Exploring the relationship between blood platelet and other components utilizing count regression: A cross-sectional study in Bangladesh.

Background and Aims: Blood, vital for transporting nutrients and maintaining balance, comprises red blood cells, white blood cells, and platelets, each pivotal. Imbalances lead to issues-low red cells cause fatigue (anemia), high white cells hint at infection, low counts raise infection risks. Using trendy statistical approaches, investigating the complex link between platelet counts and numerous blood components. Our investigation, leveraging count regression approaches, revealed deep insights into the interaction between platelet counts and other important hematological markers. Methods: A cross-sectional study utilized data from 3120 individuals, including both male and female participants, who visited these hospitals between June 16, 2022 and December 17, 2022, to assess their blood samples through testing by using convenience non-parametric sampling framework. Platelet count was taken into account as a measure of outcome in this research. This specific study region was chosen for its easy accessibility, which helped the seamless execution of the data-gathering technique. Count regression, negative binomial regression, and quasi-Poisson regression techniques have been employed for examining relationship of the data sets. Results: Three different count regression models were utilized to assess the proper association between the response and the relevant covariates and we found negative binomial count regression model (Akaike information criterion = 76.55, Bayesian information criterion = 76.59, and deviance = 3.14) was providing comparatively better performance than others. Based on the chosen model we found white blood cell, erythrocyte sedimentation rate, and eosinophils are significant but neutrophil, monocyte, and lymphocyte are not significant. We have also gone through proper model adequacy checking for our selected model and we found enough evidence to justify our model. Conclusion: From the result, we found insightful remarks into the mechanisms involved in platelet production and regulation, which can aid in developing increased effective treatments and interventions to maintain optimal platelet levels and prevent health problems related to abnormal platelet counts.

Application of improved harmonic Poisson segmented regression model in evaluating the effectiveness of Kala-Azar intervention in Yangquan City, China.

Background: The Centre for Disease Control and Prevention in Yangquan, China, has taken a series of preventive and control measures in response to the increasing trend of Kala-Azar. In response, we propose a new model to more scientifically evaluate the effectiveness of these interventions. Methods: We obtained the incidence data of Kala-Azar from 2017 to 2021 from the Centre for Disease Control and Prevention (CDC) in Yangquan. We constructed Poisson segmented regression model, harmonic Poisson segmental regression model, and improved harmonic Poisson segmented regression model, and used the three models to explain the intervention effect, respectively. Finally, we selected the optimal model by comparing the fitting effects of the three models. Results: The primary analysis showed an underlying upward trend of Kala-Azar before intervention [incidence rate ratio (IRR): 1.045, 95% confidence interval (CI): 1.027-1.063, p < 0.001]. In terms of long-term effects, the rise of Kala-Azar slowed down significantly after the intervention (IRR:0.960, 95%CI:0.927-0.995, p = 0.026), and the risk of Kala-Azar increased by 0.3% for each additional month after intervention (ß1 + ß3 = 0.003, IRR = 1.003). The results of the model fitting effect showed that the improved harmonic Poisson segmental regression model had the best fitting effect, and the values of MSE, MAE, and RMSE were the lowest, which were 0.017, 0.101, and 0.130, respectively. Conclusion: In the long term, the intervention measures taken by the Yangquan CDC can well curb the upward trend of Kala-Azar. The improved harmonic Poisson segmented regression model has higher fitting performance, which can provide a certain scientific reference for the evaluation of the intervention effect of seasonal infectious diseases.

A Statistical Route to Robust SERS Quantitation Beyond the Single-Molecule Level.

Single-molecule surface-enhanced Raman spectroscopy (SM-SERS) holds great potential to revolutionize ultratrace quantitative analysis. However, achieving quantitative SM-SERS is challenging because of strong intensity fluctuation and blinking characteristics. In this study, we reveal the relation P = 1 - e-α between the statistical SERS probability P and the microscopic average molecule number α in SERS spectra, which lays the physical foundation for a statistical route to implement SM-SERS quantitation. Utilizing SERS probability calibration, we achieve quantitative SERS analysis with batch-to-batch robustness, extremely wide detection range of concentration covering 9 orders of magnitude, and ultralow detection limit far below the single-molecule level. These results indicate the physical feasibility of robust SERS quantitation through statistical route and certainly open a new avenue for implementing SERS as a practical analysis tool in various application scenarios.

A mechanistic modeling and estimation framework for environmental pathogen surveillance.

Environmental pathogen surveillance is a promising disease surveillance modality that has been widely adopted for SARS-CoV-2 monitoring. The highly variable nature of environmental pathogen data is a challenge for integrating these data into public health response. One source of this variability is heterogeneous infection both within an individual over the course of infection as well as between individuals in their pathogen shedding over time. We present a mechanistic modeling and estimation framework for connecting environmental pathogen data to the number of infected individuals. Infected individuals are modeled as shedding pathogen into the environment via a Poisson process whose rate parameter λt varies over the course of their infection. These shedding curves λt are themselves random, allowing for variation between individuals. We show that this results in a Poisson process for environmental pathogen levels with rate parameter a function of the number of infected individuals, total shedding over the course of infection, and pathogen removal from the environment. Theoretical results include determination of identifiable parameters for the model from environmental pathogen data and simple, explicit formulas for the likelihood for particular choices of individual shedding curves. We give a two step Bayesian inference framework, where the first step corresponds to calibration from data where the number of infected individuals is known, followed by an estimation step from environmental surveillance data when the number of infected individuals is unknown. We apply this modeling and estimation framework to synthetic data, as well as to an empirical case study of SARS-CoV-2 in environmental dust collected from isolation rooms housing university students. Both the synthetic data and empirical case study indicate high inter-individual variation in shedding, leading to wide credible intervals for the number of infected individuals. We examine how uncertainty in estimates of the number of infected individuals from environmental pathogen levels scales with the true number of infected individuals and model misspecification. While credible intervals for the number of infected individuals are wide, our results suggest that distinguishing between no infection and small-to-moderate levels of infection (≈10 infected individuals) may be possible, and that it is broadly possible to differentiate between moderate (≈40) and high (≈200) numbers of infected individuals.

Comparison of univariate and bivariate Poisson regression methods in the analysis of determinants of female schooling and fertility in Malawi.

Recent research has established existence of a correlation between women's education and fertility, suggesting that they share similar risk factors. However, in many studies, the two variables were analysed separately, which could bias the conclusions by undermining the apparent correlations of such paired outcomes. In this article, the univariate and bivariate Poisson regression models were applied to nationally representative sample of 24,562 women from the 2015-16 Malawi demographic and health survey to examine the risk factors of women's education levels and fertility. The R software version 4.1.2 was used for the analyses. The results showed that estimates from the bivariate Poisson model were consistent with those obtained from the separate univariate Poisson models. The sizes of estimates of coefficients, their standard errors, p-values, and directions were comparable in both bivariate and univariate Poisson models. Using either the univariate or bivariate Poisson model, it was found that the age of a woman at first sexual experience, her current age, household wealth index, and contraceptive usage were significantly associated with both the woman's schooling and fertility. The study further revealed that ethnicity, religion, and region of residence impacted education level only and not fertility. Similarly, marital status and occupation impacted fertility only and not education. The study also found that higher education levels were linked to a lower number of children, with a strong negative correlation of -0.62 between the two variables. The study recommends using bivariate Poisson regression for analysing paired count response data, when there is an apparent covariance between the outcome variables. The results suggest that efforts by policymakers to achieve the desired women's sexual and reproductive health in sub-Saharan Africa should be intertwined with improving women's and girls' education attainment in the region.

The Design and Optimality of Survey Counts: A Unified Framework Via the Fisher Information Maximizer.

Grouped and right-censored (GRC) counts have been used in a wide range of attitudinal and behavioural surveys yet they cannot be readily analyzed or assessed by conventional statistical models. This study develops a unified regression framework for the design and optimality of GRC counts in surveys. To process infinitely many grouping schemes for the optimum design, we propose a new two-stage algorithm, the Fisher Information Maximizer (FIM), which utilizes estimates from generalized linear models to find a global optimal grouping scheme among all possible N -group schemes. After we define, decompose, and calculate different types of regressor-specific design errors, our analyses from both simulation and empirical examples suggest that: 1) the optimum design of GRC counts is able to reduce the grouping error to zero, 2) the performance of modified Poisson estimators using GRC counts can be comparable to that of Poisson regression, and 3) the optimum design is usually able to achieve the same estimation efficiency with a smaller sample size.

Interactions between latent variables in count regression models.

In psychology and the social sciences, researchers often model count outcome variables accounting for latent predictors and their interactions. Even though neglecting measurement error in such count regression models (e.g., Poisson or negative binomial regression) can have unfavorable consequences like attenuation bias, such analyses are often carried out in the generalized linear model (GLM) framework using fallible covariates such as sum scores. An alternative is count regression models based on structural equation modeling, which allow to specify latent covariates and thereby account for measurement error. However, the issue of how and when to include interactions between latent covariates or between latent and manifest covariates is rarely discussed for count regression models. In this paper, we present a latent variable count regression model (LV-CRM) allowing for latent covariates as well as interactions among both latent and manifest covariates. We conducted three simulation studies, investigating the estimation accuracy of the LV-CRM and comparing it to GLM-based count regression models. Interestingly, we found that even in scenarios with high reliabilities, the regression coefficients from a GLM-based model can be severely biased. In contrast, even for moderate sample sizes, the LV-CRM provided virtually unbiased regression coefficients. Additionally, statistical inferences yielded mixed results for the GLM-based models (i.e., low coverage rates, but acceptable empirical detection rates), but were generally acceptable using the LV-CRM. We provide an applied example from clinical psychology illustrating how the LV-CRM framework can be used to model count regressions with latent interactions.

Poisson function and volume ratio of soft anisotropic materials under large deformations.

Accurate transverse deformation measurements are required for the estimation of the Poisson function and volume ratio. In this study, pure silicone and soft composite specimens were subjected to uniaxial tension, and the digital image correlation method was used to measure longitudinal and in- and out-of-plane transverse stretches. To minimize the effects of measurement errors on parameter estimation, the measured transverse stretches were defined in terms of the longitudinal stretch using a new formulation based on Poisson's ratios and two stretch-dependent parameters. From this formulation, Poisson functions and volume ratio for soft materials under large deformations were obtained. The results showed that pure silicone can be considered isotropic and nearly incompressible under large deformations, as expected. In contrast, Poisson's ratio of silicone reinforced with extensible fabric can exceed classical bounds, including negative value (auxetic behavior). The incompressibility assumption can be employed for describing the stress-stretch curve of pure silicone, while volume ratios are required for soft composites. Data of human skin, aortic wall, and annulus fibrosus from the literature were selected and analyzed. Except for the aortic wall, which can be considered nearly incompressible, the studied soft tissues must be regarded as compressible. All tissues presented anisotropic behavior.

Excellent Hole Mobility and Out-of-Plane Piezoelectricity in X-Penta-Graphene (X = Si or Ge) with Poisson's Ratio Inversion.

Recently, the application of two-dimensional (2D) piezoelectric materials has been seriously hindered because most of them possess only in-plane piezoelectricity but lack out-of-plane piezoelectricity. In this work, using first-principles calculation, by atomic substitution of penta-graphene (PG) with tiny out-of-plane piezoelectricity, we design and predict stable 2D X-PG (X = Si or Ge) semiconductors with excellent in-plane and out-of-plane piezoelectricity and extremely high in-plane hole mobility. Among them, Ge-PG exhibits better performance in all aspects with an in-plane strain piezoelectric coefficient d11 = 8.43 pm/V, an out-of-plane strain piezoelectric coefficient d33 = -3.63 pm/V, and in-plane hole mobility µh = 57.33 × 103 cm2 V-1 s-1. By doping Si and Ge atoms, the negative Poisson's ratio of PG approaches zero and reaches a positive value, which is due to the gradual weakening of the structure's mechanical strength. The bandgaps of Si-PG (0.78 eV) and Ge-PG (0.89 eV) are much smaller than that of PG (2.20 eV), by 2.82 and 2.47 times, respectively. This indicates that the substitution of X atoms can regulate the bandgap of PG. Importantly, the physical mechanism of the out-of-plane piezoelectricity of these monolayers is revealed. The super-dipole-moment effect proposed in the previous work is proved to exist in PG and X-PG, i.e., it is proved that their out-of-plane piezoelectric stress coefficient e33 increases with the super-dipole-moment. The e33-induced polarization direction is also consistent with the super-dipole-moment direction. X-PG is predicted to have prominent potential for nanodevices applied as electromechanical coupling systems: wearable, ultra-thin devices; high-speed electronic transmission devices; and so on.

Digital modelling method of coal-mine roadway based on millimeter-wave radar array.

The roadway space of coal mine is narrow, and the illumination is low and uneven. Dynamic mining is accompanied by dust and water mist. The accuracy and reliability of roadway data collected by vision and laser sensors are poor. Based on this, a digital modeling method of coal mine roadway based on millimeter-wave radar array is proposed. Firstly, aiming at the problem of complex environmental interference, combined with the characteristics of small amount of single frame data of millimeter-wave point cloud, a multi-layer filtering noise reduction processing and dynamic subgraph registration method of millimeter-wave point cloud is proposed to filter out interference points and realize single radar point cloud registration. Secondly, aiming at the problem that a single millimeter-wave radar cannot scan the complete roadway information at one time, combined with the characteristics of small elevation field of view of millimeter-wave radar, a millimeter-wave radar array acquisition system is built, and an improved iterative closest point (ICP) registration algorithm based on point cloud features is established to construct the roadway point cloud fusion model. Finally, aiming at the problem of uneven and sparse point cloud after array fusion, a Poisson surface reconstruction method based on point cloud density weighted interpolation is proposed to refine the roadway structure and realize the accurate reconstruction of digital roadway model. The experimental results show that the digital modeling method of millimeter-wave radar array can accurately obtain the information of roadway surrounding rock, the density of roadway point cloud is increased by 22.4%, and the average absolute error percentage of the width and height of the reconstructed roadway model is only 0.82% and 0.72%, which provides a new research method for the reconstruction of underground roadway and an important basis for the digital modeling method of coal mine roadway.

Quality Assessment of TikTok as a Source of Information About Mitral Valve Regurgitation in China: Cross-Sectional Study.

BACKGROUND: In China, mitral valve regurgitation (MR) is the most common cardiovascular valve disease. However, patients in China typically experience a high incidence of this condition, coupled with a low level of health knowledge and a relatively low rate of surgical treatment. TikTok hosts a vast amount of content related to diseases and health knowledge, providing viewers with access to relevant information. However, there has been no investigation or evaluation of the quality of videos specifically addressing MR. OBJECTIVE: This study aims to assess the quality of videos about MR on TikTok in China. METHODS: A cross-sectional study was conducted on the Chinese version of TikTok on September 9, 2023. The top 100 videos on MR were included and evaluated using quantitative scoring tools such as the modified DISCERN (mDISCERN), the Journal of the American Medical Association (JAMA) benchmark criteria, the Global Quality Score (GQS), and the Patient Education Materials Assessment Tool for Audio-Visual Content (PEMAT-A/V). Correlation and stepwise regression analyses were performed to examine the relationships between video quality and various characteristics. RESULTS: We obtained 88 valid video files, of which most (n=81, 92%) were uploaded by certified physicians, primarily cardiac surgeons, and cardiologists. News agencies/organizations and physicians had higher GQS scores compared with individuals (news agencies/organizations vs individuals, P=.001; physicians vs individuals, P=.03). Additionally, news agencies/organizations had higher PEMAT understandability scores than individuals (P=.01). Videos focused on disease knowledge scored higher in GQS (P<.001), PEMAT understandability (P<.001), and PEMAT actionability (P<.001) compared with videos covering surgical cases. PEMAT actionability scores were higher for outpatient cases compared with surgical cases (P<.001). Additionally, videos focused on surgical techniques had lower PEMAT actionability scores than those about disease knowledge (P=.04). The strongest correlations observed were between thumbs up and comments (r=0.92, P<.001), thumbs up and favorites (r=0.89, P<.001), thumbs up and shares (r=0.87, P<.001), comments and favorites (r=0.81, P<.001), comments and shares (r=0.87, P<.001), and favorites and shares (r=0.83, P<.001). Stepwise regression analysis identified "length (P<.001)," "content (P<.001)," and "physicians (P=.004)" as significant predictors of GQS. The final model (model 3) explained 50.1% of the variance in GQSs. The predictive equation for GQS is as follows: GQS = 3.230 - 0.294 × content - 0.274 × physicians + 0.005 × length. This model was statistically significant (P=.004) and showed no issues with multicollinearity or autocorrelation. CONCLUSIONS: Our study reveals that while most MR-related videos on TikTok were uploaded by certified physicians, ensuring professional and scientific content, the overall quality scores were suboptimal. Despite the educational value of these videos, the guidance provided was often insufficient. The predictive equation for GQS developed from our analysis offers valuable insights but should be applied with caution beyond the study context. It suggests that creators should focus on improving both the content and presentation of their videos to enhance the quality of health information shared on social media.

Auxetic Biomedical Metamaterials for Orthopedic Surgery Applications: A Comprehensive Review.

Poisson's ratio in auxetic materials shifts from typically positive to negative, causing lateral expansion during axial tension. This scale-independent characteristic, originating from tailored architectures, exhibits specific physical properties, including energy adsorption, shear resistance, and fracture resistance. These metamaterials demonstrate exotic mechanical properties with potential applications in several engineering fields, but biomedical applications seem to be one of the most relevant, with an increasing number of articles published in recent years, which present opportunities ranging from cellular repair to organ reconstruction with outstanding mechanical performance, mechanical conduction, and biological activity compared with traditional biomedical metamaterials. Therefore, focusing on understanding the potential of these structures and promoting theoretical and experimental investigations into the benefits of their unique mechanical properties is necessary for achieving high-performance biomedical applications. Considering the demand for advanced biomaterial implants in surgical technology and the profound advancement of additive manufacturing technology that are particularly relevant to fabricating complex and customizable auxetic mechanical metamaterials, this review focuses on the fundamental geometric configuration and unique physical properties of negative Poisson's ratio materials, then categorizes and summarizes auxetic material applications across some surgical departments, revealing efficacy in joint surgery, spinal surgery, trauma surgery, and sports medicine contexts. Additionally, it emphasizes the substantial potential of auxetic materials as innovative biomedical solutions in orthopedics and demonstrates the significant potential for comprehensive surgical application in the future.

Estimated Incidence of Hospitalizations Attributable to RSV Infection Among Adults in Ontario, Canada, Between 2013 and 2019.

INTRODUCTION: Adult respiratory syncytial virus (RSV) burden is underestimated due to non-specific symptoms, limited standard-of-care and delayed testing, reduced diagnostic test sensitivity-particularly when using single diagnostic specimen-when compared to children, and variable test sensitivity based on the upper airway specimen source. We estimated RSV-attributable hospitalization incidence among adults aged ≥ 18 years in Ontario, Canada, using a retrospective time-series model-based approach. METHODS: The Institute for Clinical Evaluative Sciences data repository provided weekly numbers of hospitalizations (from 2013 to 2019) for respiratory, cardiovascular, and cardiorespiratory disorders. The number of hospitalizations attributable to RSV was estimated using a quasi-Poisson regression model that considered probable overdispersion and was based on periodic and aperiodic time trends and viral activity. As proxies for viral activity, weekly counts of RSV and influenza hospitalizations in children under 2 years and adults aged 60 years and over, respectively, were employed. Models were stratified by age and risk group. RESULTS: In patients ≥ 60 years, RSV-attributable incidence rates were high for cardiorespiratory hospitalizations (range [mean] in 2013-2019: 186-246 [215] per 100,000 person-years, 3‒4% of all cardiorespiratory hospitalizations), and subgroups including respiratory hospitalizations (144-192 [167] per 100,000 person-years, 5‒7% of all respiratory hospitalizations) and cardiovascular hospitalizations (95-126 [110] per 100,000 person-years, 2‒3% of all cardiovascular hospitalizations). RSV-attributable cardiorespiratory hospitalization incidence increased with age, from 14-18 [17] hospitalizations per 100,000 person-years (18-49 years) to 317-411 [362] per 100,000 person-years (≥ 75 years). CONCLUSIONS: Estimated RSV-attributable respiratory hospitalization incidence among people ≥ 60 years in Ontario, Canada, is comparable to other incidence estimates from high-income countries, including model-based and pooled prospective estimates. Recently introduced RSV vaccines could have a substantial public health impact.

Association between precipitation and mortality due to diarrheal diseases by climate zone: A multi-country modeling study.

Background: Precipitation could affect the transmission of diarrheal diseases. The diverse precipitation patterns across different climates might influence the degree of diarrheal risk from precipitation. This study determined the associations between precipitation and diarrheal mortality in tropical, temperate, and arid climate regions. Methods: Daily counts of diarrheal mortality and 28-day cumulative precipitation from 1997 to 2019 were analyzed across 29 locations in eight middle-income countries (Argentina, Brazil, Costa Rica, India, Peru, the Philippines, South Africa, and Thailand). A two-stage approach was employed: the first stage is conditional Poisson regression models for each location, and the second stage is meta-analysis for pooling location-specific coefficients by climate zone. Results: In tropical climates, higher precipitation increases the risk of diarrheal mortality. Under extremely wet conditions (95th percentile of 28-day cumulative precipitation), diarrheal mortality increased by 17.8% (95% confidence interval [CI] = 10.4%, 25.7%) compared with minimum-risk precipitation. For temperate and arid climates, diarrheal mortality increases in both dry and wet conditions. In extremely dry conditions (fifth percentile of 28-day cumulative precipitation), diarrheal mortality risk increases by 3.8% (95% CI = 1.2%, 6.5%) for temperate and 5.5% (95% CI = 1.0%, 10.2%) for arid climates. Similarly, under extremely wet conditions, diarrheal mortality risk increases by 2.5% (95% CI = -0.1%, 5.1%) for temperate and 4.1% (95% CI = 1.1%, 7.3%) for arid climates. Conclusions: Associations between precipitation and diarrheal mortality exhibit variations across different climate zones. It is crucial to consider climate-specific variations when generating global projections of future precipitation-related diarrheal mortality.

Quantitative Analysis of the Synergy of Doping and Nanostructuring of Oxide Photocatalysts.

In this paper, the effect of doping and nanostructuring on the electrostatic potential across the electrochemical interface between a transition metal oxide and a water electrolyte is investigated by means of the Poisson-Boltzmann model. For spherical nanoparticles and nanorods, compact expressions for the limiting potentials at which the space charge layer includes the whole semiconductor are reported. We provide a quantitative analysis of the distribution of the potential drop between the solid and the liquid and show that the relative importance changes with doping. It is usually assumed that high doping improves charge dynamics in the semiconductor but reduces the width of the space charge layer. However, nanostructuring counterbalances the latter negative effect; we show quantitatively that in highly doped nanoparticles the space charge layer can occupy a similar volume fraction as in low-doped microparticles. Moreover, as shown by some recent experiments, under conditions of high doping the electric fields in the Helmholtz layer can be as high as 100 mV/Å, comparable to electric fields inducing freezing in water. This work provides a systematic quantitative framework for understanding the effects of doping and nanostructuring on electrochemical interfaces, and suggests that it is necessary to better characterize the interface at the atomistic level.

Mechanical Properties of Rock Salt from the Klodawa Salt Dome-A Statistical Analysis of Geomechanical Data.

Rock salt is a potential medium for underground storage of energy resources and radioactive substances due to its physical and mechanical properties, distinguishing it from other rock media. Designing storage facilities that ensure stability, tightness, and safety requires understanding the geomechanical properties of rock salt. Despite numerous research efforts on the behaviour of rock salt mass, many cases still show unfavourable phenomena occurring within it. Therefore, the formulation of strength criteria in a three-dimensional stress state and the prediction of deformation processes significantly impact the functionality of storage in salt caverns. This article presents rock salt's mechanical properties from the Klodawa salt dome and a statistical analysis of the determined geomechanical data. The analysis is divided into individual mining fields (Fields 1-6). The analysis of numerical parameter values obtained in uniaxial compression tests for rock salt from mining Fields 1-6 indicates an average variation in their strength and deformation properties. Upon comparing the results of Young's modulus (E) with uniaxial compressive strength (UCS), its value was observed with a decrease in uniaxial compressive strength (E = 4.19968·UCS2, R-square = -0.61). The tensile strength of rock salt from mining Fields 1-6 also exhibits moderate variability. An increasing trend in tensile strength was observed with increased bulk density (σt = 0.0027697·ρ - 4.5892, r = 0.60). However, the results of triaxial tests indicated that within the entire range of normal stresses, the process of increasing maximum shear stresses occurs linearly ((σ1 - σ3)/2 = ((σ1 + σ3)/2)·0.610676 + 2.28335, r = 0.92). A linear relationship was also obtained for failure stresses as a function of radial stresses (σ1 = σ3·2.51861 + 32.9488, r = 0.73). Based on the results, the most homogeneous rock salt was from Field 2 and Field 6, while the most variable rock salt was from Field 3.

Origami-Enhanced Mechanical Properties for Worm-Like Robot.

In recent years, the exploration of worm-like robots has garnered much attention for their adaptability in confined environments. However, current designs face challenges in fully utilizing the mechanical properties of structures/materials to replicate the superior performance of real worms. In this article, we propose an approach to address this limitation based on the stacked Miura origami structure, achieving the seamless integration of structural design, mechanical properties, and robotic functionalities, that is, the mechanical properties originate from the geometric design of the origami structure and at the same time serve the locomotion capability of the robot. Three major advantages of our design are: the implementation of origami technology facilitates a more accessible and convenient fabrication process for segmented robotic skin with periodicity and flexibility, as well as robotic bristles with anchoring effect; the utilization of the Poisson's ratio effect for deformation amplification; and the incorporation of localized folding motion for continuous peristaltic locomotion. Utilizing the high geometric designability inherent in origami, our robot demonstrates customizable morphing and quantifiable mechanical properties. Based on the origami worm-like robot prototype, we experimentally verified the effectiveness of the proposed design in realizing the deformation amplification effect and localized folding motion. By comparing this to a conventional worm-like robot with discontinuous deformation, we highlight the merits of these mechanical properties in enhancing the robot's mobility. To sum up, this article showcases a bottom-up approach to robot development, including geometric design, mechanical characterization, and functionality realization, presenting a unique perspective for advancing the development of bioinspired soft robots.

Experimental Study of Auxetic Structures Made of Re-Entrant ("Bow-Tie") Cells.

This article presents a study of metamaterial structures that exhibit auxetic properties. This unusual phenomenon of simultaneous orthogonal expansion of the metamaterial in tension, and vice versa in compression, with vertical and horizontal contraction, is explored for structures made of re-entrant unit cells. The geometry of such structures is analysed in detail, and the relationships are determined by the value of the Poisson's ratio. It is shown that the Poisson's ratio depends not only on the geometry of the unit cell but also on the degree of strain. Depending on the dimensions of the structure's horizontal and inclined struts, the limit values are determined for the angle between them. By creating physical structures made of re-entrant cells, it is demonstrated that the mechanism of change in the structure's dimensions is not due to the hinging but to the bending of the struts. The experimental section contains the results of compression tests of a symmetrical structure and tensile tests of a flat mesh structure. In the case of the mesh structure, a modification of the re-entrant cells was used to create arched strut joints. This modification makes it possible to obtain greater elongation of the mesh structure and larger NPR values.

Stress Wave Propagation in a Rayleigh-Love Rod with Sudden Cross-Sectional Area Variations Impacted by a Striker Rod.

This paper presents an in-depth study of the stress wave behavior propagating in a Rayleigh-Love rod with sudden cross-sectional area variations. The analytical solutions of stress waves are derived for the reflection and transmission propagation behavior at the interface of the cross-sectional area change in the rod, considering inertia and Poisson's effects on the rod material. Examples solved using the finite element method are provided to verify the correctness of the analytical results. Based on the forward analysis of Rayleigh-Love wave propagation in a rod impacted by a striker rod, an impact-echo-type nondestructive testing (NDT) method is proposed to conduct defect assessment in rod-type structural components with sudden cross-sectional area changes within a cover medium. This proposed NDT method can identify the location, extension, and cross-sectional area drop ratios of an irregular zone in the rod to be inspected.

When the Poisson Ratio of Polymer Networks and Gels Is Larger Than 0.5?

We use coarse-grained molecular dynamics simulations to study deformation of networks and gels of linear and brush strands in both linear and nonlinear deformation regimes under constant pressure conditions. The simulations show that the Poisson ratio of networks and gels could exceed 0.5 in the nonlinear deformation regime. This behavior is due to the ability of the network and gel strands to sustain large reversible deformation, which, in combination with the finite strand extensibility results in strand alignment and monomer density, increases with increasing strand elongation. We developed a nonlinear network and gel deformation model which defines conditions for the Poisson ratio to exceed 0.5. The model predictions are in good agreement with the simulation results.