Shape Of Distribution Research Articles

A hierarchical constrained density regression model for predicting cluster‐level dose‐response

AbstractWith the advent of new alternative methods for rapid toxicity screening of chemicals comes the need for new statistical methodologies which appropriately synthesize the large amount of data collected. For example, transcriptomic assays can be used to assess the impact of a chemical on thousands of genes, but current approaches to analyzing the data treat each gene separately and do not allow sharing of information among genes within pathways. Furthermore, the methods employed are fully parametric and do not account for changes in distribution shape that may occur at high exposure levels. To address the limitations of these methods, we propose Constrained Logistic Density Regression (COLDER) to model expression data from different genes simultaneously. Under COLDER, the dose‐response function for each gene is assigned a prior via a discrete logistic stick‐breaking process (LSBP) whose weights depend on gene‐level characteristics (e.g., pathway membership) and atoms consist of different dose‐response functions subject to a shape constraint that ensures biological plausibility. The posterior distribution for the benchmark dose among genes within the same pathways can be estimated directly from the model, which is another advantage over current methods. The ability of COLDER to predict gene‐level dose‐response is evaluated in a simulation study and the method is illustrated with data from a National Toxicology Program study of Aflatoxin B1.

Impact of cocoa variety on physical and chemical properties, reconstitutability, and flowability of cocoa powders

Cocoa of the Criollo, Forastero, and Trinitario varieties grown in the same production area of the Republic of the Congo were simultaneously harvested and similar post-harvesting operations (fermentation in crates followed by sun-drying) were performed. Cocoa powders were then produced according the same process consisting in successive roasting, grinding, fat extraction by pressing, and ultrafine grinding of the defatted mass. In order to investigate the influence of cocoa variety, the main physical and chemical properties (proximate composition, pH, particle size and shape distributions) of powders were characterized and related to cocoa powders abilities to reconstitute and flow. The three varieties showed very close proximal compositions, with lipid contents varying between 21 and 23 % (w/w), which classifies them as defatted cocoa powders. Cocoa powders of all varieties were non-wettable, and not soluble (solubility index around 0.15 g/g), consistently with their high lipid content. The fatty, fibrous, and proteic nature of the product has an impact on the grinding process, resulting in non-wettable powders and particles of varying sizes and shapes. All powders had bimodal particle size distributions, with large particles around 500 μm and small ones around 25 μm. However, slight differences between varieties were observed for some shape and functional properties. Trinitario and Forastero cocoa particles were slightly less spherical. The Criollo cocoa powder was relatively more compressible than the Trinitario variety. No significant difference of flow properties was attributable to cocoa variety. Basic flowability energy of all cocoa powders were relatively high, ranging from 908 to 973 mJ, which could denote a low aptitude to flow that may be related to the probable softness and stickiness of particle surface due to the high lipid content of cocoa powders. Specific energy values required to make studied cocoa powders flow were in the range between 6.61 and 6.99 mJ/g, which corresponds to intermediate values indicating a moderate level of cohesion and thus a fair flowability. The flow parameters obtained from the shear cell test showed no significant difference of cohesion (comprised between 1.54 and 1.70 kPa), flow factor (ranging from 2.53 to 2.76), and internal angle of friction (between 33.4 and 33.9°) linked to cocoa variety. This may be explained by the impact of particle size, as higher particles are generally less cohesive due to their lower surface specific area implying less interparticular contact points. Apart from these slight differences, all varieties showed similar properties and thus could be blended instead of being sorted in the cocoa manufacturing process. This study permitted to highlight that the terroir, the method of harvesting and preparing the beans, and then producing the powder have probably more impact on the physical and chemical and functional properties of cocoa powders than their variety.

Learning facial structural dependency in 3D aligned space for face alignment

Facial structure's statistical characteristics offer pivotal prior information in facial landmark prediction, forming inter-dependencies among different landmarks. Such inter-dependencies ensure that predictions adhere to the shape distribution typical of natural faces. In challenging scenarios like occlusions or extreme facial poses, this structure becomes indispensable, which can help to predict elusive landmarks based on more discernible ones. While current deep learning methods do capture these landmark dependencies, it's often an implicit process heavily reliant on vast training datasets. We contest that such implicit modeling approaches fail to manage more challenging situations. In this paper, we propose a new method that harnesses the facial structure and explicitly explores inter-dependencies among facial landmarks in an end-to-end fashion. We propose a Structural Dependency Learning Module (SDLM). It uses 3D face information to map facial features into a canonical UV space, in which the facial structure is explicitly 3D semantically aligned. Besides, to explore the global relationships between facial landmarks, we take advantage of the self-attention mechanism in the image and UV spaces. We name the proposed method Facial Structure-based Face Alignment (FSFA). FSFA reinforces the landmark structure, especially under challenging conditions. Extensive experiments demonstrate that FSFA achieves state-of-the-art performance on the WFLW, 300W, AFLW, and COFW68 datasets.

Effect of SARS-CoV-2 shedding rate distribution of individuals during their disease days on the estimation of the number of infected people. Application of wastewater-based epidemiology to the city of Thessaloniki, Greece

During the COVID-19 pandemic, wastewater-based epidemiology has proved to be an important tool for monitoring the spread of a disease in a population. Indeed, wastewater surveillance was successfully used as a complementary approach to support public health monitoring schemes and decision-making policies. An essential feature for the estimation of a disease transmission using wastewater data is the distribution of viral shedding rate of individuals in their personal human wastes as a function of the days of their infection. Several candidate shapes for this function have been proposed in literature for SARS-CoV-2. The purpose of the present work is to explore the proposed function shapes and examine their significance on analyzing wastewater SARS-CoV-2 shedding rate data. For this purpose, a simple model is employed applying to medical surveillance and wastewater data of the city of Thessaloniki during a period of Omicron variant domination in 2022. The distribution shapes are normalized with respect to the total virus shedding and then their basic features are investigated. Detailed analysis reveals that the main parameter determining the results of the model is the difference between the day of maximum shedding rate and the day of infection reporting. Since the latter is not part of the distribution shape, the major feature of the distribution affecting the estimation of the number of infected people is the day of maximum shedding rate with respect to the initial infection day. On the contrary, the duration of shedding (total number of disease days) as well as the exact shape of the distribution are by far less important. The incorporation of such wastewater surveillance models in conventional epidemiological models - based on recorded disease transmission data- may improve predictions for disease spread during outbreaks.

Bessel–Bessel–Gaussian vortex laser beams

Abstract We obtain and investigate Bessel–Bessel–Gaussian vortex beams (BBG beams) with the complex amplitude being equal to a product of the Gaussian function with two Bessel functions, whose arguments are expressed as complicated radicals including the cylindrical coordinates and a free parameter that defines the shape of the intensity distribution. If this parameter is small, the intensity has the shape of an inhomogeneous ring. For larger values of this parameter, the intensity has the shape of two arcs or ‘crescents’, oriented by their concave sides to each other. The complex amplitude of such beams is derived in explicit form for an arbitrary distance from the waist. We demonstrate that the BBG beams rotate upon propagation anomalously fast: at a distance much shorter than the Rayleigh length, the intensity distribution is already rotated by almost 45°, whereas typically, the rotation angle of vortex Gaussian beams is equal to the Gouy phase. It is also shown that the parameter of the BBG beam allows controlling its topological charge (TC): when the parameter value is positive and increases, the beam TC also increases stepwise by an even number. Besides, we study two other similar vortex BBG beams: either with four local intensity maxima, lying on the Cartesian coordinates axes, or with one intensity maximum with a crescent shape, whose center is on the horizontal axis. The derived three new families of asymmetric vortex laser beams, whose complex amplitude is described by explicit analytical expressions at an arbitrary distance from the waist, extend the variety of laser beams that can be used for manipulating and rotating microparticles, free space data transmission, and in quantum informatics.

Why Infrastructure Studies for Journalism?

This article makes a case for the value of infrastructure studies in analyzing journalism’s evolving landscape. It argues that infrastructural thinking is valuable to understand the changing neighbourhood of journalism, encompassing not just newsrooms but also the broader sociotechnical systems and resources, values and practices shaping news production, distribution, and consumption. Through a careful reading of the literature on infrastructure thinking and its application to journalism studies, it highlights how infrastructural thinking has been used for new and conventional research objects from micro, meso and macro levels of the field. We identify four themes that journalism scholars have focused on: journalism institutions, platforms and platform power, adjacent institutions and applications, and sociotechnical apparatus. We argue that infrastructure studies provides a timely two-way lens to deconstruct the “always there” nature of prior journalism systems and how they shape both the scope of scholarly inquiry and journalism.

Local environmental factors drive distributions of ecologically-contrasting mosquito species (Diptera: Culicidae)

Mosquitoes are important vectors of disease pathogens and multiple species are undergoing geographical shifts due to global changes. As such, there is a growing need for accurate distribution predictions. Ecological niche modelling (ENM) is an effective tool to assess mosquito distribution patterns and link these to underlying environmental preferences. Typically, macroclimatic variables are used as primary predictors of mosquito distributions. However, they likely undervalue local conditions and intraspecific variation in environmental preferences. This is problematic, as mosquito control takes place at the local scale. Utilising high-resolution (10 × 10 m) Maxent ENMs on the island of Bonaire as model system, we explore the influence of local environmental variables on mosquito distributions. Our results show a distinct set of environmental variables shape distribution patterns across ecologically-distinct species, with urban variables strongly associated with introduced species like Aedesaegypti and Culexquinquefasciatus, while native species show habitat preferences for either mangroves, forests, or ephemeral water habitats. These findings underscore the importance of distinct local environmental factors in shaping distributions of different mosquitoes, even on a small island. As such, these findings warrant further studies aimed at predicting high-resolution mosquito distributions, opening avenues for preventative management of vector-borne disease risks amidst ongoing global change and ecosystem degradation.

Frequency Bias Causes Overestimation of Climate Change Impacts on Global Flood Occurrence

AbstractThe frequency change of 100‐year flood events is often determined by fitting extreme value distributions to annual maximum discharge from a historical base period. This study demonstrates that this approach may significantly bias the computed flood frequency change. An idealized experiment shows frequency bias exceeding 100% for a 50‐year base period. Further analyses using Monte Carlo simulations, mathematical derivations, and hydrological model outputs reveal that bias magnitude inversely relates to base period length and is weakly influenced by the generalized extreme value distribution's shape parameter. The bias, persisting across different estimation methods, implies floods may exceed local defenses designed based on short historical records more often than expected, even without climate change. We introduce a frequency bias adjustment method, which significantly reduces the projected rise in global flood occurrence. This suggests a substantial part of the earlier projected increase in flood occurrence and impacts is not attributable to climate change.

Effects of slit geometric parameters on spray characteristics of double-slit pintle injectors

Pintle injectors have garnered significant research attention in recent years, particularly for their applicability in reusable launch vehicles, owing to their wide thrust control range and excellent combustion stability. While research has explored the characteristics of pintle injectors in the context of developing these components for actual engine applications, studies focusing on the effects of design parameters on injector performance have been limited. This study investigated the effects of slit geometric parameters, specifically the blockage factor (BF) and slit area ratio (γ), on the spray characteristics of double-slit pintle injectors. Cold-flow tests were conducted using planar pintle injectors with water and ethanol as simulants. The spray angle and Sauter mean diameter (SMD) were measured using the shadowgraph technique, and the distribution of mass flow rate and mixture ratio was analyzed using a mechanical patternator. The experimental results revealed that two distinct streams were injected at different angles from each row of slits, resulting in a division of spray shape, SMD, and mass flow distribution into three regions based on the two streams. These spray angles, termed primary and secondary spray angles, were quantified as functions of the local momentum ratio, determined by BF and γ. To correlate the spray characteristics with combustion performance, mixing quality and a representative droplet size metric, the integral Sauter mean diameter (ID32), were presented. The study found that higher values of BF and γ corresponded to improved mixing quality.

3D osteocyte lacunar morphometry of human bone biopsies with high resolution microCT: From monoclonal gammopathy to newly diagnosed multiple myeloma

Osteocytes are mechanosensitive, bone-embedded cells which are connected via dendrites in a lacuno-canalicular network and regulate bone resorption and formation balance. Alterations in osteocyte lacunar volume, shape and density have been identified in conditions of aging, osteoporosis and osteolytic bone metastasis, indicating patterns of impaired bone remodeling, osteolysis and disease progression. Osteolytic bone disease is a hallmark of the hematologic malignancy multiple myeloma (MM), in which monoclonal plasma cells in the bone marrow disrupt the bone homeostasis and induce excessive resorption at local and distant sites. Qualitative and quantitative changes in the 3D osteocyte lacunar morphometry have not yet been evaluated in MM, nor in the precursor conditions monoclonal gammopathy of undetermined significance (MGUS) and smoldering multiple myeloma (SMM). In this study, we characterized the osteocyte lacunar morphology in trabecular bone of the iliac crest at the ultrastructural level using high resolution microCT in human bone biopsy samples of three MGUS, two SMM and six newly diagnosed MM. In MGUS, SMM and MM we found a trend for lower lacunar density and a shift towards larger lacunae with disease progression (higher 50 % cutoff of the lacunar volume cumulative distribution) in the small osteocyte lacunae 20–900 μm3 range compared to control samples. In the larger lacunae 900–3000 μm3 range, we detected significantly higher lacunar density and microporosity in the MM group compared to the MGUS/SMM group. Regarding the shape distribution, the MGUS/SMM group showed a trend for flatter, more elongated and anisotropic osteocyte lacunae compared to the control group. Altogether, our findings suggest that osteocytes in human MM bone disease undergo changes in their lacunae density, volume and shape, which could be an indicator for osteolysis and disease progression. Future studies are needed to understand whether alterations of the lacunae architecture affect the mechanoresponsiveness of osteocytes, and ultimately bone adaptation and fracture resistance in MM and its precursors conditions.

Sensor Systems for Measuring Force and Temperature with Fiber-Optic Bragg Gratings Embedded in Composite Materials

Currently, there is a lot of interest in smart sensors and integrated composite materials in various industries such as construction, aviation, automobile, medical, information technology, communication, and manufacturing. Here, a new conceptual design for a force and temperature sensor system is developed using fiber-optic Bragg grating sensors embedded within composite materials, and a mathematical model is proposed that allows one to estimate strain and temperature based on signals obtained from the optical Bragg gratings. This is important for understanding the behaviors of sensors under different conditions and for creating effective monitoring systems. Describing the strain gradient distribution, especially considering different materials with different Young’s modulus values, provides insight into how different materials respond to applied forces and temperature changes. The shape of the strain gradient distribution was obtained, which is a quadratic function with a maximum value of 1500 µ, with a maximum value at the center of the lattice and a symmetrically decreasing strain value with distance from the central part of the fiber Bragg grating. With the axial strain at the installation site of the Bragg grating sensor under applied force values ranging from 10 to 11 N, the change in strain was linear. As a result of theoretical research, it was found that the developed system with fiber-optic sensors based on Bragg gratings embedded in composite materials is resistant to external influences and temperature changes.

Average Carbon Number Analysis and Relationship with Octane Number and PIONA Analysis of Premium and Regular Gasoline Expended in Ecuador

The quality of fuel depends on its chemical composition, which influences engine performance. Gas chromatography, a cornerstone of global oil and fuel R&D, remains crucial for ensuring the quality of petroleum products and regulatory compliance. Scientists use the most accurate analysis (PIONA) as a tool derived from gas chromatography coupled with mass spectrometry to identify and quantify hydrocarbons that influence resistance to detonation, which is determined by the research octane number (RON). This study introduces the “average carbon number (ACN)”, calculated from the molar chemical composition of commercial gasoline samples sold in Ecuador (Extra gasoline and Súper gasoline). A quantitative comparison of the ACN with techniques applied using standardized international procedures reveals that the ACN characterizes gasoline samples by providing insight into the distribution shape of carbon graphs. A comprehensive statistical analysis demonstrates the potential usefulness of ACN in characterizing fuel composition, highlighting its relevance in broader fuel quality assessments without the need for carbon distribution plots.

Variation in Lip Shape and Aesthetics in the Young Female Population: A Statistical Atlas Study.

Background: The distribution of lip shapes in young females and how morphological variation relates to attractiveness are poorly defined. Objectives: We hypothesized that among young female lip images generated by a statistical atlas model, those with more full lips compared with those with less full lips would be perceived as more attractive as measured by anonymous survey participants. Method: A statistical atlas of lip morphology was created using photographs of 700 women aged 18-35 years. The average lip shape was determined by coregistering and averaging images. Morphological variation was analyzed using principal component analysis. The relationship between attractiveness and observed lip morphologies was assessed using publicly distributed surveys. Results: In total, 428 survey responses were obtained. We developed a statistical model of variation of lip shape in the population and its relationship to attractiveness. The most attractive lips were significantly fuller than the average shape in the population, with greater vertical height and surface area. Conclusion: A statistical atlas can provide a visual guide to variation in lip shape in the population. The most attractive lip shapes vary significantly from the population average, lending support to procedures that increase lip height and surface area.

Interplay of Kinetic and Thermodynamic Factors in the Stationary Composition of Vapor-Liquid-Solid IIIVxV1-x Nanowires.

Compositional control over vapor-liquid-solid III-V ternary nanowires based on group V intermix (VLS IIIVxV1-x NWs) is complicated by the presence of a catalyst droplet with extremely low and hence undetectable concentrations of group V atoms. The liquid-solid and vapor-solid distributions of IIIVxV1-x NWs at a given temperature are influenced by the kinetic parameters (supersaturation and diffusion coefficients in liquid, V/III flux ratio in vapor), temperature and thermodynamic constants. We analyze the interplay of the kinetic and thermodynamic factors influencing the compositions of VLS IIIVxV1-x NWs and derive a new vapor-solid distribution that contains only one parameter of liquid, the ratio of the diffusion coefficients of dissimilar group V atoms. The unknown concentrations of group V atoms in liquid have no influence on the NW composition at high enough levels of supersaturation in liquid. The simple analytic shape of this vapor-solid distribution is regulated by the total V/III flux ratio in vapor. Calculating the temperature-dependent desorption rates, we show that the purely kinetic regime of the liquid-solid growth occurs for VLS IIIVxV1-x NWs in a wide range of conditions. The model fits the data well on the vapor-solid distributions of VLS InPxAs1-x and GaPxAs1-x NWs and can be used for understanding and controlling the compositions of any VLS IIIVxV1-x NWs, as well as modeling the compositional profiles across NW heterostructures in different material systems.

Assessment, quantification and propagation of uncertainty in seismic life-cycle cost analysis

Seismic life-cycle cost analysis (SLCCA) is a comprehensive tool to estimate the expected lifetime economic losses due to seismic events in terms of present-day value and aids in lifetime investment planning and design decision-making. The state-of-the-art framework for SLCCA incorporates three essential components: seismic hazard analysis, seismic fragility curves and damage cost estimates. Due to different sources of uncertainty prevailing in each component, the SLCC estimates may vary substantially. However, most of the existing literature primarily focuses on the expected value of the SLCC, and uncertainties stemming from multiple sources are unaccounted for. To address this drawback, this study proposes a comprehensive approach to quantify and propagate multiple uncertainties and offers a visualization of the shape and nature of SLCC distribution. Furthermore, the application of the framework is demonstrated using a case study example of the reinforced concrete (RC) frame building. Results reveal that the SLCC distribution is multimodal due to multiple uncertainty sources and the expected SLCC values also shift significantly compared to the state-of-the-art approaches, highlighting the criticality of the uncertainty propagation. Lastly, a novel framework is proposed to rank uncertainty sources based on their relative influence that will aid stakeholders in informed decision-making under uncertainty.

Integrating supply and benefits of ecosystem services into ecological security pattern: A spatial flow perspective

Ecological security pattern (ESP) is an effective way to alleviate the conflict between social development and ecological conservation. Previous studies on ESP mostly identified ecological sources derived from local supply and demand of diverse ecosystem services (ESs), overlooking the spatial flow of ESs and thus limiting its significance in both local and regional scales. This study proposed a framework integrating ESs supply and benefit from a spatial flow perspective for ecological sources identification. After modifying resistance surface by nighttime light data and extracting ecological corridors by MCR model, the ESP was finally constructed and optimized for sustainable land use management in the Yangtze River Delta (YRD) in 2020. The findings indicated that ecological sources were predominantly concentrated in the southwestern forestland and central Taihu Lake region, accounting for 18.47 % of the total area in the YRD. Totally 1035 ecological corridors were extracted, including 45 main corridors with a distance of 4284.47 km, overall exhibiting a “one vertical and three horizontals” shape distribution. Finally, an ESP of “four areas and four lines” was constructed after optimization, and targeted suggestions were proposed for regional sustainability. This study introduced spatial flow perspective and ESs benefit into ESP construction, which provides a scientific basis for sustainable land use management in a regional integration perspective.

Utilizing radiomics and dosiomics with AI for precision prediction of radiation dermatitis in breast cancer patients

PurposeThis study explores integrating clinical features with radiomic and dosiomic characteristics into AI models to enhance the prediction accuracy of radiation dermatitis (RD) in breast cancer patients undergoing volumetric modulated arc therapy (VMAT).Materials and methodsThis study involved a retrospective analysis of 120 breast cancer patients treated with VMAT at Kaohsiung Veterans General Hospital from 2018 to 2023. Patient data included CT images, radiation doses, Dose-Volume Histogram (DVH) data, and clinical information. Using a Treatment Planning System (TPS), we segmented CT images into Regions of Interest (ROIs) to extract radiomic and dosiomic features, focusing on intensity, shape, texture, and dose distribution characteristics. Features significantly associated with the development of RD were identified using ANOVA and LASSO regression (p-value < 0.05). These features were then employed to train and evaluate Logistic Regression (LR) and Random Forest (RF) models, using tenfold cross-validation to ensure robust assessment of model efficacy.ResultsIn this study, 102 out of 120 VMAT-treated breast cancer patients were included in the detailed analysis. Thirty-two percent of these patients developed Grade 2+ RD. Age and BMI were identified as significant clinical predictors. Through feature selection, we narrowed down the vast pool of radiomic and dosiomic data to 689 features, distributed across 10 feature subsets for model construction. In the LR model, the J subset, comprising DVH, Radiomics, and Dosiomics features, demonstrated the highest predictive performance with an AUC of 0.82. The RF model showed that subset I, which includes clinical, radiomic, and dosiomic features, achieved the best predictive accuracy with an AUC of 0.83. These results emphasize that integrating radiomic and dosiomic features significantly enhances the prediction of Grade 2+ RD.ConclusionIntegrating clinical, radiomic, and dosiomic characteristics into AI models significantly improves the prediction of Grade 2+ RD risk in breast cancer patients post-VMAT. The RF model analysis demonstrates that a comprehensive feature set maximizes predictive efficacy, marking a promising step towards utilizing AI in radiation therapy risk assessment and enhancing patient care outcomes.

Self-consistent theory of cosmic ray penetration into molecular clouds: Relativistic case

We study penetration of interstellar cosmic rays (CRs) into molecular clouds surrounded by nonuniform diffuse envelopes. The present work generalizes our earlier model of CR self-modulation [Ivlev ; Dogiel ], in which the value for the envelope’s gas density where CRs excite MHD waves was treated as a free parameter. Now, we investigate the case where the density monotonically increases toward the center. Assuming that CRs are relativistic, we obtain a universal analytical solution which does not depend on the particular shape of gas distribution in the envelope, and self-consistently derive boundaries of the diffusion zone formed within the envelope, where CRs are scattered at the self-excited waves. The values of the gas density at the boundaries are found to be substantially smaller than those assumed in the earlier model, which leads to a significantly stronger modulation of penetrating CRs. We compute the impact of CR self-modulation on the gamma-ray emission and show that the results of our theoretical model are in excellent agreement with recent observations of nearby giant molecular clouds by Yang []. Published by the American Physical Society 2024

Performance Analysis Relevant to Primary Design Parameters of Pervious Concrete: A Critical Review

Pervious concrete (PC) is a structural element with environmental benefits. The industrial application is highly limited by restrictions in predicting performance owing to high uncertainties and issues in mass-producing with uniform characteristics. Primary performance indicators of PC are compressive strength, porosity, and permeability, where the porosity distribution and pore characteristics are crucial in its mechanical properties. Although compaction can improve uniformity in the properties of concrete, it is properly employed in PCs to ensure connectivity between pores and thereby enhance permeability. The compactability of fresh concrete predominantly depends on binder thickness dictated by aggregate-to-binder (A/B) ratio, water-to-binder (W/B) ratio, aggregate size, shape distribution of aggregates, and interfacial transition zone. In addition, the method of compaction, the compaction energy, and the distribution of compaction energy in the concrete matrix affect the above. The concrete compaction methods and their effectiveness vary between laboratory studies and field-scale installations. This state-of-the-art critical review of literature reviews the performance parameters of PC, compaction types and methods, compactability of PC, and models currently employed to optimize the mix design. It also highlights the potential trends for future studies to assist optimization of compaction in PC. The authors believe that this comprehensive review would assist professionals in developing a standard code of practice for using PC concrete.

Phase-field modelings of fracture investigate the influence of interfacial effects on damage and optimal material distribution in brittle inclusion-matrix structures

This present work uses the phase-field modelings to investigate the influence of interfacial effects on damage and mechanical behavior, as well as the optimal distribution of the inclusion shape within brittle inclusion-matrix structures in various typical cases. These two constituent phases in the structures are assumed to be either isotropic or anisotropic. To achieve these goals, this work will: (i) use the phase-field modelings either considering or neglecting interfacial debonding, and the anisotropic phase-field modeling; (ii) determine and incorporate the strain tensor orthogonal decompositions into each specific phase-field modeling to enhance the accuracy and effectiveness of the simulation methods; (iii) combine the phase-field modelings with the BESO topology optimization algorithm to analyze the influence of interfacial effects on relationship curves and the optimal distribution of the inclusion shape. Through proposed numerical examples, it is demonstrated that the interfacial effects strongly influence crack paths, behavior curves, and optimal material distribution in structures. When considering interfacial effects, cracks are almost unable to penetrate into the inclusion phase. However, when neglecting interfacial effects, cracks propagate into the inclusion phase. This reason makes the structure more difficult to damage than when considering the interfacial effects, as evidenced by greater peak load values in behavior curves and greater total fracture resistance of the material. Especially in the example of inclusion phase optimization, the total fracture resistance value of the case neglecting interfacial effects is more than 107.9% greater than that considering interfacial effects.