Mixing Weights Research Articles

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.

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

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.

A Fully-correlated Anisotropic Micrograin BSDF Model

We introduce an improved version of the micrograin BSDF model [Lucas et al. 2023] for the rendering of anisotropic porous layers. Our approach leverages the properties of micrograins to take into account the correlation between their height and normal, as well as the correlation between the light and view directions. This allows us to derive an exact analytical expression for the Geometrical Attenuation Factor (GAF), summarizing shadowing and masking inside the porous layer. This fully-correlated GAF is then used to define appropriate mixing weights to blend the BSDFs of the porous and base layers. Furthermore, by generalizing the micrograins shape to anisotropy, combined with their fully-correlated GAF, our improved BSDF model produces effects specific to porous layers such as retro-reflection visible on dust layers at grazing angles or height and color correlation that can be found on rusty materials. Finally, we demonstrate very close matches between our BSDF model and light transport simulations realized with explicit instances of micrograins, thus validating our model.

A Multi-Performance Reliability Evaluation Approach Based on the Surrogate Model with Cluster Mixing Weight

Kriging surrogate model has extracted extensive attention in reliability evaluation, owing to its excellent applicability and operability nowadays, which confronts with difficulties in balancing the efficiency and accuracy for complicated mechanical assets with multiple failure modes. Consequently, this paper devises a multi-performance reliability analysis approach within the surrogate model framework, particularly innovative in its use of cluster mixing weight. Specifically, high-value test points are selected to fit the surrogate model after sorting the samples referring to the corresponding values; then, a cluster-based active learning strategy is employed to accomplish rapid convergence, and the particle swarm algorithm is utilized to optimize relevant parameters. Afterwards, the mixing weight for every performance referring to the contributions to the final reliability is determined, and the failure probability is subsequently predicted. Furthermore, the superiority of the proposed approach with the clustering surrogate model and mixing weight, compared with traditional sampling as well as other surrogate models, has been verified via case studies, contributing to overcoming the multi-performance reliability analysis oriented to complicated mechanical assets.

Decentralized adaptive work package learning for personalized and privacy-preserving occupational health and safety monitoring in construction

Precision construction occupational health and safety (COHS) is a prerequisite for project success. Work package-based distributed monitoring shows a high capability for this purpose. However, a theoretical dilemma exists between larger work packages with greater technical efficiency and smaller ones with greater data privacy. This paper develops a decentralized adaptive work package (DAWP) learning model and blockchain for personalized COHS monitoring. The DAWP learning model is first formulated to form adaptive topologies to concatenate and share model parameters of work packages with their neighbors. DAWP learning can compute graphs using mixing weights and similarity to improve personalization. Then, studying blockchain can transform DAWP into a decentralized collaboration. Lastly, blockchain-DAWP (BC-DAWP) is evaluated by controlled experiments of multiple monitoring tasks. The results indicated that the BC-DAWP with lightweight models outperforms the proposed baselines in a personalized and privacy-preserving manner, which paves the way for the next-generation decentralized COHS monitoring.

SUSTAINABLE INVESTIGATION ON POND ASH-INDUCED COMPRESSED INTERLOCKING BRICKS

Investigating the sustainability of using pond ash in compressed interlocking bricks is a multi-faceted process that requires assessing environmental, economic, and social aspects. India churns out more than 200 million tonnes of untapped coal ash every year. Stone crusher plants and thermal power plants are exhibiting huge amounts of unutilized by-products which have started invading our environment negatively and could soon create hazardous impacts. This paper focuses on investigating a new emerging sustainable brick constituting all these waste substances. Unutilized stone dust from crushers of the Khordha Industrial Area and Ash (Pond ash and Fly ash) were collected from the NALCO Power Plant situated at Angul, Odisha, India. Pond ash Induced Compressed Interlocking Bricks (PAICIB) were fabricated having parent mix with pond ash & fly ash levels of 35% and 40% stone dust by weight, which was fixed after several trial mix experiments. Variation was done with the proportion of lime and cement to the parent mix. Compressive strength tests were conducted on day 7 and 28. The parent mix of 30cmx15cmx10cm PAICIB containing no cement and 10% hydrated lime by weight of parent mix sustained a compressive strength up to 5.5 N/mm2 (failure load of 204KN) and the water absorption was 17% after 28 days. The primary focus of this investigation is to utilize the waste materials for creating an eco-friendly brick that can meet the demand of the current rising population of populated countries like India. This work becomes a part of the “fly ash management and utilization mission” by NGT as the bricks are formed here using the disposed pond ash in rivers reducing water pollution too.

A modified EM-type algorithm to estimate semi-parametric mixtures of non-parametric regressions

Semi-parametric Gaussian mixtures of non-parametric regressions (SPGMNRs) are a flexible extension of Gaussian mixtures of linear regressions (GMLRs). The model assumes that the component regression functions (CRFs) are non-parametric functions of the covariate(s) whereas the component mixing proportions and variances are constants. Unfortunately, the model cannot be reliably estimated using traditional methods. A local-likelihood approach for estimating the CRFs requires that we maximize a set of local-likelihood functions. Using the Expectation-Maximization (EM) algorithm to separately maximize each local-likelihood function may lead to label-switching. This is because the posterior probabilities calculated at the local E-step are not guaranteed to be aligned. The consequence of this label-switching is wiggly and non-smooth estimates of the CRFs. In this paper, we propose a unified approach to address label-switching and obtain sensible estimates. The proposed approach has two stages. In the first stage, we propose a model-based approach to address the label-switching problem. We first note that each local-likelihood function is a likelihood function of a Gaussian mixture model (GMM). Next, we reformulate the SPGMNRs model as a mixture of these GMMs. Lastly, using a modified version of the Expectation Conditional Maximization (ECM) algorithm, we estimate the mixture of GMMs. In addition, using the mixing weights of the local GMMs, we can automatically choose the local points where local-likelihood estimation takes place. In the second stage, we propose one-step backfitting estimates of the parametric and non-parametric terms. The effectiveness of the proposed approach is demonstrated on simulated data and real data analysis.

Hardy type inequalities with mixed weights in cones

Hardy type inequalities with mixed weights in cones