Mixing Weights Research Articles

Urinary NGAL and KIM-1 in Canine Monocytic Ehrlichiosis

Glomerulonephritis, caused by the deposition of immune complexes, can lead to kidney damage in dogs with canine monocytic ehrlichiosis (CME). The early diagnosis of renal insult is important to prevent severe kidney disease in infected dogs by Ehrlichia canis. This study aimed to investigate urinary biomarkers of renal function, neutrophil gelatinase (uNGAL), and kidney injury molecule-1 (uKIM-1) using the Luminex® xMAP® platform, and the proportion of mixed or high molecular weight proteinuria in dogs with CME. This study included blood samples of thirty dogs with clinical signs of CME and amplified DNA for E. canis (CME group) and six dogs of different breeds and both sexes, aged 3 to 7 years, that showed no clinical-laboratory alterations or tick parasitism and were tested negative for E. canis via PCR (control group). The total calcium, phosphorus (p < 0.05), urea (p < 0.001), creatinine (p < 0.05), urinary density (p < 0.05), urinary protein creatinine ratio (p < 0.001), uNGAL (p < 0.05), and uKIM-1 (p > 0.05), as well as the proportion of high molecular weight proteinuria and mixed proteinuria (p < 0.01), were measured. Elevated serum concentrations of creatinine, urea, and phosphorus combined with reduced urinary density, increased urinary creatinine–protein ratio, urinary NGAL, and mixed proteinuria detected renal damage in dogs with CME, while KIM -1 remained unchanged. uNGAL can detect early renal lesions, reflecting renal damage before a significant increase in serum creatinine occurs, and appears to be an early diagnostic biomarker in renal disease in dogs with CME.

A neural network integrated mathematical model to analyse the impact of nutritional status on cognitive development of child

Cognitive development is a crucial developmental aspect of children. It is a concise field of study in psychology and neuroscience that focuses on various developmental aspects of the brain. Among all other factors, nutritional status is believed to play a very important role in cognitive development. The purpose of this work is to analyze the impacts of different nutritional status levels on the child’s cognitive development. This work designs a model that uses a neural network and differential equations. The neural network is applied on a dataset called “Child Birth Weight Dataset” available at IEEE Dataport ( http://dx.doi.org/10.21227/dvd4-3232) for finding the nutritional status of a child. The different levels of nutritional status, such as low-nutritional status, normal-nutritional status, and over-nutritional status are integrated with the formulated differential equations. The model is computationally simulated considering four different sets of parameter values that represent four different perspectives such as ‘only positive’, ‘only negative’, ‘mix and unequal weight’, and ‘mix and equal weight’ of the influencing factors. The experimental results show that normal-nutritional status is the best nutritional status for cognitive development. However, the best cognitive development happens when all other influencing factors like environmental effects, socioeconomic status, heredity, learning opportunities, and use of experiences are given equal importance. The results also depict that the low- and over-nutritional status cannot restrict cognitive development for a long time. After a certain period, the development gets triggered and it happens. It may be slow and not up to the mark of the development under normal-nutritional status, but it happens. Simply it can be said that nutritional status alone does not have control over the cognitive development of a child. Along with nutritional status, other influencing factors are important too.

Survival Function Estimation for COVID-19 Patients Using TPLD and PTPLD

This paper aims to study some properties of the three-parameter Lindley distribution TPLD and the proposed new formula for the TPLD in studying the life-time distribution, survival function, and hazard function. We proposed a new formula for the TPLD by considering the new mixing weights of the mixing function that were used in constructing the TPLD and by replacing the mixing weights in the mixing equation to get a new formula for the TPLD that is called the proposed three-parameter Lindley distribution, and we denoted it by PTPLD. We study some properties of PTPLD in the lifetime distribution. We use three methods of estimation, the method of moments , the MLH method and Standard Bayesian method, to estimate the parameters and the survival functions for the two distributions (TPLD and PTPLD) and compare the survival functions estimates for both distributions using the above three methods of estimation, and we select the best estimate based on some criteria of error. In addition, we used real data from the AL-Hussain Teaching Hospital in Nasiriya City to estimate the survival time of infected patients with COVID-19 using TPLD and PTPLD. It was concluded that the proposed formula for the distribution is the best for all selected sample sizes.

Bivariate density estimation under marginal unimodality constraints

Empirical analyses of observed pairs of variables often suggest that the underlying joint distribution of these variables is highly dependent and each one of them is marginally unimodal. Exploiting marginal shape information can improve the bivariate density estimation. We develop a nonparametric density estimation method where the joint density of the bivariate outcome variables is estimated using a flexible class of mixtures of scaled Beta distributions and the mixing weights are suitably constrained to ensure marginal unimodality. Large sample consistency of the proposed density estimator is established using the method of sieves and Argmax Continuous Mapping Theorem. The proposed method enlarges the scope of previous studies in two important aspects: (i) joint density provides a simultaneous estimation of higher-order moments beyond mean and variance functions, and (ii) marginal shape constraints provide more efficient estimates. The proposed procedures are illustrated using simulated and real case study datasets on gestational age and infant birth weight.

Filtering coupled Wright-Fisher diffusions.

Coupled Wright-Fisher diffusions have been recently introduced to model the temporal evolution of finitely-many allele frequencies at several loci. These are vectors of multidimensional diffusions whose dynamics are weakly coupled among loci through interaction coefficients, which make the reproductive rates for each allele depend on its frequencies at several loci. Here we consider the problem of filtering a coupled Wright-Fisher diffusion with parent-independent mutation, when this is seen as an unobserved signal in a hidden Markov model. We assume individuals are sampled multinomially at discrete times from the underlying population, whose type configuration at the loci is described by the diffusion states, and adapt recently introduced duality methods to derive the filtering and smoothing distributions. These respectively provide the conditional distribution of the diffusion states given past data, and that conditional on the entire dataset, and are key to be able to perform parameter inference on models of this type. We show that for this model these distributions are countable mixtures of tilted products of Dirichlet kernels, and describe their mixing weights and how these can be updated sequentially. The evaluation of the weights involves the transition probabilities of the dual process, which are not available in closed form. We lay out pseudo codes for the implementation of the algorithms, discuss how to handle the unavailable quantities, and briefly illustrate the procedure with synthetic data.

Performance of natural asphalt as a paving material: A laboratory and field evaluation

The ever-growing need to build roads to meet the necessary transportation demands is challenging, especially for developing countries. Low-volume roads (LVRs) are usually the backbone of catalyzing economic growth in these countries. With impediments surrounding Petroleum bitumen (price fluctuations) and environmental concerns, scientists are putting their effort into finding an alternative. The presented research is an attempt to check if Natural asphalt can be used as a full or partial replacement of the Petroleum bitumen. To the best of the authors' knowledge, only limited studies have focused on characterizing and understanding the engineering properties of Natural asphalt. The available techniques do not provide reliable information to the road authorities and hence they are discouraged from using it in practice. Particularly for countries, where the Natural asphalt source is available, the overall dependence on importing the Bitumen could be substantially reduced. Empirical and experience-based design criteria may not be sufficient as the standards were never developed for such materials, hence, a scientific approach is required before bringing it into practice. In this research, Natural asphalt sourced from different locations in Nigeria was assessed. Before performing the mixture level tests using Marshall and Cantabro design methods, the rheological and fatigue properties of the extracted Natural bitumen were examined in the laboratory. In the design of the experiment, various percentages of Natural asphalt were added between 0 % and 20 % by total mix weight; implying that the remaining required fraction of binder was fulfilled by the addition of petroleum bitumen. By using a ranking system (supported by statistics), an optimal design of mixture was obtained which was used in the field (exposed to normal traffic) at 30 different sections. Overall, results showed a good correlation between laboratory and field performance as compared to the petroleum-based mixture which was used as a reference mixture

Periodic dual-mixing method for fast and robust solving of ultra-thin fluid-structure interaction problems

This study proposes the periodic dual-mixing method (PDMM) that can rapidly and robustly solve ultra-thin fluid-structure interaction (UTFSI) problems, especially in severe mixed lubrication. The dual mixing denotes a two-step process: the first step is to mix the historical inputs and outputs separately according to optimal mixing weights; the obtained results are mixed with dynamic damping factors in the second step to predict the deformation of the next iteration. Meanwhile, the residual is injected into the predicted deformation periodically. The comparison study is carried out with the popular IQN-ILS method in general FSI, which shows the PDMM is 5.3 times faster and 5 times more robust. Furthermore, the advantages of the PDMM are more significant under harsh mixed lubrication conditions.

The Two Component Generalized Finite Erlang Mixture and its Properties and Special Cases

This research proposes the construction of a two-component generalized finite Erlang mixture with four parameters. Three distinct cases of this mixture are presented, differing in their mixing weights and corresponding component probability (Erlang) distributions. Special cases of these mixtures, including the one-, two-, and three-parameter finite Erlang mixtures, have also been derived. The statistical properties examined for these mixtures include the distribution function, survival function, hazard function, moment generating function, raw and central moments, mean, variance, coeffcient of skewness, coeffcient of kurtosis, and order statistics. Parameter estimation for the finite Erlang mixtures was conducted using both the method of moments and maximum likelihood estimation. Furthermore, the 4-parameter mixed Erlang distributions were applied to a real dataset on the relief times of patients receiving an analgesic, to evaluate their goodness of fit. The results demonstrate the potential of these mixtures to provide robust modeling for empirical data, suggesting their applicability in various statistical and practical contexts.

E-commerce and Mobile Application Development in the Sports Industry

In order to achieve user recommendations that best match their current contextual needs, the author proposes a mobile service QoS (Quality of Service) hybrid recommendation model based on sports user situational awareness. Cluster the users and service items covered by mobile users based on their location contextual information according to the classification principle of autonomous systems, forming a collaborative filtering and recommendation mechanism for mobile user service items; In response to the cold start problem of new users and new projects in traditional QoS recommendation methods, prediction and recommendation of missing QoS attribute preference values are based on User based and Item based CF; In response to the problem of difficult determination of mixed recommendation weights caused by massive data and uneven distribution of service QoS attribute values in mobile network environments, MapReduce based ant colony neural network weight training is used for CF mixed recommendation. Experimental results have shown that the Hadoop sports industry has improved the operational efficiency of algorithms and reduced the time for global QoS preference prediction; And by comparing the operation results of the 10% and 100% sub datasets, it can also be seen that the acceleration ratio of the algorithm will continuously improve with the increase of data volume, thereby improving the operational efficiency of the recommendation algorithm. It has been proven that the MapReduce based ant colony neural network weight training method significantly reduces the global computation time of the algorithm and improves the operational efficiency of the recommendation system.

Co‐Application of Wood Biochar and Nano‐Titanium Dioxide to Immobilize Vanadium in Alkaline Soils

ABSTRACTTitanium dioxide (TiO2) and biochar have been used as amendments to adsorb vanadium (V) in aqueous solutions; however, their simultaneous application in the remediation of V‐contaminated neutral‐alkaline soils is rare. TiO2 nanoparticles, biochar, and a blend of biochar+TiO2 were investigated as amendments for an alkaline V‐contaminated soil. Treatments involved mixing (weight basis) 1% TiO2 (T1), 5% biochar (T2), 1% TiO2 + 5% biochar (T3) with soil, and an unamended soil (T4). An adsorption edge study was performed from pH 4 to 10 at the V concentration of 200 mg L−1. A standard vanadium (V) solution was prepared using NaVO3. An incubation study was conducted over 3 months with V‐contaminated soil at a rate of 200 mg kg−1, at 80% field capacity. At the end of the incubation period, the treated soils were subjected to V fractionation. X‐ray diffraction (XRD) patterns and FTIR spectra of TiO2 nanoparticles, biochar, uncontaminated soil, and four treated soils were obtained. The adsorption edge of V was below pH 4.6, suggesting reduced retention of V in alkaline soils. In the combined treatment, the adsorption edge was elevated by one unit compared to the control. V adsorption increased in TiO2, biochar, and combined TiO2+biochar treatments at 7%, 4%, and 20%, respectively, compared with the unamended soil (control) at a pH of ~7.6. Functional groups revealed the possibility of inner‐sphere and outer‐sphere adsorption mechanisms between vanadate and the mix amendment of TiO2+biochar. The labile V fraction decreased, and the nonlabile V fraction increased, significantly in the TiO2+biochar amended soil compared to the unamended soil. Applying a blend of biochar+TiO2 reduced the mobility of V in a neutral‐alkaline soil, thereby preventing it from contaminating the nearby soil and water bodies.

Preparation and release pattern study of long-term controlled release Blonanserin microspheres

To prepare a PLGA microsphere loaded with the antipsychotic Blonanserin without release leg period and released in a near-zero model for long time, in this study, 15 kDa and 75 kDa PLGA were chosen to be mixed with different ratios, and Blonanserin microspheres (Bn-MS) without significant differences in the particle size, drug loading capacity, and encapsulation rate were prepared by microfluidics. The release kinetic model was fitted to the release behavior by monitoring the changes in particle size and morphology during the Bn-MS release to investigate microspheres’ in vitro release pattern. The results showed that the addition of appropriate ratios of mixed molecular weights to Bn-MS could eliminate the release hysteresis period. When the ratio of 15 kDa and 75 kDa was 1:9 (F3), the Bn-MS had a low burst release rate, moderate release rate, no release hysteresis period, a long release period of up to 35 days, and a stable release pattern close to the zero level. The results of the release mechanism study indicated that the hybrid PLGA improved the release behavior of the microspheres by adjusting the dissolution degradation rate of Bn-MS, which in turn affected the release mechanism of the microspheres.

The role of training variability for model-based and model-free learning of an arbitrary visuomotor mapping.

A fundamental feature of the human brain is its capacity to learn novel motor skills. This capacity requires the formation of vastly different visuomotor mappings. Using a grid navigation task, we investigated whether training variability would enhance the flexible use of a visuomotor mapping (key-to-direction rule), leading to better generalization performance. Experiments 1 and 2 show that participants trained to move between multiple start-target pairs exhibited greater generalization to both distal and proximal targets compared to participants trained to move between a single pair. This finding suggests that limited variability can impair decisions even in simple tasks without planning. In addition, during the training phase, participants exposed to higher variability were more inclined to choose options that, counterintuitively, moved the cursor away from the target while minimizing its actual distance under the constrained mapping, suggesting a greater engagement in model-based computations. In Experiments 3 and 4, we showed that the limited generalization performance in participants trained with a single pair can be enhanced by a short period of variability introduced early in learning or by incorporating stochasticity into the visuomotor mapping. Our computational modeling analyses revealed that a hybrid model between model-free and model-based computations with different mixing weights for the training and generalization phases, best described participants' data. Importantly, the differences in the model-based weights between our experimental groups, paralleled the behavioral findings during training and generalization. Taken together, our results suggest that training variability enables the flexible use of the visuomotor mapping, potentially by preventing the consolidation of habits due to the continuous demand to change responses.

Bootstrapped Edge Count Tests for Nonparametric Two-Sample Inference Under Heterogeneity

Nonparametric two-sample testing is a classical problem in inferential statistics. While modern two-sample tests, such as the edge count test and its variants, can handle multivariate and non-Euclidean data, contemporary gargantuan datasets often exhibit heterogeneity due to the presence of latent subpopulations. Direct application of these tests, without regulating for such heterogeneity, may lead to incorrect statistical decisions. We develop a new nonparametric testing procedure that accurately detects differences between the two samples in the presence of unknown heterogeneity in the data generation process. Our framework handles this latent heterogeneity through a composite null that entertains the possibility that the two samples arise from a mixture distribution with identical component distributions but with possibly different mixing weights. In this regime, we study the asymptotic behavior of weighted edge count test statistic and show that it can be effectively recalibrated to detect arbitrary deviations from the composite null. For practical implementation we propose a Bootstrapped Weighted Edge Count test which involves a bootstrap-based calibration procedure that can be easily implemented across a wide range of heterogeneous regimes. A comprehensive simulation study and an application to detecting aberrant user behaviors in online games demonstrates the excellent non-asymptotic performance of the proposed test. Supplementary materials for this article are available online.

Preparation of silver nanowires with controlled parameters for conductive transparent electrodes

Silver nanowires (AgNWs) have excellent flexibility, unique optical transmittance and high conductivity. The polyol process is appropriate for preparing AgNWs due to its simplicity, effectiveness, low cost, and high yield. This work aims to investigate the effect of preparation parameters of the polyol process on the silver nanowires properties. The parameters include the controlling agent, molecular weight of the polyvinylpyrrolidone (PVP), the temperature, and the reducing agent. The amount of silver nanoparticles formed during preparation was used to determine the optimum preparation conditions. The transmission electron microscope (TEM) images showed minimal amount of Ag nanoparticles when using mixed molecular weight of PVP-40K, and PVP-1.3M at 150 °C with the assistance of copper chloride as a controlling agent. The prepared AgNWs had an average length of 3.7 µm and aspect ratio of 15.3. The fabricated electrodes were characterized using a scanning electron microscope (SEM) and four probe resistivity measurements. The electrical measurement of the AgNWs electrodes indicated that the surfactant thickness is a critical parameter in having low sheet resistance electrodes. Also, the optical transmission was affected by the amount of nanoparticles. The prepared electrode with high concentration of AgNWs and a minimal amount of nanoparticles exhibited 80% optical transmission.

Characterization, stability, and skin application of astaxanthin particulates

PurposeAstaxanthin (AX), commonly used for dermal applications, exhibits anti-inflammatory and antioxidant activities; however, it has poor water solubility. In this study, we investigated the physicochemical properties of AX-containing particulates formulated using the amphiphilic graft copolymer Soluplus (polyvinyl caprolactam-polyvinyl acetate-polyethylene glycol graft copolymer: Sol) and polyethylene glycol-2000 (PEG 2000); in addition, the stability and skin applications of AX particulates were investigated.MethodsAX, Sol, and PEG were mixed by weight to prepare AX particles using the hydration method. The prepared particles were subjected to stability evaluations including particle size distribution, zeta potential estimation, and fluorescence spectroscopy as well as physical evaluations including 1H-1H NOESY NMR spectral measurement, powder X-ray diffraction, and differential scanning calorimetry. Functional evaluations included singlet oxygen scavenging, skin permeation test, and fluorescence microscopy.ResultsRelatively stable particles of Sol/AX and Sol/PEG 2000/AX, approximately 100 nm and 125 nm in size, respectively, were formed at a mixed weight ratio (9/1) of 0.1 M Ascorbic Acid solution (0.1 M ASC) and a mixed weight ratio (8/1/1) of 0.1 M ASC, respectively, at 25 °C after storage for 14 days under light-shielded condition. Stability evaluations revealed a decrease in fluorescence intensity and color fading for Sol/AX = 9/1 and Sol/PEG 2000/AX = 8/1/1 (dispersion medium: distilled water); however, no change in fluorescence intensity of AX was observed immediately after preparation in Sol/AX = 9/1 and Sol/PEG 2000/AX = 8/1/1 (dispersion medium: 0.1 M ASC). The fluorescence intensity of AX did not fluctuate significantly immediately after adjustment, and the particles remained stable, showing a bright orange color with time. NMR spectra of Sol/AX = 9/1 and Sol/PEG 2000/AX (dispersion medium: 0.1 M ASC) showed the interactions between the CH3 group e from Sol (1.8 ~ 2.0 ppm) and the CH groups H-15,11 from AX (6.7 ~ 6.8 ppm), 8’,12’ (6.4 ~ 6.5 ppm), H-10,14 (6.4 ~ 6.5 ppm), and 7,7’ (6.2 ~ 6.3 ppm), indicating the disappearance of cross peaks. Furthermore, new cross peaks were identified for the CH3 group e of Sol (1.8 ~ 2.0 ppm), the 7-membered ring z of Sol (1.5 ~ 1.8 ppm), the 5-membered ring S of ASC (3.5 ~ 3.6 ppm), the CH group T (3.8 ~ 3.9 ppm), and the CH group U (4.7 ppm). Fluorescence microscopy observations of microparticles formulated with Sol/PEG 2000/AX showed a slight improvement in skin penetration.ConclusionNew AX particulates were formed using Sol/PEG 2000/AX = 8/1/1, suggesting that Sol/PEG 2000/AX maintained the stability and improved the skin penetration of AX.

A dynamic generative model can extract interpretable oscillatory components from multichannel neurophysiological recordings.

Modern neurophysiological recordings are performed using multichannel sensor arrays that are able to record activity in an increasingly high number of channels numbering in the 100s to 1000s. Often, underlying lower-dimensional patterns of activity are responsible for the observed dynamics, but these representations are difficult to reliably identify using existing methods that attempt to summarize multivariate relationships in a post hoc manner from univariate analyses or using current blind source separation methods. While such methods can reveal appealing patterns of activity, determining the number of components to include, assessing their statistical significance, and interpreting them requires extensive manual intervention and subjective judgment in practice. These difficulties with component selection and interpretation occur in large part because these methods lack a generative model for the underlying spatio-temporal dynamics. Here, we describe a novel component analysis method anchored by a generative model where each source is described by a bio-physically inspired state-space representation. The parameters governing this representation readily capture the oscillatory temporal dynamics of the components, so we refer to it as oscillation component analysis. These parameters - the oscillatory properties, the component mixing weights at the sensors, and the number of oscillations - all are inferred in a data-driven fashion within a Bayesian framework employing an instance of the expectation maximization algorithm. We analyze high-dimensional electroencephalography and magnetoencephalography recordings from human studies to illustrate the potential utility of this method for neuroscience data.

Can Geopolymer Be a Replacement for Portland Cement in Full-Depth Reclamation Projects? Short- and Long-Term Performance Analysis

The fundamental concern of the full-depth reclamation (FDR) technique using Portland cement (FDR-PC) lies in its detrimental effect on the environment. Therefore, researchers are exploring alternative binders that are comparatively greener but compatible with FDR. This study evaluates both the strength and durability aspects of FDR incorporating geopolymer binder (FDR-GP) as an alternative to conventional FDR-PC. Various blends of fly ash (FA) and ground granulated blast furnace slag (GGBS) have been experimented with as geopolymer pre-cursors, ensuring that the additive content does not exceed 20% by weight of the total mix. Sodium hydroxide (NaOH) was utilized for the alkalinization in varying molarities to enhance the performance of the mixes. The experimental design was formulated to achieve a target 7-day unconfined compressive strength (UCS) strength of 4.95 MPa. This was obtained at a lower molarity (M) of 2 M and a 70% FA, 30% GGBS binder content. The mixes performed satisfactorily in both compression and flexure, along with excellent durability properties analyzed through wetting and drying tests. Furthermore, the long-term performance of FDR-GP mixes was investigated with regard to both UCS and flexural strength after 56, 161, and 238 days of curing. Thereafter, a comprehensive microstructural analysis was conducted to understand the fundamental changes in the matrix that influence mix performance. The outcome of this research confirms the potential of using FA/GGBS-based geopolymer as an alternative binder in FDR projects of flexible pavement, the mechanical and durability properties of which adequately satisfy the requirements as stipulated in various design standards.

Efficient capture of particulate matter with composite air filters comprising bamboo-derived nanofiber and base paper

Particulate-matter (PM) air filters comprising chemically synthesized polymers and petroleum-based materials are not environmentally sustainable because they do not biodegrade, causing secondary environmental pollution. To overcome this problem, we synthesized and analyzed the PM-capturing performance of composite sandwich-structured air filters containing bamboo-derived lignocellulose nanofibers and lignocellulose base paper. Bamboo pulp was produced through an organosolv process, and lignocellulose nanofibers and lignocellulose base paper were manufactured via electrospinning and paper processes, respectively. The manufacturing process was not only optimized via a rheological analysis of the precursor solution with different mixed solvent composition (6:4, 5:5, and 4:6 of 1-ethyl-3-methylimidazolium acetate:dimethylformamide) and different lignocellulose weight percent (3.3, 4.0, and 4.6 wt%), but also via nanofiber characterization, including electrospinnability, diameter distribution, and basis weight. The base paper condition was optimized based on analyses of the air permeability, PM filtration efficiency, and strength according to the paper thickness of 0.38 ± 0.05 and 0.10 ± 0.05 mm. As the lignocellulose nanofibers and base paper effectively captured particles smaller and larger than 2.5 μm, respectively, the structure of their composite sandwich-structured air filter was optimized to improve its PM-capturing performance. The study results provide new insights into the manufacturing of highly efficient PM air filters suitable for practical applications with diverse PM size distributions.

Isolation of Polyethylene Glycol with Larger Molecular Weights via Metal-Organic Frameworks.

Polymer products typically present as mixtures with a range of molecular weights, which notably influence the expression of their properties. In this study, a technique is proposed to separate polyethylene glycol (PEG) mixtures of varying molecular weights using metal-organic frameworks (MOFs), thereby narrowing down their molecular weight distribution. Due to the hydrogen bond interactions between PEG and -OH groups in the pores of NU-1000, NU-1000 can selectively adsorb PEG with larger molecular weights from PEG mixture. This separation method consistently yields with narrower molecular weight distribution across multiple cycles. This is the first application of MOFs in regulating the dispersity (Ð) of polymers in solution, providing a novel approach for separating and purifying mixed molecular weight polymers.

Application of Bottom Ash with Hydrated Lime in Pavement SubgradeConstruction

Introduction: A structurally strong subgrade is always desirable in pavement construction so as to provide a resilient and durable foundation for the subsequent pavement layers for adequate performance under the given traffic load/volume and environmental conditions. Emphasizing the increased awareness for effective waste recycling and demand for a sustainable construction approach, re-utilizing industrial wastes in pavement subgrade construction can be a viable option. The potential re-utilization of bottom ash (BA), an incombustible by-product generated in huge quantities from coal combustion in thermal power plants, in replacing natural soil in the subgrade construction of pavements will not only limit the extent of disturbance to ecological balance through over-utilization of natural soil but also open an avenue for the reuse of bottom ash. Method: In this study, an effort was made to study the possible use of different percentage replacement of BA (0 to 60% with an increment of 15%) with natural soil with and without hydrated lime (5% by weight of soil-bottom ash mix) for applications in road subgrade. Laboratory investigations were conducted for basic soil characteristics, compaction properties, and strength evaluation through California bearing ratio (CBR) and unconfined compressive strength (UCS) tests. Result: The addition of 5% lime to the soil-bottom ash mixes decreased the plasticity index significantly. The highest MDD and the lowest OMC were found in soil with 45% bottom ash. The CBR results of control soil increased from 6.3% to 137.7% for soil-bottom ash mix containing 45% bottom ash and 5% hydrated lime, and the same combination showed a 104% increase in UCS. Conclusion: The observed results indicated that the coupled behavior of the bottom ash and hydrated lime treatment has effectively increased the soil's compaction and mechanical strength and its suitability for use in road subgrade construction.