Research on Rutting Model of Semi-Rigid Asphalt Pavement Based on Hamburg Rutting Test

In order to establish a more effective rutting model of semi-rigid asphalt pavement, after sampling on-site,the Hamburg rutting test was conducted to analyze the relationship between ambient temperature, load magnitude,number of load actions and rutting depth; Taking Shami model as a reference,the environmental temperature,load size,load times and asphalt thickness are taken as model parameters;the rutting prediction models of upper,middle and lower surfaces of semi-rigid asphalt pavement structure are established by multiple linear regression analysis,and the models are modified by 6 sections of 4 expressways.The model is used to test 8 sections of 5 expressways,the results show that the average error rate of the calculated value of the model is 15.16%,which is obviously lower than the average error rate of 27.32% of the calculated value of the rut model in the current standard.Therefore,the model has high accuracy and can provide theoretical guidance for the design and maintenance of semi-rigid asphalt pavement

SmartUnit: Empirical Evaluations for Automated Unit Testing of Embedded Software in Industry

In this paper, we aim at the automated unit coverage-based testing for embedded software. To achieve the goal, by analyzing the industrial requirements and our previous work on automated unit testing tool CAUT, we rebuild a new tool, SmartUnit, to solve the engineering requirements that take place in our partner companies. SmartUnit is a dynamic symbolic execution implementation, which supports statement, branch, boundary value and MC/DC coverage. SmartUnit has been used to test more than one million lines of code in real projects. For confidentiality motives, we select three in-house real projects for the empirical evaluations. We also carry out our evaluations on two open source database projects, SQLite and PostgreSQL, to test the scalability of our tool since the scale of the embedded software project is mostly not large, 5K-50K lines of code on average. From our experimental results, in general, more than 90% of functions in commercial embedded software achieve 100% statement, branch, MC/DC coverage, more than 80% of functions in SQLite achieve 100% MC/DC coverage, and more than 60% of functions in PostgreSQL achieve 100% MC/DC coverage. Moreover, SmartUnit is able to find the runtime exceptions at the unit testing level. We also have reported exceptions like array index out of bounds and divided-by-zero in SQLite. Furthermore, we analyze the reasons of low coverage in automated unit testing in our setting and give a survey on the situation of manual unit testing with respect to automated unit testing in industry.Comment: In Proceedings of 40th International Conference on Software Engineering: Software Engineering in Practice Track, Gothenburg, Sweden, May 27-June 3, 2018 (ICSE-SEIP '18), 10 page

Electrocardiogram of a Silver Nanowire Based Dry Electrode: Quantitative Comparison With the Standard Ag/AgCl Gel Electrode

Novel dry electrodes have promoted the development of wearable electrocardiogram (ECG) that is collected in daily life to monitor the ambulatory activity of heart status. To evaluate the performance of a dry electrode, it is necessary to compare it with the commercial disposable silver/silver chloride (Ag/AgCl) gel electrode. In this paper, a silver nanowire (AgNW)-based dry electrode was fabricated for noninvasive and wearable ECG sensing. Signals from the AgNW electrode and the Ag/AgCl electrode were simultaneously collected in two conditions: sitting and walking. Signal quality was evaluated in terms of ECG morphology, R-peak to R-peak interval, and heart rate variability analysis. Quantitative comparisons showed that the AgNW electrode could collect acceptable ECG waveforms as the Ag/AgCl electrode in both the sitting and walking conditions. However, the baseline drift and waveform distortions existed in the AgNW electrode, likely due to electrode motion. If the skin-electrode contact is improved, the dry electrode can be a promising substitute for the Ag/AgCl electrode

Rethinking Causal Relationships Learning in Graph Neural Networks

Graph Neural Networks (GNNs) demonstrate their significance by effectively modeling complex interrelationships within graph-structured data. To enhance the credibility and robustness of GNNs, it becomes exceptionally crucial to bolster their ability to capture causal relationships. However, despite recent advancements that have indeed strengthened GNNs from a causal learning perspective, conducting an in-depth analysis specifically targeting the causal modeling prowess of GNNs remains an unresolved issue. In order to comprehensively analyze various GNN models from a causal learning perspective, we constructed an artificially synthesized dataset with known and controllable causal relationships between data and labels. The rationality of the generated data is further ensured through theoretical foundations. Drawing insights from analyses conducted using our dataset, we introduce a lightweight and highly adaptable GNN module designed to strengthen GNNs' causal learning capabilities across a diverse range of tasks. Through a series of experiments conducted on both synthetic datasets and other real-world datasets, we empirically validate the effectiveness of the proposed module

Near infrared spectroscopy coupled with radial basis function neural network for at-line monitoring of Lactococcus lactis subsp. fermentation

AbstractIn our previous work, partial least squares (PLSs) were employed to develop the near infrared spectroscopy (NIRs) models for at-line (fast off-line) monitoring key parameters of Lactococcus lactis subsp. fermentation. In this study, radial basis function neural network (RBFNN) as a non-linear modeling method was investigated to develop NIRs models instead of PLS. A method named moving window radial basis function neural network (MWRBFNN) was applied to select the characteristic wavelength variables by using the degree approximation (Da) as criterion. Next, the RBFNN models with selected wavelength variables were optimized by selecting a suitable constant spread. Finally, the effective spectra pretreatment methods were selected by comparing the robustness of the optimum RBFNN models developed with pretreated spectra. The results demonstrated that the robustness of the optimal RBFNN models were better than the PLS models for at-line monitoring of glucose and pH of L. lactis subsp. fermentation

Can acoustic indices reflect the characteristics of public recreational behavioral in urban green spaces?

Acoustic indicators serve as an effective means of assessing the quality of urban green space soundscape. The informative, easy accessibility and non-invasive nature of acoustic monitoring renders it an excellent tool for studying the interaction among the natural environment, wildlife, and human activities. Urban green space is essential in the urban ecosystem and constitutes the primary location for public outdoor recreation. However, the existing methods for monitoring public recreational behavior, such as on-site observation, drone observation, or questionnaire interviews, require significant labor or professional expertise. All of these methods have their limitations, so there is still much to be researched in the acoustic indices and recreational behavior. As a result, the potential for using acoustic characteristics to monitor public recreational behavior remains underexplored. To address this gap, this study investigates the potential of 5 widely used acoustic indices and acoustic intensity for monitoring public recreational behavior: Acoustic Complexity Index (ACI), Acoustic Diversity Index (ADI), Acoustic Richness (AR), Normalized Difference Soundscape Index (NDSI), and Power Spectral Density (PSD). Data were collected from 35 monitoring points in urban green spaces during the opening hours (6:00–22:00) to analyze the relationship between these indices and public recreational behavior. The findings indicate that (1) ACI, ADI, and AR daily exhibited multi-peak daily variation characteristics similar to those of public recreational behavior, displaying a “W” shape, while NDSI exhibits opposite variation characteristics; (2) the spatial variation characteristics of ACI, ADI, and AR change in response to the green space, and these changes align with public recreational behavior; (3) the correlation analysis and generalized linear mixed model construction further demonstrate that acoustic indices are effective in capturing the dynamic activities of visitor behavior; and (4) PSD undergoes significant temporal dynamic changes along the frequency gradient, with different frequency intervals reflecting the activity information of different recreational behaviors. In conclusion, this research highlights the effectiveness of using acoustic indices to analyze both the spatial and temporal variation characteristics of public recreational behavior in urban green spaces. The results can provide valuable data support for the enhancement and renovation of urban green spaces

Iridescent Daytime Radiative Cooling with No Absorption Peaks in the Visible Range

Coatings for passive radiative cooling applications must be highly reflected in the solar spectrum, and thus can hardly support any coloration without losing their functionality. In this work, a colorful daytime radiative cooling surface based on structural coloration is reported. A designed radiative cooler with a bioinspired array of truncated SiO2 microcones is manufactured via a self-assembly method and reactive ion etching. Complemented with a silver reflector, the radiative cooler exhibits broadband iridescent coloration due to the scattering induced by the truncated microcone array while maintaining an average reflectance of 95% in the solar spectrum and a high thermal emissivity (ε) of 0.95, owing to the reduced impedance mismatch provided by the patterned surface at infrared wavelengths, reaching an estimated cooling power of ≈143 W m-2 at an ambient temperature of 25 °C and a measured average temperature drop of 7.1 °C under direct sunlight. This strong cooling performance is attributed to its bioinspired surface pattern, which promotes both the aesthetics and cooling capacity of the daytime radiative cooler

Determination of the 95% effective dose of remimazolam tosylate in anesthesia induction inhibits endotracheal intubation response in senile patients

Background and Purpose: The prevalence of elderly patients prompts anesthesiologists to determine the optimal dose of medication due to the altered pharmacokinetics and pharmacodynamics of this population. The present study aimed to determine the 95% effective dose (ED95) of remimazolam tosylate in anesthesia induction to inhibit endotracheal intubation-related cardiovascular reaction in frail and non-frail senile patients.Methods: A prospective sequential allocation dose-finding study of remimazolam tosylate was conducted on 80 elderly patients who received general anesthesia between May and June 2022 at the First Affiliated Hospital of Nanchang University. The initial dose was 0.3 mg/kg. The blood pressure and heart rate fluctuations during intubation were either <20% (negative cardiovascular response) or ≥20% (positive cardiovascular response). If positive, the dose of the next patient was increased by 0.02 mg/kg, while if negative, it was reduced by 0.02 mg/kg according to the 95:5 biased coin design (BCD). The ED95 and 95% confidence intervals (CIs) were determined using R-Foundation isotonic regression and bootstrapping methods.Results: The ED95 of remimazolam tosylate to inhibit the response during tracheal intubation was 0.297 mg/kg (95% CI: 0.231–0.451 mg/kg) and 0.331 mg/kg (95% CI: 0.272–0.472 mg/kg) in frail and non-frail senile patients, respectively.Conculation and Implications: The CI of the two groups overlap, and no difference was detected in the ED95 of remimazolam tosylate in inhibiting endotracheal intubation-related cardiovascular response in frail and non-frail senile patients. These results suggested that remimazolam tosylate is an optimal anesthesia inducer for all elderly patients.Clinical Trial Registration:https://www.chictr.org.cn, identifier ChiCTR2200055709

Development and validation of a PMA-qPCR method for accurate quantification of viable Lacticaseibacillus paracasei in probiotics

The effectiveness of probiotic products hinges on the viability and precise quantification of probiotic strains. This study addresses this crucial requirement by developing and validating a precise propidium monoazide combination with quantitative polymerase chain reaction (PMA-qPCR) method for quantifying viable Lacticaseibacillus paracasei in probiotic formulations. Initially, species-specific primers were meticulously designed based on core genes from the whole-genome sequence (WGS) of L. paracasei, and they underwent rigorous validation against 462 WGSs, 25 target strains, and 37 non-target strains across various taxonomic levels, ensuring extensive inclusivity and exclusivity. Subsequently, optimal PMA treatment conditions were established using 25 different L. paracasei strains to effectively inhibit dead cell DNA amplification while preserving viable cells. The developed method exhibited a robust linear relationship (R2 = 0.994) between cycle threshold (Cq) values and viable cell numbers ranging from 103 to 108 CFU/mL, with an impressive amplification efficiency of 104.48% and a quantification limit of 7.30 × 103 CFU/mL. Accuracy assessments revealed biases within ±0.5 Log10 units, while Bland–Altman analysis demonstrated a mean bias of 0.058 Log10, with 95% confidence limits of −0.366 to 0.482 Log10. Furthermore, statistical analysis (p = 0.76) indicated no significant differences between theoretical and measured values. This validated PMA-qPCR method serves as a robust and accurate tool for quantifying viable L. paracasei in various sample matrices, including pure cultures, probiotics as food ingredients, and composite probiotic products, thereby enhancing probiotic product quality assurance and contributing to consumer safety and regulatory compliance

Identification and quantification of viable Lacticaseibacillus rhamnosus in probiotics using validated PMA-qPCR method

The identification and quantification of viable bacteria at the species/strain level in compound probiotic products is challenging now. Molecular biology methods, e.g., propidium monoazide (PMA) combination with qPCR, have gained prominence for targeted viable cell counts. This study endeavors to establish a robust PMA-qPCR method for viable Lacticaseibacillus rhamnosus detection and systematically validated key metrics encompassing relative trueness, accuracy, limit of quantification, linear, and range. The inclusivity and exclusivity notably underscored high specificity of the primers for L. rhamnosus, which allowed accurate identification of the target bacteria. Furthermore, the conditions employed for PMA treatment were fully verified by 24 different L. rhamnosus including type strain, commercial strains, etc., confirming its effective discrimination between live and dead bacteria. A standard curve constructed by type strain could apply to commercial strains to convert qPCR Cq values to viable cell numbers. The established PMA-qPCR method was applied to 46 samples including pure cultures, probiotics as food ingredients, and compound probiotic products. Noteworthy is the congruity observed between measured and theoretical values within a 95% confidence interval of the upper and lower limits of agreement, demonstrating the relative trueness of this method. Moreover, accurate results were obtained when viable L. rhamnosus ranging from 103 to 108 CFU/mL. The comprehensive appraisal of PMA-qPCR performances provides potential industrial applications of this new technology in quality control and supervision of probiotic products