Browse Articles

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.

Evaluating the Biocompatibility of Novel Green-synthesized Nano-modified Glass Ionomer Cement: A Biochemical and Histopathological Analysis Study in Wistar Albino Rats

Electrodeposition of Cu Sn alloy coatings with enhanced corrosion resistance durability and self cleaning properties

Comparative Evaluation of 5% Pyrophosphate-containing Toothpaste with a Standard Fluoridated Toothpaste in the Inhibition of Calculus Formation: A Single-blind Randomized Controlled Clinical Trial

Publisher Correction: The intelligent fault identification method based on multi-source information fusion and deep learning

Evaluation of Frictional Resistance in Different Bracket Systems with Different Orthodontic Archwires: An In Vitro Study

Association between adverse childhood experiences and masculinity with well-being: moderating role of behavioural emotional regulation among men of three nations

Abstract The psychosocial aspects of men’s health and well-being have gained attention in the literature in recent years. However, evidence from developing countries is limited. Therefore, the present study attempted to understand the determining role of Adverse Childhood Experiences (ACEs) and masculinity on well-being factors, namely self-care and self-compassion among men, along with the moderating role of behavioral emotional regulation (BER) between masculinity and self-care. We adopted a cross-sectional study design. The data were collected from three countries, which are patriarchal societies, namely Ethiopia, India, and Oman, with a total sample size of 823 men between 18 and 45 years. Self-reported measures of the key variables were administered among the participants. We performed descriptive statistical analyses and path analysis. The ACEs were positively associated with masculinity (b = 1.544; 99% CI = 1.227–1.853), while it reduced the likelihood of self-compassion. Further, the increase in masculinity increased self-care (b = 0.195; 99% CI = 0.097- 0.295). However, the use of negative BER strategies reduced the likelihood of involvement in self-care (b=-1.185; 95% CI= -2.280- − 0.125) and changed the direction between masculinity and self-care (b=-0.644; 95% CI = − 0.988- − 0.279) acting as a moderator (b = 0.027; 95% CI = 0.003–0.051). The results suggest the importance of BER in effectively promoting self-care among men. Future self-care programs and interventions in the three nations should consider training men in BER. BER-focused interventions can facilitate positive coping among men and further enhance self-care and self-compassion.

Oral Health and the Role of Medical Social Workers: Still Necessary, and How should We Think about Their Role?

Risk assessment of phthalate ester exposure from various cheese types produced and consumed in Saudi Arabia

Osseointegration of Dental Implants in Irradiated Patients: A Bibliometric Study