Qualitative analysis of citations received by a scientific manuscript is a challenging task in the field of citation analysis. In most cases, the existing approaches that involve citations for the scientific impact evaluation normally employ a quantitative parameter, such as the number of received citations, while ignoring the qualitative feature, such as the context of citations, while, in reality, a received citation might hold positive feedback and negative or neutral feedback. In this study, a measure is purposed for the scientific evaluation of the articles based on the context of the citations named the context-based article impact factor (CBAIF). CBAIF not only considers the positive, negative, or neutral context of the citations but also involves the citing and cited author’s conflict-of-interest relationship for the evaluation of their scientific impact. With the help of experimentation, it is observed that CBAIF performs a fair ranking of articles based on citation’s context, whether it is cited positively or being criticized by some authors. Experimental results show that the CBAIF value with the context of citations revealed accurate results rather than the article impact factor (AIF) value without the context of citations.

The English-language Postpartum Specific Anxiety Scale (PSAS) is a valid, reliable measure for postpartum anxiety (PPA), but its 51-item length is a limitation. Consequently, the PSAS Working Group developed the PSAS Research Short-Form (PSAS-RSF), a statistically robust 16-item tool that effectively assesses PPA. This study aimed to assess and validate the reliability of an Arabic-language version of the PSAS-RSF in Jordan (PSAS-JO-RSF). Using a cross-sectional methodological design, a sample of Arabic-speaking mothers (N = 391) with infants aged up to 6 months were recruited via convenience sampling from a prominent tertiary hospital in northern Jordan. Factor analysis, composite reliability (CR), average variance extracted (AVE), McDonald's ω, and inter-item correlation measures were all examined. Explanatory factor analysis revealed a four-factor model consistent with the English-language version of the PSAS-RSF, explaining a cumulative variance of 61.5%. Confirmatory factor analysis confirmed the good fit of the PSAS-JO-RSF (χ2/df = 1.48, CFI = 0.974, TLI = 0.968, RMSEA = 0.039, SRMR = 0.019, p < 0.001). The four factors demonstrated acceptable to good reliability, with McDonald's ω ranging from 0.778 to 0.805, with 0.702 for the overall scale. The CR and AVE results supported the validity and reliability of the PSAS-JO-RSF. This study establishes an Arabic-language version of the PSAS-JO-RSF as a valid and reliable scale for screening postpartum anxieties in Jordan.

The transition from batch to continuous production in the catalytic hydrogenation of nitrile butadiene rubber (NBR) into hydrogenated NBR (HNBR) marks a significant advance for applications under demanding conditions. This study introduces a continuous process utilizing a static mixer (SM) reactor, which notably achieves a hydrogenation conversion rate exceeding 97%. We thoroughly review a mechanistic model of the SM reactor to elucidate the internal dynamics governing the hydrogenation process and address the inherent uncertainties in key parameters such as the Peclet number (Pe), dimensionless time (θτ), reaction coefficient (R), and flow rate coefficient (q). A comprehensive dataset generated from varied parameter values serves as the basis for training an artificial neural network (ANN), which is then compared against traditional models including linear regression, decision tree, and random forest in terms of efficacy. Our results clearly demonstrate the ANN’s superiority in predicting the degree of hydrogenation, achieving the lowest root mean squared error (RMSE) of 3.69 compared to 21.90 for linear regression, 4.94 for decision tree, and 7.51 for random forest. The ANN’s robust capability for modeling complex nonlinear relationships and dynamics significantly enhances decision-making, planning, and optimization of the reactor, reducing computational demands and operational costs. In other words, this approach allows users to rely on a single ML-based model instead of multiple mechanistic models for reflecting the effects of possible uncertainties. Additionally, a feature importance study validates the critical impact of time and element number on the hydrogenation process, further supporting the ANN’s predictive accuracy. These findings underscore the potential of ML-based models in streamlining and enhancing the efficiency of chemical production processes.