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Investigating the association between polyunsaturated fatty acids and osteomyelitis by Mendelian randomization
The association between vitamin D and restless legs syndrome following acute ischemic stroke
Characteristics of bacteria based self healing rubberized concrete for sustainable and durable construction
Abstract Developing longer-lifespan concrete with minimized surface cracking is crucial for sustainable construction. This study investigates self-healing rubberized concrete incorporating 15% recycled rubber waste as a sand replacement. To enhance strength and flexibility through crack closure, bacteria Sporosarcina Pasteurii and Rhizobium Leguminosarum were introduced at 20% of the water volume. Slump, compressive and flexural strength, SEM, and EDX were the tests performed to identify the effects of bacteria and rubber on the concrete characteristics. The results illustrated that the use of rubber as a partial replacement for sand significantly reduced concrete workability and mechanical performance, with slump, compressive strength, and flexural strength decreasing by up to 77%, 49%, and 47%, respectively. However, incorporating SpP and RL bacteria, particularly at concentrations of 1010 + 1010 and 1014 + 1010, effectively mitigated these negative effects. The improvement in compressive strength and flexural strength was up to 98.7% and 137.4%, respectively for mixture containing SpP and RL bacteria at concentration 1010 +1010 compared to mixture containing 15% rubber only. Complete crack self-healing was achieved in SHRC mixtures after 80 days. Microstructure analysis revealed that the formation of calcium carbonate in large quantities within the concrete matrix, which works to heal cracks and fill voids. Thus, using rubber with bacteria to heal cracks could be a cost-effective solution that helps to increase tire rubber recycling rates.
Multistability bifurcation analysis and transmission pathways for the dynamics of the infectious disease-cholera model with microbial expansion inducing the Allee effect in terms of Guassian noise and crossover effects
Pan-cancer analysis of GJB5 as a novel prognostic and immunological biomarker
Identification and quantification of muscular cocontraction for ankle rehabilitation through variational mode decomposition in surface electromyography
Integrated endogenous hormones and transcriptome analysis contribute to fruit development related gene mining in Eriobotrya japonica
Author Correction: Effects of climate-related disasters on loneliness, social support, social functioning, and social contacts: longitudinal analyses of impact and recovery
Association of acrylamide dietary intake with glycation and oxidative status biomarkers and intakes of advanced glycation end-products or alpha-dicarbonyls
SkinEHDLF a hybrid deep learning approach for accurate skin cancer classification in complex systems
An African swine fever vaccine-like variant with multiple gene deletions caused reproductive failure in a Vietnamese breeding herd
Prevalence of the Histological Characteristics in Various Teeth of Different Age-groups: An Observational In Vitro Study
Author Correction: Full-length RNA-Seq of the RHOH gene in human B cells reveals new exons and splicing patterns
Systematic Application of Hobo Twin-stage Approach to Rehabilitate Worn Dentition: A Case Report
Investigation of green supply chain management practices and sustainability in Indian manufacturing enterprises using a structural equation modelling approach
Abstract Green Supply Chain Management (GSCM) has gained increasing attention as a means of ensuring sustainable manufacturing and a sustainable society. This study examines the relationship between GSCM practices and top management performance to understand its effect on low-carbon performance, sustainable manufacturing, and sustainable society. Data were gathered from 389 top-, middle-, and lower-level managers working in bag-manufacturing firms in India. The data were analyzed using a structural equation modelling (SEM) approach. The findings indicate positive and significant relationships among the constructs, with "green product and product design" showing the most substantial influence on "top management performance" (β = 0.274, p < 0.001). This top management performance significantly boosts "low carbon performance" (β = 0.375, p < 0.001), which in turn positively impacts “sustainable manufacturing” (β = 0.283, p < 0.001) and “sustainable society” (β = 0.347, p < 0.001). The SEM model explained 20% of the variance in "top management performance," 19.5% in "low carbon performance," 16.1% in "sustainable society," and 14.7% in "sustainable manufacturing." The link between “low-carbon performance” and "Top Management Performance" is found to have a medium effect size, indicating a strong and discernible correlation between the two variables. In practical terms, an organization will likely make significant strides toward sustainability and carbon emissions reduction when senior management actively supports and implements these measures. This study highlights that adopting GSCM practices is limited to improving firm performance and goes beyond creating sustainable manufacturing and society. This study is in the exploratory stage and adopts a holistic approach to understand the impacts of GSCM practices. Further studies on GSCM practices should be conducted to gain deeper insights. The model provides a broader picture for manufacturers to develop a long-term vision while adopting GSCM practices for sustainable manufacturing and sustainability.
Are Salivary Cortisol Levels Elevated in Periodontitis Patients Experiencing Stress Compared to Those without Stress? A Systematic Review and Meta-analysis
Stability and robustness of kinetochore dynamics under sudden perturbations and stochastic influences
Evaluation of the Efficacy of Different Agents on Decontamination of Dental Implant Surface: An In Vitro Study
Adaptive dynamic prediction model of mining subsidence aided by measured data
Abstract Underground mining-induced surface subsidence adversely affects both the surface environment and the structures located above it. Accurately predicting the dynamic subsidence and deformation caused by underground mining is crucial when employing maintenance and remediation methods to mitigate these adverse effects, as it directly impacts the selection of maintenance strategies, timing, and volume assessments. In response to the limitations of traditional time function and parameter models in adapting to the dynamic changes of actual underground mining activities—resulting in low subsidence prediction accuracy—this paper presents an adaptive prediction model for dynamic subsidence supported by measured data and developed through programming. This model utilizes historically measured data on surface subsidence to derive optimal parameters for each historical period. By analyzing the trends in these parameters, it dynamically adjusts the parameter value for subsequent predictions, achieving high-precision prediction of the surface dynamic subsidence. Engineering case study results indicate significant variations in the optimal time function parameter values throughout the mining process. The estimated parameter values obtained through the extrapolative prediction method, supported by measured data, align closely with the optimal values. The average relative RMSE of predicted dynamic subsidence for each period is 4.3%, markedly lower than the 9.1% achieved by traditional prediction models. This enhancement significantly improves the accuracy of dynamic subsidence predictions due to underground mining and provides robust technical support for the maintenance and remediation of structures.