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In this paper, we investigate the relationship between organizational culture (OC) profiles and the adoption of Industry 4.0 (I4.0) design principles. We surveyed 153 middle and senior managers from Brazil and India, whose manufacturing companies have been implementing I4.0. Participants provided answers regarding the perceived OC characteristics that prevail in their companies, which were assessed based on the Competing Values Framework. We also collected data on the adoption levels of I4.0 design principles, whose analysis was conducted through the utilization of multivariate data analysis techniques. Our results indicated that, depending on the I4.0 design principle, OC profiles (Clan, Adhocracy, Market and Hierarchy) may either corroborate or impair its adoption, allowing the verification of the hypothesized relationships. The understanding of the association between OC profiles and I4.0 design principles provides practitioners with arguments to identify possible problems during their digital transformation. As changes in OC require significant efforts and are usually time-consuming, companies that can anticipate those issues might face a smoother transition towards the Fourth Industrial Revolution, hence, obtaining competitive advantages. [ABSTRACT FROM AUTHOR]

Cervical cancer (CCa) is the fourth most prevalent and common cancer affecting women worldwide, with increasing incidence and mortality rates. Hence, early detection of CCa plays a crucial role in improving outcomes. Non-invasive imaging procedures with good diagnostic performance are desirable and have the potential to lessen the degree of intervention associated with the gold standard, biopsy. Recently, artificial intelligence-based diagnostic models such as Vision Transformers (ViT) have shown promising performance in image classification tasks, rivaling or surpassing traditional convolutional neural networks (CNNs). This paper studies the effect of applying a ViT to predict CCa using different image benchmark datasets. A newly developed approach (ViT-PSO-SVM) was presented for boosting the results of the ViT based on integrating the ViT with particle swarm optimization (PSO), and support vector machine (SVM). First, the proposed framework extracts features from the Vision Transformer. Then, PSO is used to reduce the complexity of extracted features and optimize feature representation. Finally, a softmax classification layer is replaced with an SVM classification model to precisely predict CCa. The models are evaluated using two benchmark cervical cell image datasets, namely SipakMed and Herlev, with different classification scenarios: two, three, and five classes. The proposed approach achieved 99.112% accuracy and 99.113% F1-score for SipakMed with two classes and achieved 97.778% accuracy and 97.805% F1-score for Herlev with two classes outperforming other Vision Transformers, CNN models, and pre-trained models. Finally, GradCAM is used as an explainable artificial intelligence (XAI) tool to visualize and understand the regions of a given image that are important for a model's prediction. The obtained experimental results demonstrate the feasibility and efficacy of the developed ViT-PSO-SVM approach and hold the promise of providing a robust, reliable, accurate, and non-invasive diagnostic tool that will lead to improved healthcare outcomes worldwide. [ABSTRACT FROM AUTHOR]

Objectives: To measure the efficiency of combining clinical, biochemical markers, and doppler studies (uterine and umbilical arteries) as values to predict fetal growth restriction. Methods: This randomized clinical cross-sectional research has been carried out at the ultrasound unit in the Department of Obstetrics and Gynecology and the laboratory unit, Faculty of Medicine, Zagazig University between 2017 and 2021. Results: One hundred twenty-four pregnant women who visited the antenatal clinic at the Department of Obstetrics and Gynecology with suspected clinical criteria for FGR were included. In our study, the incidence of positive FGR in high-risk patients was 24.5%. Abnormal biochemical markers (PAPPA=0.42 + Homocysteine =6.3) were 42.3% sensitive, 88.0% specific, and 70.8% accurate in the prediction of FGR. Also, a statistically significant relationship between FGR and uterine artery doppler is noted. However, a combination of uterine artery doppler (PI>1.57 + diastolic notching) and biochemical markers was 46.2% sensitive, 58.8% specific, and 55.7% accurate in the prediction of FGR. Finally, there is a highly statistically significant relationship between both FGR and umbilical artery Doppler at 29th-31st weeks. Conclusions: The incorporated model of screening in this study can be a beneficial method to identify patients at increased risk of FGR. The best finding in this study is that a combination of uterine artery and umbilical artery doppler was 88.5% sensitive, 96.3% specific and 94.3% accurate in the prediction of FGR. [ABSTRACT FROM AUTHOR]

Cervical cancer affects more than half a million women worldwide each year and causes over 300,000 deaths. The main goals of this paper are to study the effect of applying feature selection methods with stacking models for the prediction of cervical cancer, propose stacking ensemble learning that combines different models with meta-learners to predict cervical cancer, and explore the black-box of the stacking model with the best-optimized features using explainable artificial intelligence (XAI). A cervical cancer dataset from the machine learning repository (UCI) that is highly imbalanced and contains missing values is used. Therefore, SMOTE-Tomek was used to combine under-sampling and over-sampling to handle imbalanced data, and pre-processing steps are implemented to hold missing values. Bayesian optimization optimizes models and selects the best model architecture. Chi-square scores, recursive feature removal, and tree-based feature selection are three feature selection techniques that are applied to the dataset For determining the factors that are most crucial for predicting cervical cancer, the stacking model is extended to multiple levels: Level 1 (multiple base learners) and Level 2 (meta-learner). At Level 1, stacking (training and testing stacking) is employed for combining the output of multi-base models, while training stacking is used to train meta-learner models at level 2. Testing stacking is used to evaluate meta-learner models. The results showed that based on the selected features from recursive feature elimination (RFE), the stacking model has higher accuracy, precision, recall, f1-score, and AUC. Furthermore, To assure the efficiency, efficacy, and reliability of the produced model, local and global explanations are provided. [ABSTRACT FROM AUTHOR]

Projects are undertaken to serve the particular purposes of organizations. Many projects focus on having monetary benefits, but some projects are non-profit oriented and funded by international or national non-governmental organizations (NGOs) or donors to serve the development of communities. These funded projects are usually established in many disciplines including health, environment, economics, education, social and human rights in countries where the welfare of the population is deficient. From perspectives of project teams and sponsors, having a project-oriented approach allows more project control over their available resources and expected outcomes, especially long-term or sustainable outcomes. In this study, the term ‘sustainable outcomes’ refers to project activities and their outcomes that survive after the termination of external funding. Sustainable outcomes are derived from the maintenance of benefits generated from an initial project, the continuation of project activities within a new organization, and the maintenance of benefits realization. The sustainable outcomes are impacted by abilities to carry out both on¬going and long-term implementation. Therefore, it is important to ensure that personal competence in development projects contribute to sustainable outcomes. This study employs a correlation coefficient analyses to identify personal competence of project team members that contribute to achieving sustainable outcomes. Seventy-five respondents from development projects participated in the questionnaire survey. The research findings show that each of the sustainable outcome domain required different combinations of personal competence attributes.

Today’s organizations operate within rapid changes in economy and market conditions. Organizational agility (OA) provides organizations with the ability to adapt rapidly to internal and external changes productively and cost-effectively. Furthermore, the adaptive ability and real-time responsiveness of organizations are generally necessitated for projects and portfolios to reach maturity. While the characteristics of OA and portfolio management maturity (PfMM) have been studied in the existing literature, the relationships between these two variables have not been fully explored. This study employs canonical correlation analysis (CCA) not only to investigate relationships among the characteristics of OA and PfMM but also to present the status quo of the studied variables from Australian perspectives. The results of the analysis are graphically presented to identify the formation of each OA characteristics at different levels of PfMM. The research data was collected from 36 respondents in public and private Australian sectors. The research findings show diverse combinations of the OA characteristics constructed at different levels of PfMM. The results also identify specific OA characteristics that highly contribute to the highest level of PfMM. This allows organizations with limited resources to precisely concentrate on the characteristics that will improve performance and achieve PfMM.