm, ≈ 20% monomer conversion). Our model could be readily modified to predict the preswollen concentration profile of the polyacrylamide gel surface layer for any reaction conditions─monomer and initiator concentration, oxygen concentration, reaction time, and reaction media depth─or used to select conditions that correspond to a certain desired surface gel layer profile.

Quality control of trimethylolpropane synthesis by intelligent retrieval of near-infrared spectra

<p indent="0mm">We developed an intelligent inversion method of near-infrared spectroscopy to establish a quantitative analysis model of the product and its by-products in the production process of trimethylolpropane (TMP), to realize the rapid detection of its product and its by-product content, to quantitatively monitor the process synthesis of TMP, and to build an online monitoring system of the TMP synthesis process. Firstly, gas chromatography was used to determine the content of the product and its by-products during the synthesis of TMP, and then the NIR spectra of a large number of samples were collected, and finally, the partial least squares regression (PLS) method of TQ Analyst software was invoked to establish four intelligent prediction models for the product TMP, the by-products bis-trimethylolpropane, 2-ethyl-2-hexenal and 2-ethylacrolein. The results showed that the corrected root mean square errors (RMSE(C) of the four quantitative analytical models developed for TMP, bis-trimethylolpropane, 2-ethyl-2-hexenal and 2-ethylacrylaldehyde were 0.744, 0.300, 1.50 and 1.28, respectively, and the correlation coefficients (<italic>R</italic>) were 0.9868, 0.9840, 0.9945 and 0.9113, respectively. The root mean square errors of prediction (RMSEP) were 1.29, 0.588, 1.44, and 1.18, respectively, and the <italic>R</italic> were 0.9797, 0.9209, 0.9960, and 0.9323. It was finally confirmed that the developed NIR intelligent inversion model of the products and their by-products in the synthesis process of TMP has good prediction accuracy and prediction stability, and can be used for online monitoring of the synthesis process of TMP.

Modeling the artificial night sky brightness at short distances from streetlights

Contrary to some widespread intuitive belief, the night sky brightness perceived by the human eye or any other physical detector does not come (exclusively) from high in the sky. The detected brightness is built up from the scattered radiance contributed by all elementary atmospheric volumes along the line of sight, starting from the very first millimeter from the eye cornea or the entrance aperture of the measuring instrument. In artificially lit environments, nearby light sources may be responsible for a large share of the total perceived sky radiance. We present in this paper a quantitative analytical model for the sky radiance in the vicinity of outdoor light sources, free from singularities at the origin, which provides useful insights for the correct design or urban dark sky places. It is found that the artificial zenith sky brightness produced by a small ground-level source detected by a ground-level observer at short distances (from the typical dimension of the source up to several hundred meters) decays with the inverse of the distance to the source. This amounts to a reduction of 2.5 mag/arcsec2 in sky brightness for every log10 unit increase of the distance. The effects of screening by obstacles are also discussed.

Rapid Analysis of Raw Meal Composition Content Based on NIR Spectroscopy for Cement Raw Material Proportioning Control Process

Due to fast analysis speed, analyzing composition content of cement raw meal utilizing near infrared (NIR) spectroscopy, combined with partial least squares regression (PLS), is a reliable alternative method for the cement industry to obtain qualified cement products. However, it has hardly been studied. The raw materials employed in different cement plants differ, and the spectral absorption intensity in the NIR range of the raw meal component is weaker than organic substances, although there are obvious absorption peaks, which place high demands on the generality of modeling and accuracy of the analytical model. An effective modeling procedure is proposed, which optimizes the quantitative analytical model from several modeling stages, and two groups of samples with different raw material types and origins are collected to validate it. For the samples in the prediction set from Qufu, the root mean square error of prediction (RMSEP) of CaO, SiO2, Al2O3, and Fe2O3 were 0.1910, 0.2307, 0.0921, and 0.0429, respectively; the average prediction errors for CaO, SiO2, Al2O3, and Fe2O3 were 0.171%, 0.193%, 0.069%, and 0.032%, respectively; for the samples in the prediction set from Linyi, the RMSEP of CaO, SiO2, Al2O3, and Fe2O3 were 0.1995, 0.1267, 0.0336 and 0.0242, respectively, the average prediction errors for CaO, SiO2, Al2O3, and Fe2O3 were 0.154%, 0.100%, 0.022%, and 0.018%, respectively. The standard methods for chemical analysis of cement require that the mean measurement error for CaO, SiO2, Al2O3, and Fe2O3 should be within 0.40%, 0.30%, 0.20%, and 0.15%, respectively. It is obvious that the results of both groups of samples fully satisfied the requirements of raw material proportioning control of the production line, demonstrating that the modeling procedure has excellent generality, the models established have high prediction accuracy, and the NIR spectroscopy combined with the proposed modeling procedure is a rapid and accurate alternative approach for the analysis of cement raw meal composition content.

Improvement of NIR prediction ability by dual model optimization in fusion of NSIA and SA methods

Feature selection and sample partitioning are both important to establish a quantitative analytical model for near-infrared (NIR) spectroscopy. The classical interval partial least squares (iPLS) model for waveband selection can be improved in combination of the simulated annealing (SA) algorithm. The sample set partitioning based on a joint x-y distance (SPXY) method for sample partitioning is based on the distances of both the x- and y- dimensions; it is expected to be optimized using the non-dominant sorting strategies (NS) combined with the immune algorithm (IA). In this study, we investigated the dual model optimization mode for simultaneous selection of feature waveband and sample partitioning, and proposed a novel method defined as SA-iPLS & SPXY-NSIA. The method explores a population evolution process, and takes the candidate individual as the link for the fusion optimization of SA-iPLS and SPXY-NSIA. The method screens feature wavebands and observes a good partition of the modeling samples, to construct a combined optimization strategy for fusion optimization of the target waveband and suitable sets of sample partitioning. The performance of the SA-iPLS & SPXY-NSIA method was tested using a soil sample dataset. To prove model enhancement, the proposed method was compared to the two traditional methods of Kennard-Stone (KS) and SPXY in combination with SA-iPLS. Experimental results show that the fusion model established by SA-iPLS & SPXY-NSIA performed better than the KS-SA-iPLS and SPXY-SA-iPLS models. The best testing results of the fusion model is with RMSET, RPDT and RT observed as 0.0107, 1.7233 and 0.9097, respectively. The proposed method is prospectively able to effectively improve the predictive ability of the NIR analytical model.

Label-free monitoring of spatiotemporal changes in the stem cell cytoskeletons in time-lapse phase-contrast microscopy.

Investigation of the dynamic structural changes in the actin cytoskeleton during cell migration provides crucial information about the physiological conditions of a stem cell during in-vitro culture. Here we proposed a quantitative analytical model associated with texture extraction with cell tracking techniques for in situ monitoring of the cytoskeletal density change of stem cells in phase-contrast microscopy without fluorescence staining. The reliability of the model in quantifying the texture density with different orientation was first validated using a series of simulated textural images. The capability of the method to reflect the spatiotemporal regulation of the cytoskeletal structure of a living stem cell was further proved by applying it to a set of 72 h phase-contrast microscopic video of the growth dynamics of mesenchymal stem cells in vitro culture.

Evaluation of quality consistency of herbal preparations using five-wavelength fusion HPLC fingerprint combined with ATR-FT-IR spectral quantized fingerprint: Belamcandae rhizoma antiviral injection as an example

The present study was designed to establish a comprehensive and practical method for the quality evaluation and control of herbal preparation (HEP). The chemical information of belamcandae rhizoma antiviral injection (BRAI) was extracted using a dual-system control system comprised of HPLC chromatography and attenuated total reflection Fourier transform infrared (ATR-FT-IR) spectroscopy. To begin with, a quantitative analysis of six BRAI index components was accomplished. On this basis, HPLC five-wavelength fusion fingerprint was established to control the overall quality of BRAI. Secondly, the quality control system of ATR-FT-IR spectroscopy was constructed, that is, a quantitative analytic model to forecast the total amount of index components. Concurrently, the ATR-FT-IR quantized fingerprint was established for similarity analysis utilizing the Systematic quantified fingerprint method (SQFM). In addition, an integrated evaluation technique based on equal weights was presented to assure the comprehensiveness of chemical information. The 20 samples demonstrated good consistency in terms of quality, with a quality rating of grade 1–3. Notably, the rationality of the average method of Systematic quantified fingerprint method application in BRAI was further verified using PCA and low-level data fusion. Finally, this paper developed a model for intelligent analysis that is both quick and complete. We have redefined the quality standard of belamcandae rhizoma that can be put into production (70%< Pm<130%) in light of the influence of other HEM in the BRAI. This research demonstrated a quality analysis technique based on chromatography and spectrum, which was suited for HEP consistency control since it was easy, quick, accurate, and reliable.

Design and fabrication of Gr/Ag-coated tilted grating sensor for ultra-sensitive detection of DNA hybridization

A graphene sensitized biosensor based on 6° tilted fiber Bragg grating (TFBG) was proposed. The introduction of the graphene coating boosted the sensitivity and stability of the Ag/TFBG sensor, and improved the probe loading and molecular capture of the sensitized layer. A quantitative analytical model was proposed to calculate the hybridization efficiency and maximum sensor response of DNA single base mismatch. The results showed that the kinetic affinity response of the Gr/Ag/TFBG sensor was stably demonstrated with a dissociation constant (kd=9.392 ± 0.178·10−3 min−1) and the detection limit of DNA was as low as 3 pM. This indicates that the sensor has the ability to distinguish single base mutations quantitatively in real time. The surface modification, probe binding and DNA hybridization were characterized based on atomic force microscopy (AFM) and Raman detection spectrum detection system. A 3D diversity analysis model for TFBG sensors was innovatively proposed based on COMSOL. The multi-mode coupling and polarization response analysis of the inner core, the electric field distribution and polarization transmission mode of the sensitized layer, and the polarization gain brought by graphene are systematically described. Excellent bioanalytical capabilities combined with well-established analytical methods make prepared sensor promising for high-throughput screening of genetic variants and disease markers.

Quantitative Analysis of Dynamic Subacromial Ultrasonography: Reliability and Influencing Factors.

Objective: Current imaging methods used to examine patients with subacromial impingement syndrome (SIS) are limited by their semi-quantitative nature and their capability of capturing dynamic movements. This study aimed to develop a quantitative analytic model to assess subacromial motions using dynamic ultrasound and to examine their reliability and potential influencing factors. Method: We included 48 healthy volunteers and examined their subacromial motions with dynamic ultrasound imaging. The parameters were the minimal vertical acromiohumeral distance, rotation radius, and degrees of the humeral head. The generalized estimating equation (GEE) was used to investigate the impact of different shoulder laterality, postures, and motion phases on the outcome. Result: Using the data of the minimal vertical acromiohumeral distance, the intra-rater and inter-rater reliabilities (intra-class correlation coefficient) were determined as 0.94 and 0.88, respectively. In the GEE analysis, a decrease in the minimal vertical acromiohumeral distance was associated with the abduction phase and full-can posture, with a beta coefficient of −0.02 cm [95% confidence interval (CI), −0.03 to −0.01] and −0.07 cm (95% CI, −0.11 to −0.02), respectively. The abduction phase led to a decrease in the radius of humeral rotation and an increase in the angle of humeral rotation, with a beta coefficient of −1.28 cm (95% CI, −2.16 to −0.40) and 6.60° (95% CI, 3.54–9.67), respectively. A significant negative correlation was observed between the rotation angle and radius of the humeral head and between the rotation angle and the minimal vertical acromiohumeral distance. Conclusion: Quantitative analysis of dynamic ultrasound imaging enables the delineation of subacromial motion with good reliability. The vertical acromiohumeral distance is the lowest in the abduction phase and full-can posture, and the rotation angle of the humeral head has the potential to serve as a new parameter for the evaluation of SIS.

Non-destructive quantitative analysis of pharmaceutical ointment by transmission Raman spectroscopy

Transmission Raman spectroscopy was used to develop a non-destructive quantitative analytical model for the assay of a crystal dispersion-type ointment containing acyclovir as a model drug with a concentration of 3% w/w. The obtained Raman spectra were pre-processed by applying multiplicative scatter correction, standard normal variate, and first or second derivative by the Savitzky-Golay method to optimize the partial least squares (PLS) regression model. The optimized PLS model showed good prediction performance for 85%, 100%, and 115% label claims, with average recovery values of 100.7%, 99.3%, and 99.8%, respectively. Although the material properties and manufacturing method of acyclovir and white petrolatum were expected to be different from those of the calibration set, the mean recovery value of the commercial product was 104.2%. These results indicate that transmission Raman spectroscopy is a useful process analytical technology tool for product development and quality control of a crystal dispersion-type ointment with low drug concentration.

Nonlinear heating and scattering in a single crystalline silicon nanostructure.

Silicon nanophotonics has attracted significant attention because of its unique optical properties such as efficient light confinement and low non-radiative loss. For practical applications such as all-optical switch, optical nonlinearity is a prerequisite, but the nonlinearity of silicon is intrinsically weak. Recently, we discovered a giant nonlinearity of scattering from a single silicon nanostructure by combining Mie resonance enhanced photo-thermal and thermo-optic effects. Since scattering and absorption are closely linked in Mie theory, we expect that absorption, as well as heating, of the silicon nanostructure shall exhibit similar nonlinear behaviors. In this work, we experimentally measure the temperature rise of a silicon nanoblock by in situ Raman spectroscopy, explicitly demonstrating the connection between nonlinear scattering and nonlinear heating. The results agree well with finite-element simulation based on the photo-thermo-optic effect, manifesting that the nonlinear effect is the coupled consequence of the red shift between scattering and absorption spectra. Our work not only unravels the nonlinear absorption in a silicon Mie-resonator but also offers a quantitative analytic model to better understand the complete photo-thermo-optic properties of silicon nanostructures, providing a new perspective toward practical silicon photonics applications.

Socially Responsible Human Resource Management and Voluntary Environmental Behavior: Moderating the Effect of Ecocentric Leadership

Purpose: The study examines the influence of socially responsible human resource management (SRHRM) on employees’ voluntary environmental behavior (VEB), and it tests the moderating effect of ecocentric leadership (EL). Design/Method: We adopted deductive reasoning in a positivist paradigm by applying quantitative analytical techniques and structural equation modeling. We selected 187 respondents from clothing industry through a self-administered questionnaire survey in Chattogram, the commercial capital of Bangladesh. Findings: The outcomes revealed that both SRHRM and EL influence VEB. However, the moderation analysis indicated that EL negatively intervenes in the effect of SRHRM on VEB. Implications: One of the essential contributions of our study is its development of the knowledge related to valuing, developing, and measuring SRHRM’s contribution to improving VEB in a moderated model with the help of EL to ensure long-term sustainability. Originality: Very little is known about the importance of SRHRM in fostering employees’ voluntary environmental behavior, so this study will be one of the first to examine the topic. Moreover, the moderating effect of EL posits that its interaction effect is not substantial in stimulating voluntary environmental behavior if organizations can institutionalize SRHRM.

Performance Modeling of Moving Target Defenses with Reconfiguration Limits

Moving Target Defense (MTD) has recently emerged as a game changer in the security landscape due to its proven potential to introduce asymmetric uncertainty that gives the defender a tactical advantage over the attacker. Many different MTD techniques have been developed, but, despite the huge progress made in this area, critical gaps still exist with respect to the problem of studying and quantifying the cost and benefits of deploying MTDs. In fact, all existing techniques address a very narrow set of attack vectors, and, due to the lack of shared metrics, it is difficult to quantify and compare multiple techniques. Building on our preliminary work in this field, we propose a quantitative analytic model for assessing the resource availability and performance of MTDs, and a method for maximizing a utility function that captures the tradeoffs between security and performance. The proposed model generalizes our previous model and can be applied to a wider range of MTDs and operational scenarios to improve availability and performance by imposing limits on the maximum number of resources that can be in the process of being reconfigured. The analytic results are validated by simulation and experimentation, confirming the accuracy of our model.

Attached and Attaching Three-Dimensional Jets From Circular Nozzles: Analytical Model

Abstract Here we consider velocity profiles of three dimensional attaching and attached jets emerging from circular nozzles. Like their well-studied two-dimensional counterparts, these wall jets lose momentum due to interactions with nearby surfaces. Unlike their two-dimensional counterparts, simple and quantitative expressions for the velocity profiles of three-dimensional wall jets remain elusive. Here we present a quantitative analytical model of the three-dimensional velocity profiles of attached jets inclusive of a local skin coefficient of friction. We compare these expressions to experimental velocity profiles at moderate and high nozzle Reynolds numbers to find reasonable quantitative agreement. This work has implications for a variety of industries including nuclear waste processing, where jet flows in mixing vessels suspend solids and gases trapped in radioactive waste tanks.

A Quantitative Analytical Model for Predicting and Optimizing the Rate Performance of Battery Cells

The prediction of the performance of battery cells is usually accomplished by computationally expensive numerical simulations. Here, we present a simple analytical model as an efficient alternative to predict the rate capability of battery cells limited by electrolyte transport without the need to fit parameters to simulations. It exhibits very good agreement with simulations over a wide range of discharge rate and electrode thickness and offers a speedup of >10 5 times. The optimal electrode properties predicted by the model differ <∼10% from simulation results, suggesting it as an attractive computational tool for the cell-level design of batteries. The model reveals that the discharge capacities of half- and full cells exhibit qualitatively different scaling relations with electrode thickness and current density, and the rate performance of thick electrodes can be improved by avoiding electrode materials (e.g., LiFePO 4 , Li 4 Ti 5 O 12 ) whose open-circuit potentials are insensitive to the state of charge. A simple analytical model to predict battery cell rate performance The model can replace P2D battery simulations with >100,000-fold speedup Performance of half- versus full battery cells scales differently with cell properties A prediction that NMC has better rate capability than LiFePO 4 in thick electrodes Prediction of battery cell performance is traditionally accomplished by sophisticated numerical simulations. Wang and Tang develop a simple analytical model implemented in open-source code as an efficient alternative, which enables quantitative design and optimization at a negligible computational cost and offers revealing insights into the dependence of rate capability on cell properties.