Effectiveness of psychological capital on mistake management culture as a resource for learning in organization

Today, healthy organizations such as hospital have found out the importance of quality of work life (QWL) of their personnel. QWL direct to enhancement of job satisfaction and improvement the quality of services to patient hospital and high performance. One of factors that impact QWL is culture of mistake management when contribute different organization aspects such as QWL and cover its needs and finally promote job performance. These were results that we took from testing our hypothesizes based upon a sample of 207 nurses of four hospitals

Absorption in the wavemaker by Artificial neural networks (ANNs)

Wave-making theories are becoming available, but their applicability is limited to specific ranges of waves and wavemaker types. Machine learning can also be used to discover nonlinear functional relationships. As a result, based on machine learning, this paper proposes a simple and universal framework for generating and absorbing waves.This framework trains neural networks to determine the transfer function between the wavemaker's free-surface elevation and velocity. To increase the generalization ability of neural networks, penalty term and data augmentation techniques based on wave-making mechanisms are introduced, rather than pure data-driven.As a result, once the wavemaker has the target wave profiles in front of it, it can generate waves while also absorbing reflected waves. Analytical solutions are used to validate the simulated wave profiles and wave orbital velocities, demonstrating that the proposed framework is effective at eliminating the re-reflection wave.The validation for generating the solitary wave and the New Year's wave is then performed, indicating that the generated waves agree very well with the desired wave elevation. The proposed framework can help with wavemaker design in the future, and it does not require any complex theoretical derivation

Antimicrobial Activity of Calcium-Enriched Mixture Cement and Biodentine on Enterococcus faecalis: An in Vitro Study

Introduction: The main cause of endodontic failure is residual bacteria in the root canal system. Enterococcus faecalis (E. faecalis) is the predominant species isolated from infected root canals. This study aims to compare the antibacterial activity of calcium-enriched mixture (CEM) cement and Biodentine as root canal filling materials on E. faecalis. Methods and Materials: Seventy extracted human single-rooted teeth were prepared and infected with E. faecalis for 24 h. Specimens were randomly divided into control or experimental groups; the later were filled with either CEM cement or Biodentine. Dentinal samples were collected after 7 and 30 days and transferred to test tubes. After incubation, the number of colony forming units (CFUs) were counted and analyzed using the Kruskal-Wallis test, followed by the Mann Whitney U test. The level of significance was set at 0.05. Results: The reduction in mean CFU level of E. faecalis was significantly more in the presence of CEM cement at both time intervals (P<0.001). Compared to the positive control, Biodentine significantly reduced the mean CFU level only after 30 days (P<0.01). Conclusion: Although both biomaterials exerted antibacterial activity against E. faecalis, the CEM cement had more antibacterial activity than Biodentine.Keywords: Antibacterial Agent; Biodentine; Calcium-Enriched Mixture Cement; CEM Cement; Endodontics

A Hybrid AI Framework to Address the Issue of Frequent Missing Values with Application in EHR Systems: the Case of Parkinson’s Disease

Electronic health record (EHR) systems hold vast amounts of patient data that, when analyzed with explainable AI techniques and predictive analytics, can improve clinical decision support systems (CDSS). However, the volume of data, with millions of patient records and hundreds of features collected over time, presents significant challenges, including handling missing values. In this project, we introduce a framework that addresses the issue of incompleteness in EHR data, enabling researchers to select the most important variables at an acceptable level of missing data to develop accurate predictive models. We demonstrate the effectiveness of this framework by applying it to developing a CDSS for detecting Parkinson\u27s disease based on large EHR data. Parkinson\u27s disease is hard to diagnose, and even specialists\u27 diagnoses can be inaccurate; moreover, limited access to specialists in remote areas results in many undiagnosed patients. Our framework can be integrated into EHR systems or used as an independent tool by healthcare practitioners who are not necessarily specialists, bridging the gap in specialized care in remote areas. Our results show that the framework improves the accuracy of predictive models and identifies patients with Parkinson\u27s disease who might otherwise go undiagnosed

Fitness Landscape of Antibiotic Tolerance in Pseudomonas aeruginosa Biofilms

Bacteria in biofilms have higher antibiotic tolerance than their planktonic counterparts. A major outstanding question is the degree to which the biofilm-specific cellular state and its constituent genetic determinants contribute to this hyper-tolerant phenotype. Here, we used genome-wide functional profiling of a complex, heterogeneous mutant population of Pseudomonas aeruginosa MPAO1 in biofilm and planktonic growth conditions with and without tobramycin to systematically quantify the contribution of each locus to antibiotic tolerance under these two states. We identified large sets of mutations that contribute to antibiotic tolerance predominantly in the biofilm or planktonic setting only, offering global insights into the differences and similarities between biofilm and planktonic antibiotic tolerance. Our mixed population-based experimental design recapitulated the complexity of natural biofilms and, unlike previous studies, revealed clinically observed behaviors including the emergence of quorum sensing-deficient mutants. Our study revealed a substantial contribution of the cellular state to the antibiotic tolerance of biofilms, providing a rational foundation for the development of novel therapeutics against P. aeruginosa biofilm-associated infections

Functional recovery of sciatic nerve through inside-out vein graft in rats

AbstractObjectivePresent study aimed at further comprehensive functional, histomorphometrical and immunohistochemical assessment of peripheral nerve regeneration using rat sciatic nerve transection model.MethodsThe 10-mm rat sciatic nerve gap was created in rats. In control group nerve stumps were sutured to adjacent muscle and in treatment group the gap was bridged using an inside-out vein graft. In sham-operated group the nerve was manipulated and left intact. All animals underwent walking track analysis test 4, 8, and 12 weeks after surgery. Subsequently, muscle mass measurement was performed to assess reenervation, histological examination to observe the sciatic nerve regeneration morphologically and Immunohistochemistry to detect Schwann cells using anti S-100. Results were analyzed using a factorial ANOVA with two between-subjects factors. Bonferroni test for pairwise comparisons was used to examine the effect of treatments.ResultsFunctional analysis of myelinated nerve fibers showed that nerve function improved significantly in the time course in treatment group. However, quantitative morphometrical analysis of myelinated nerve fibers showed that there was no significant difference between 8 and 12 weeks in treatment group. Muscle weight ratio was bigger and weight loss of the gastrocnemius muscle was ameliorated by inside-out vein grafting. The position of positive immunohistochemical reactions further implied that regenerated axons and Schwann cell-like cells existed after vein grafting was performed, and was accompanied by the process of myelination and structural recovery of regenerated nerves.ConclusionFunctional analysis of peripheral nerve repair is far more reliable than quantitative morphometrical analysi