A New Model for Determining Factors Affecting Human Errors in Manual Assembly Processes Using Fuzzy Delphi and DEMATEL Methods

Human errors (HEs) are common problems in manual assembly processes, impacting product quality and resulting in additional costs. Based on expert judgments, this study aims to identify the most significant factors affecting HEs in manual assembly processes and explore the cause-and-effect relationships among those factors. In order to achieve this objective, a proposed model is constructed using two types of Multi-Criteria Decision-Making (MCDM) techniques. Firstly, using two rounds of the fuzzy Delphi method (FDM), twenty-seven factors with an influence score of 0.7 or higher were found to have a major impact on HEs during manual assembly processes, with at least a 75% consensus among experts. After that, the twenty-seven factors affecting HEs were given to experts in a third round to analyze the cause-and-effect relationships among those factors using the fuzzy decision-making trial and evaluation laboratory (DEMATEL) method. In MCDM techniques, symmetry refers to an important property that can be used to find relationships between variables. It is based on the principle that the relative importance or preference between two variables should remain the same regardless of their positions or roles. Therefore, symmetry is a factor that MCDM approaches take into account to ensure that the relationships between variables are accurately represented, leading to more reliable decision-making outcomes. The reliability and normality of the surveying data were examined using the SPSS 22.0 software program. The study results revealed that training level, poor workplace layout, a lack of necessary tools, and experience were the major factors affecting HEs as root causes. Moreover, a failure to address the error-causing problem, unintentional unsafe acts, fatigue, and poor error visual perception were found to be effect (dependent) factors. The findings of this study can help organizations make better-informed decisions on how to reduce worker errors and interest in the factors that contribute to assembly errors and provide a good basis for reaching the quality of final assembled parts

Decline in ESBL Production and Carbapenem Resistance in Urinary Tract Infections among Key Bacterial Species during the COVID-19 Pandemic

The COVID-19 pandemic has led to significant changes in healthcare practices, including increased antibiotic usage. This study aimed to investigate the impact of the pandemic on the prevalence of extended-spectrum β-lactamase (ESBL) production and carbapenem resistance among key bacterial species causing urinary tract infections (UTIs). Conducted at King Fahad Medical City in Riyadh from January 2018 to December 2022, the study analyzed urine samples from 9697 UTI patients. Patients were categorized into ‘pre-COVID-19’ and ‘during COVID-19’ groups. Bacterial isolates were identified, and antimicrobial susceptibility testing was performed following guidelines. ESBL production was detected using the Double-Disc Synergy Test. Escherichia coli and Klebsiella pneumoniae were the main pathogens. During the pandemic, ESBL production decreased in E. coli by 1.9% and in K. pneumoniae by 6.0%. Carbapenem resistance also declined, with E. coli displaying a 1.2% reduction and K. pneumoniae and Pseudomonas aeruginosa displaying 10.7% and 7.9% reductions, respectively. Notably, logistic regression analysis revealed that the odds of ESBL presence were 10% lower during the COVID-19 pandemic (OR 0.91; 95% CI 0.83–0.99; p = 0.040), and there was a significant reduction in the odds of carbapenem resistance (OR 0.43; 95% CI 0.37–0.51; p < 0.001). This study reveals a significant decrease in ESBL production and carbapenem resistance among UTI pathogens during the COVID-19 pandemic, hinting at the impact of modified antibiotic and healthcare approaches. It emphasizes the need for persistent antimicrobial resistance surveillance and policy adaptation to address resistance challenges, offering key directions for future public health actions

Significant change in expression value at gene level was observed in 20/144 genes.

<p>Two different algorithms were used to measure expression values from Exon array data to support the results. AltAnalyze (1a) and Expression Console (1b) show complimentary results with maximum changes observed in BCAS1, INHBA, IL6 and MUC4 genes.</p

Integrated Exon Level Expression Analysis of Driver Genes Explain Their Role in Colorectal Cancer

<div><p>Integrated analysis of genomic and transcriptomic level changes holds promise for a better understanding of colorectal cancer (CRC) biology. There is a pertinent need to explain the functional effect of genome level changes by integrating the information at the transcript level. Using high resolution cytogenetics array, we had earlier identified driver genes by ‘Genomic Identification of Significant Targets In Cancer (GISTIC)’ analysis of paired tumour-normal samples from colorectal cancer patients. In this study, we analyze these driver genes at three levels using exon array data – gene, exon and network. Gene level analysis revealed a small subset to experience differential expression. These results were reinforced by carrying out separate differential expression analyses (SAM and LIMMA). ATP8B1 was found to be the novel gene associated with CRC that shows changes at cytogenetic, gene and exon levels. Splice index of 29 exons corresponding to 13 genes was found to be significantly altered in tumour samples. Driver genes were used to construct regulatory networks for tumour and normal groups. There were rearrangements in transcription factor genes suggesting the presence of regulatory switching. The regulatory pattern of AHR gene was found to have the most significant alteration. Our results integrate data with focus on driver genes resulting in highly enriched novel molecules that need further studies to establish their role in CRC.</p></div

Transcription factor genes showing significant change in their effect as represented by the change in number of outbound and inbound edges in tumour and normal samples.

<p>Transcription factor genes showing significant change in their effect as represented by the change in number of outbound and inbound edges in tumour and normal samples.</p

Driver Genes from GISTIC analysis showing more than two fold change in expression value as calculated by two different programs – AltAnalyze and Expression Console.

<p>N/A  =  No annotation for these genes were found in the analysis program.</p>a<p>Genes found to be differentially expressed in SAM/LIMMA analyses. CLDN7 and LOX genes were additional driver genes that were differentially expressed.</p>b<p>Genes found to have high splice index values.</p>c<p>Genes found to be eligible as biomarkers for colorectal cancer.</p><p>Driver Genes from GISTIC analysis showing more than two fold change in expression value as calculated by two different programs – AltAnalyze and Expression Console.</p

Differentially regulated genes found to have incoherent expression levels and genomic changes.

<p>AA  =  Fold change value as calculated by AltAnalyze program.</p><p>EC  =  Fold change value as calculated by Expression Console program.</p><p>TF  =  Transcription Factor. Unknown is the TF that is not found in the driver genes.</p><p>Differentially regulated genes found to have incoherent expression levels and genomic changes.</p

Flow diagram for Analysis Strategy.

<p>A)The entire analyses is categorized into fours stages from ‘Data Generation’ to ‘Network Analyses'. B) Analysis strategy using different programs is displayed in this diagram. There are three components of the analysis – Gene, Exon and Network handled by different programs. Gene level analyses are conducted using ‘Affymetrix, Expression/Transcriptome analysis console’ and ‘Tibco Spotfire’. Exon level analysis is carried out by ‘AltAnalyze’ and ‘Affymetrix power tools’. Network analyses employed ‘GENIE3’, ‘IPA’ and ‘Cytoscape’. ‘Nexus Copy Number’ is a program used in earlier studies to eventually generate a list of 144 driver genes.</p

Biomarker molecules among the differentially expressed genes.

<p>Fold change and p-values were calculated using Integromics biomarker suite.</p><p>Biomarker molecules among the differentially expressed genes.</p