Mixture Proportions Research Articles

Sustainable soil stabilization of expansive soil subgrades through lime-fly ash admixture

Expansive soils, known for their significant volume changes with moisture variation, pose severe challenges for construction and pavement integrity. This study investigates the stabilization of expansive subgrade soils using a lime-fly ash mixture, aiming to enhance engineering properties and reduce associated risks. Soil samples from Nashik, Maharashtra, India, were analyzed for their geotechnical properties, revealing high plasticity and expansive nature. The study utilized different proportions of lime-fly ash mixtures to assess improvements in the stabilized soils' Unconfined Compressive Strength (UCS) and California Bearing Ratio (CBR). Laboratory tests, including granulometry, SEM, and XRF, indicated significant changes in the soil's physical and chemical composition. The stabilization process showed a marked reduction in the Free Swell Index (FSI) and swelling pressure attributed to flocculation. The optimum mix ratio of 1:4 (lime: fly ash) demonstrated the most substantial improvement, with UCS increasing to 224 kPa and CBR values reaching 8%. Additionally, the elastic modulus of the stabilized subgrade showed considerable enhancement, indicating better load-bearing capacity and durability. A flexible pavement design was implemented on both untreated and treated subgrades, demonstrating a 28% cost reduction for the stabilized section. This research underscores the effectiveness of using a sustainable lime-fly ash blend in mitigating the challenges posed by expansive soils, offering a cost-effective and environmentally friendly solution for road construction. The findings provide a robust framework for engineers to improve the stability and longevity of pavements on expansive soils.

Multi-objective optimization of engineered cementitious composite based on machine learning and generative adversarial network

This study aims to establish a novel framework for mixture design optimization of engineered cementitious composite (ECC) by first collecting two original datasets of ECC's tensile stress and strain from the extensive and credible literature. The datasets comprise a wide range of variables including cementitious ingredients, 9 types of fiber and characteristics, admixtures, and experimental conditions. The data augmentation is then performed using a tuned constraints-modified Conditional Tabular Generative Adversarial Network (Tuned-CTGAN) to increase the model accuracy and generalizability. The fitness functions of tensile stress and strain are established based on four machine learning models with the hyperparameters tuned by the Hunger Games search (HGS) algorithm. After the data augmentation, the values of R2 in their test sets are increased from 0.874 to 0.925 and from 0.772 to 0.889, respectively. Subsequently, the third objective function (cost) is computed by polynomials and four classes of constraints (Min-max, volume, ratio, and fiber) are set up to define the variable's search space. A non-dominated sorting genetic algorithm based on reference-point strategy (NSGA-III) is introduced to optimize the mixture proportions of ECC by simultaneously optimizing tensile stress, tensile strain, and cost. This paper combines the results of data augmentation, model prediction, and multi-objective optimization for complex ECC design, which aims to provide a basis for practical application.

Improving the experience of machine learning in compressive strength prediction of industrial concrete considering mixing proportions, engineered ratios and atmospheric features

In previous literature on predicting compressive strength (CS) using machine learning (ML), the focus has primarily been on algorithm-specific improvements, with less emphasis on improving the data perspective of CS prediction. Departing from these conventional investigations, this study presents data-centric enhancements. A systematic and comprehensive methodology is adopted, involving series of steps. Firstly, a large dataset (> 24,214 datapoints) comprising 26 input features relating to mixture proportions, engineered ratios and atmospheric parameters is prepared and processed. Feature engineering is then performed for selection and processing of data features. Subsequently, six scenarios are designed and investigated for CS prediction, focusing on distinct combinations of input features. Finally, CS predictions for all six scenarios are compared using five well-known ML model, and results are validated using statistical hypothesis testing. The results show significance improvements in CS prediction, with a 12.8 % increase in coefficient of determination (R2) and a 61 % reduction in root mean square error (RMSE) achieved by incorporating engineered and atmospheric parameters in conjunction with mixture proportion as inputs in CS prediction. Based on CS prediction in six scenarios, the study presents useful discussions focusing on concrete data perspective, role of engineering ratio and atmospheric features, and interaction and influence of input features in CS prediction. In conclusion, this work emphasizes the importance of enhancing data management practices for concrete performance assessment to have a more efficient, realistic, and sustainable outcomes in the concrete industry.

The geometry of admixture in population genetics: the blessing of dimensionality.

We present a geometry-based interpretation of the f-statistics framework, commonly used in population genetics to estimate phylogenetic relationships from genomic data. The focus is on the determination of the mixing coefficients in population admixture events subject to post-admixture drift. The interpretation takes advantage of the high dimension of the dataset and analyzes the problem as a dimensional reduction issue. We show that it is possible to think of the f-statistics technique as an implicit transformation of the genomic data from a phase space into a subspace where the mapped data structure is more similar to the ancestral admixture configuration. The 2-way mixing coefficient is, as a matter of fact, carried out implicitly in this subspace. In addition, we propose the admixture test to be evaluated in the subspace because the comparison with the conventional one provides an important assessment of the admixture model. The overarching geometric framework provides slightly more general formulas than the f-formalism by using a different rationale as a starting point. Explicitly addressed are 2- and 3-way admixtures. The mixture proportions are provided by suitable linear fits, in 2 or 3 dimensions, that can be easily visualized. The difficulties encountered with introgression and gene flow are also addressed. The developments and findings are illustrated with numerical simulations and real-world cases.

A hybrid technique adopted for study on the properties of alkali-activated binder concrete by using recycled concrete aggregates

ABSTRACT This manuscript presents a novel hybrid technique for assessing the geopolymer concrete’ properties incorporating recycled concrete aggregates. Named AO-GBDT, this hybrid approach combines the Aquila Optimizer (AO) and the Gradient Boosting Decision Tree algorithm. The primary objective is to reduce errors and predict the optimal strength of RCA geopolymers. The method investigates the interaction of key parameters and prediction values, like sodium silicate and sodium hydroxide to improve the binding materials. AO is specifically utilised for optimising the mixture proportions of RCA geopolymers, while GBDT is used to forecast the compressive strength of the optimised concrete mixtures. Additionally, the effects of fly ash (FA) and ground granulated blast furnace slag (GGBS) on the concrete’s hardened and fresh characteristics are investigated. Performance evaluation conducted in MATLAB demonstrates the effectiveness of the proposed system, which is contrasted to existing strategy. The proposed strategy exhibits a root-mean-square error of 1.8, a correlation coefficient (R) of 0.95 and a mean absolute error of 0.9, indicating its superior predictive accuracy and efficacy compared to existing approaches.

Study on mechanical properties and damage mechanism of alkali-activated slag concrete

The cement industry has become one of the important sources of greenhouse gas emissions, and the alkali-activated slag (AAS) has the same mechanical properties and lower carbon emissions as cement in the production process. Therefore, it can be used as an alternative binder for low-carbon buildings. The effect of alkali concentration (mass ratio of Na2O to binder) on the macro-mechanical properties and meso-damage mechanism of alkali-activated slag concrete (AASC) under uniaxial compression was investigated in this work. Six kinds of concrete with alkali concentration of 2 %, 4 %, 6 %, 8 %, 10 % and 12 % were prepared. The microscopic characteristics of AASC were characterized by nuclear magnetic resonance (NMR), scanning electron microscopy (SEM), x-ray diffraction (XRD) and x-ray energy dispersive spectroscopy (EDS); the meso-damage evolution mechanism of AASC was discussed by statistical damage theory. The results showed that the peak stress and elastic modulus of AASC specimens increased, and the microstructure became denser with the increase of alkali concentration. However, the mechanical properties of the specimen deteriorated to some extent when the alkali concentration exceeded 6 %; for example, the peak stress decreased by 24.00 MPa when the alkali concentration was increased from 6 % to 12 % at the age of 28d. Based on the statistical damage theory, the effective stress concept was introduced to quantitatively analyze the mesoscopic damage evolution of AASC. Both fracture and yielding damage modes were considered. By exploring the regular changes of the mesoscopic parameters, the connection between the macroscopic mechanical properties and the mesoscopic damage mechanism was established. Based on the above mechanical properties and mesoscopic damage results, the alkali concentration of 6 % can be used as the best mixture proportions of AASC.

Analisis Kandungan Material Campuran Aspal Panas Mix Design AC-WC terhadap Rekonstruksi Jalan Ruas N-029.1 Bts. Kab. Muaro Jambi / Kab. Tanjabtim-Bts. Kab. Tanjabbar

To obtain a good aggregate mixture, efforts are made to keep the gradation of the aggregate mixture in the middle of its specification range. Based on road reconstruction work on section N-029.1 Bts. Regency. Muaro Jambi / Kab. Tanjabtim – Bts. Regency. Tanjabbar is at the point (sta 16+000 – 16+400) where the asphalt that is spread has a soft/liquid texture, which can be seen from the process of compacting and leveling the asphalt layer which is carried out by operating with a number of passes that is not in accordance with what was planned during the trial mix. So it is necessary to carry out an analysis to evaluate the material composition of the hot mix asphalt design layered by Asphalt Concrete-Wearing Course (AC-WC). The method used is to use secondary data analysis methods in the form of DMF, JMF and gradation data. The results of the analysis of the composition of the hot asphalt mix material show that there are differences in the total proportion value of the mixture, where in the DMF the planned proportion is 32.97% for fine aggregate, 28.26% for medium aggregate, 18.84% for coarse aggregate, 1.88 % for filler, and 5.80% for asphalt. At JMF the proportion of results from weighing is 37.68% for Hot Bin I (fine aggregate), 35.80% for Hot Bin II (medium aggregate), 18.84% for Hot Bin III (coarse aggregate), 1.88% for filler, and 5.80% for asphalt. In field implementation, the proportion of the total mixture produced was 17.90% for Hot Bin III (coarse aggregate), 33.91% for Hot Bin II (medium aggregate), 40.51% for Hot Bin I (fine aggregate), 1, 88% for filler, and 5.81% for asphalt.

The mathematical model for the optimization of compressive strength of sawdust ash geopolymer concrete

Geopolymer concrete offers an eco-friendlier option compared to traditional cement, as it lowers greenhouse gas emissions during production. It consists of an alkaline solution with sodium or potassium silicate and sodium or potassium hydroxide, as well as a source material abundant in silica and alumina. For this study, thirty geopolymer concrete samples were produced in the lab using a mix design approach from Scheffe’s (5,2) model. The focus of the research was on maximizing the compressive strength of the concrete, especially when incorporating sawdust ash as the source material. The findings indicated that subjecting sawdust ash to pyrolysis in the absence of oxygen significantly impacts its pozzolanic properties and, consequently, the characteristics of the concrete. The research determined the optimal compressive strength of geopolymer concrete incorporating sawdust ash to be 21.673 MPa, along with specific concentration ratios of NaOH, Na2SiO4 to NaOH, sawdust ash in the binder, water to binder, and activator to sawdust ash at 10.5415, 2.0446, 38.6307, 0.0363, and 2.5882 respectively. Furthermore, MATLAB-based computer programs were utilized to optimize and forecast the ideal mixture proportions for sawdust ash-based geopolymer concrete.

Synthesis of geopolymer mortar from biomass ashes and forecasting its compressive strength behaviour

The global warming dilemma raised an exigency toward sustainability in the construction industry and compelled scientists to hunt for alternative binders in construction materials. The, geopolymerization as a spectacular technology has arisen as a looming solution to this dilemma. This work assesses the features of geopolymer mortar developed with both industrial and agricultural wastes at ambient temperature curing. The geopolymer mix proportion was evolved in this study using main precursor material as fly ash, ground granulated blast furnace slag (GGBFS), sugarcane bagasse ash (SCBA) or rice husk ash (RHA). Workability and mechanical properties, including compressive strength, flexural strength, and split tensile strength of different mortar mixes with various percentages of RHA and SCBA for 365 days, were evaluated. Geopolymer mortar with 10 % SCBA and 5 % RHA achieved improvements of 25 % and 13.91 % in compressive strength, 10.6 % and 3.5 % in flexural strength, and 35 % and 16.8 % in split tensile strength, respectively. Good geopolymer gel formation is clearly evident from the SEM images of SCBA mortar. The main polymeric bonds Si–O–Al and Si-O-Si are also identified using FTIR. To assess the efficacious use of biomass ashes, resistance to chemical attack using H2SO4, HCl, Na2SiO3, and NaCl solutions for 365 days was studied. It was revealed that SCBA incorporated geopolymer mortar specimens achieved good resistance compared with RHA geopolymer mortar. The relation between the ambient temperature cured geopolymer specimen compressive strength at 28 days and 365 days chemical solution curried geopolymer specimen compressive strength find out using python and obtained R2 values more than 95 %. Also, this research proposed various machine learning techniques such as long short-term memory, support vector regression, random forest regression, XGBOOST, and AdaBOOST algorithms for the prediction of compressive strength of geopolymer using 724 mixture proportions with diverse curing temperatures and varying ages. The XGBOOST algorithm achieved the highest R² value of 0.92, demonstrating its effectiveness for the strength prediction.

Understanding the synergistic geopolymerization mechanism of multiple solid wastes in ternary geopolymers

The use of ground granulated blast furnace slag (GBFS), fly ash (FA) and steel slag (SS) to form ternary geopolymers consumes several industrial solid wastes and produces high-performance construction materials. However, the unexplored synergistic geopolymerization mechanism of multiple solid wastes limits the design of high-performance ternary geopolymers. This study investigated the synergistic mechanism through testing the macroscopic performances such as unconfined compressive strength (UCS), final setting time (FST) and fluidity, and characterizing the microscopic structure of ternary geopolymers with different mixture proportions. In addition, the influence of variables on strength development of ternary geopolymers is analyzed by developing machine learning models. Results reveal that the highest strength of ternary geopolymers was achieved at a ratio of FA to GBFS to SS of 3:5:2, and alkali activator to precursor materials of 0.08. Addition of 20 % SS refines the pore network, reducing cumulative and detrimental pore volumes, thus enhancing compressive strength. In addition, the SS addition extends the heat generation peak during geopolymerization due to its slower reaction kinetics. Material components (Na/Al, Si/Al, Ca/Si) have a significant influence on UCS, with Na/Al ratio being the most sensitive variable. The study provides a new approach for utilization of bulk industrial wastes.

Pumping-less 3D concrete printing using quick nozzle mixing

Nozzle technologies for 3D concrete printing have been evolving to balance the conflicting demands of buildability and pumpability. This study introduces a simplified approach that eliminates the need for pumping by developing a customized system, the parameters of which were evaluated. The effect of process parameters (i.e., the flow rates of liquid and solid materials) and mixture proportions (i.e., the ratios of powder to sand and liquid to solid) on the extrudability and print quality was explored. Furthermore, the mechanical properties and microstructure of concrete printed using the Quick Nozzle Mixing system were examined. Results show that Quick Nozzle Mixing Technology not only enables high-quality printing but also exhibits the potential for achieving better process efficiency and higher aggregate content. The water-to-solid ratio is a critical factor influencing extrudability and buildability. Due to anisotropy caused by this new method, printed concrete has a lower strength than cast concrete.

Evaluation of meadow fescue grass cultivars seeded with alfalfa in New York state

AbstractAlfalfa–grass mixtures sown in the northeastern United States provide high‐quality dairy forage, and meadow fescue (Festuca pratensis Huds.) may improve the quality of these mixtures. Our objectives were to evaluate competitiveness and nutritive value of nine meadow fescue (MF) cultivars in New York State at spring harvest. Three farms, two in central New York State and one in northern New York state, were used. Conventional alfalfa (Medicago sativa L.) was sown (15 lb acre−1) to nine MF cultivars (three tetraploid and six diploid) and one tall fescue Lolium arundinaceum (Schreb.) ‘Darbysh’ cultivar in a randomized complete block design with four field replicates at each field site at three seeding rates (1, 2, and 3 lb acre−1). Grass proportion in mixtures was estimated visually. Grass samples were collected shortly before first harvest and analyzed for neutral detergent fiber, neutral detergent fiber digestibility (NDFD), acid detergent fiber, in vitro digestibility, and crude protein. Most meadow fescue cultivars maintained a grass proportion between 20%–45% across farms and growing seasons when seeded at 1lb acre−1. Seeding rates above 1lb acre−1 resulted in grass proportions above the recommended 20–30% grass proportion rate. Drought in early 2022 resulted in an average drop in grass percentage of 16.9% units for meadow fescue in mixtures, compared to 2021. Nutritive value of cultivars varied among farms and over growing seasons. Meadow fescue cultivars averaged 2.7% units higher NDFD than tall fescue, and cultivars with consistently high NDFD were Hidden Valley, SW Revansch, SW Minto, and Schwetra. Tetraploid cultivars averaged 4.0% units lower NDF compared to diploid cultivars, which is very advantageous for grass in alfalfa–grass mixtures.

Multi-objective optimization of cement-based systems containing marine dredged sediment

This study presents the integrated use of particle packing methodology and response surface methodology as an innovative mixture design for developing eco-friendly cement-based systems containing marine dredged sediment. The objective was to reduce the sand and cement contents of mortar mixtures for island applications, while maintaining the same properties as a reference mixture. The study examined three input variables for mixture design and optimization: sediment-to-total sand ratio ranging from 0.1 to 0.4, water-to-binder ratio ranging from 0.4 to 0.5, and cement paste content ranging from 0.35 to 0.45. As a result, three mixtures were developed based on three optimization objectives: (1) maintaining the same fresh and hardened properties as the reference (110 mm spread flow and 49.5 MPa strength); (2) maximizing the reduction of cement and sand contents with the use of a superplasticizer; and (3) maximizing the durability (as measured with the bulk electrical resistivity). The optimal mixture proportions showed reduced sand and cement contents up to 38 % and 15 %, respectively, with mechanical properties comparable to that of the reference. Moreover, capillary absorption and drying shrinkage of the optimized mixtures were reduced compared to the reference by up to 36 % and 16.5 %, respectively. It was evidenced that the combined use of mixture design methods can significantly contribute to the development of cementitious systems with balanced eco-efficiency and properties.

A new, standardized international Pacific Rim baseline for genetic stock identification (GSI) of Chinook Salmon

AbstractObjectiveGenetic stock identification (GSI) can be an effective tool for fisheries management, but development of reference baselines for species with broad geographic distributions can be challenging. Mixed‐stock fisheries for Chinook Salmon Oncorhynchus tshawytscha have utilized GSI analyses for decades with various genetic baselines, but these have largely become outdated with advances in technology that enable more efficient genotyping. Thus, our goals were to (1) create nested baselines of genotypic data for Chinook Salmon throughout their entire natural range using existing data from multiple sources and (2) evaluate the utility of those nested baselines to conduct accurate hierarchical GSI of mixture proportions or the stock identification of individual fish.MethodsIn this study, we compiled a large genetic baseline of single‐nucleotide polymorphism (SNP) markers for 389 populations that encompass the entire geographic range of Chinook Salmon. We used cross validation and realistic mixture simulations to test the accuracy of the baseline in generating GSI estimates.ResultWe demonstrated that a multi‐tiered assignment approach can provide high accuracy at both tier 1 (broadscale, with three coastwide reporting groups; 97.8% mean accuracy) and tier 2 (fine‐scale regional reporting groups; up to 97.7% mean accuracy) levels. Realistic mixture simulations showed that this multi‐tiered approach can provide highly effective GSI results for several common mixed‐stock fisheries applications in the Pacific Ocean.ConclusionThis new SNP baseline and the multi‐tiered assignment approach provide the most comprehensive rangewide GSI baseline for Chinook Salmon over any previous application and enable highly accurate estimates for GSI purposes.

Formulation of mixture proportions and experimental study of heavyweight self-compacting concrete based on magnetite and barite

AbstractThe present study aims to determine the mix proportion of binder, heavyweight aggregates, water-to-binder ratio, and additives to develop self-compacting concrete with a bulk density higher than 2600 kg m−3. It also aims to evaluate the engineering properties, pore structure, and microstructure of established heavyweight self-compacting concrete. Barite (BA), magnetite (MAG) or their mix (MIX) were used as fillers, while binder was composed of Portland cement, blast furnace slag, metakaolin, and limestone at a ratio of 65:15:5:15. Based on text results of V-funnel, S-Cone diameter and S-Cone time, the proportion mix and binder: filler: binder to cement ration was optimized as follows: 1) BA 1: 3.5: 0.42, 2) MAG 1: 4: 0.42, and 3) MIX 1: 3.75: 0.42 with maximal aggregate size not exceeding 2 mm. Not only the bulk density was influenced by aggregate, but also, the mechanical properties, shrinkage, dynamic modulus of elasticity pore structure, and microstructure were also found to be dependent on fillers.

Inference of Locus-Specific Population Mixtures from Linked Genome-Wide Allele Frequencies.

Admixture between populations and species is common in nature. Since the influx of new genetic material might be either facilitated or hindered by selection, variation in mixture proportions along the genome is expected in organisms undergoing recombination. Various graph-based models have been developed to better understand these evolutionary dynamics of population splits and mixtures. However, current models assume a single mixture rate for the entire genome and do not explicitly account for linkage. Here, we introduce TreeSwirl, a novel method for inferring branch lengths and locus-specific mixture proportions by using genome-wide allele frequency data, assuming that the admixture graph is known or has been inferred. TreeSwirl builds upon TreeMix that uses Gaussian processes to estimate the presence of gene flow between diverged populations. However, in contrast to TreeMix, our model infers locus-specific mixture proportions employing a hidden Markov model that accounts for linkage. Through simulated data, we demonstrate that TreeSwirl can accurately estimate locus-specific mixture proportions and handle complex demographic scenarios. It also outperforms related D- and f-statistics in terms of accuracy and sensitivity to detect introgressed loci.

Technosol made with urban and industrial waste: Potential for improving soil quality and growing tree seedlings

This study evaluated the use of waste materials to produce Technosol, a "tailor-made" soil consisting of construction and demolition waste (CDW), solid waste from stone mining (WSM), natural soil (NS), and compost from plant pruning (CPP), for improving soil quality in forest recovery projects. Samples of CDW were characterized using X-ray fluorescence and X-ray diffraction. The potential of the Technosol as a substrate was assessed using a mixture of "Class A" CDW, WSM, NS, and CPP in the following proportions: (a) 10 % CDW + 42.5 % WSM + 42.5 % NS + 5 % CPP, (b) 20 % CDW + 37.5 % WSM + 37.5 % NS + 5 % CPP, and (c) natural soil as a control. A randomized block design was used with nine treatments and three replications using 5.5 L pots. The experimental treatments consisted of two proportions of mixtures, natural soil, and three tree species: pioneer, Cecropia pachystachya, secondary, Handroanthus impetiginosus and climax, Copaifera langsdorffii. The activity of the enzymes β-glucosidase and arylsulfatase was determined in the Technosol and natural soil at the end of the experiment. During 180 days, the height of the plants and the diameter of the stem were determined at intervals of 30 days and, at the end of the experiment, the aerial part dry mass (leaves and trunk) and roots were evaluated. The Technosol constructed from the mixture (a) proved to be viable for improving soil quality, as indicated by a greater enzymatic activity compared to other soils, and for the growth of H. impetiginosus seedlings, showing the capacity to gain plant height over 180 days after planting.

Study on Improvement Measures for Construction Performance and Mechanical Properties of Cement Stabilized Porous Basalt

Abstract Cement stabilized porous basalt (CSPB) is prone to early agglomeration into lumps owing to the abundant pores and high moisture absorption of porous basalt, making it difficult to unload the mixture at construction sites in high temperatures and rainy areas. To study the construction performance of a CSPB mixture, a novel test method and a convenient device were proposed to test the workability of the cement stabilized macadam mixture, and the evaluation criteria and evaluation method of the workability were proposed based on the tilt angle test. The influencing factors of the workability of CSPB were investigated, and the results show that temperature, delay time, moisture absorption of aggregate, mixture proportion, and construction technology had significant influences on construction performance. Then measures were recommended to improve the construction performance of CSPB. Considering the influence of construction performance on mechanical strength, the effects of temperature, delay time, water reducer dosage, and cement dosage on the mechanical strength of CSPB were also investigated. The results show that temperature and delay time had significant influence on the mechanical strength. The results of this research are of great significance to the field construction of CSPB.

Drag reduction and degradation of binary polymer solutions

Polymer-induced drag reduction has yielded great potential benefits for industrial processes after more than 70 years of research. However, the limitation of low shear stability has hindered further applications. This study investigates the rheology, drag reduction rate (DR), and degradation of binary polymer mixtures comprising a rigid polymer (diutan gum, DG) and a flexible polymer (polyethylene oxide, PEO). The solutions all exhibited shear-thinning behavior, and the mixed solution was less viscous than the pure PEO or DG solutions at the total concentration of 100 ppm. When fixing the PEO concentration at 50 ppm, the mixed solution viscosity significantly increased with the DG concentration. The drag reduction performance of the pure PEO solution, pure DG solution, and various proportions of binary polymer mixtures was analyzed using an in-house rotor device. The DRs of the solutions increased with the Reynolds number (Re), and decreased with shearing time. The binary solution significantly improved the shear stability of the solution without loss of DR compared to the pure PEO solution. The theoretical model for molecular degradation in turbulent flow excellently fitted the experimental data of relative drag reduction with time. Furthermore, the synergistic interaction parameter was calculated, and it was positive for most cases in the mixtures. Additionally, when Re was fixed, the synergistic interaction parameter, related to the composition of binary polymer mixtures, initially decreased and then increased with time.

YADA - Reference Free Deconvolution of RNA Sequencing Data

Introduction: We present YADA, a cellular content deconvolution algorithm for estimating cell type proportions in heterogeneous cell mixtures based on gene expression data. YADA utilizes curated gene signatures of cell type-specific marker genes, either obtained intrinsically from pure cell type expression matrices or provided by the user. Method: YADA implements an accessible and extensible deconvolution framework uniquely capable of handling marker genes alone as inputs. Adoption barriers are lowered significantly by relying solely on literature-supported cell type-specific signatures rather than full transcriptomic profiles from purified isolates. However, flexible inputs do not necessitate sacrificing rigor - predictions match metrics of current methodologies through an integrated optimization scheme balancing multiple inference algorithms. Efficiency optimizations via compiled runtimes enable rapid execution. Packaging as an importable Python toolkit promotes community enhancement while retaining codebase extensibility. Result: Validation studies demonstrate that YADA matches or exceeds the performance of current deconvolution methods on benchmark datasets. To demonstrate the utility and enable immediate usage, we provide an online Jupyter Notebook implementation coupled with tutorials. Conclusion: YADA provides an accurate, efficient, and extensible Python-based toolkit for cellular deconvolution analysis of heterogeneous gene expression data.