The Role of Knowledge Control and Knowledge Asymmetry in Trusting and Collaborating with AI-Teammates

There is an extensive literature that facilitates our understanding of how new information technologies are adopted and accepted. However, there is little empirical work that studies how innovative technologies such as Artificial Intelligence (AI) agents can be team-players with humans in the workplace. Using Actor-Network Theory, this research-in-progress work proposes a new conceptual model that aims to aid our understanding of how human perceptions regarding the asymmetry they perceive between their knowledge and that of their AI teammates and their ability to retain control over the knowledge they share with AI teammates on their level of trust in AI teammates and their willingness to collaborate with them. A 2X2 scenario-based survey study will be conducted and structural equation modeling will be used to empirically validate this model. Potential contributions to theory and practice are discussed

Braiding Thermoplastic and Glass Fibers in Composite Dental Post Improves Their Mechanical Compatibility, In Vitro Experiment

Mechanical compatibility with the human dentin is a considerable issue when fabricating dental fiber posts. To this purpose, this study introduces a new method of fabricating compatible dental posts using braiding techniques of thermoplastic fibers (matrix) with glass fibers (reinforcement). Fifty fiber-reinforced composite (FRC) posts of thermoplastic yarns polypropylene (PP) braided with continuous filaments glass fibers (GFs) for reinforcement, varying in fiber volume fraction (FVF), and core types are fabricated and tested. Posts are performed using a braiding machine, and braids are placed in an aluminum mold. The filled mold is playced inside an oven at the melting temperature of the polypropylene to produce the final post’s shape. An ultrasonic test is conducted to measure the shear modulus and Young’s modulus of FRC post specimens by measuring the velocities of both the P-wave and S-wave. In order to ensure the accuracy of the measurements, each sample is measured three times, and then the means and standard deviations of each sample are calculated before analyzing the test results using the means of two steps, namely, clustering and comparing the P and R² values of each cluster, which revealed that FVF, fiber mass, and core type of the specimen had a significant effect on the resulted Young’s and shear modulus. The results indicate that the proposed method can fabricate competitive dental posts with regard to different fabricating variables. The samples show Young’s modulus ranges of from 10.08 GPa to 31.83 GPa. The following tested hypothesis is supported: the braiding technique of thermoplastic fibers with glass fibers will improve the mechanical compatibility of the resulting posts (ex vivo).Ministry of Science and Innovation, Spain grant numbers DPI2017-83859-R, PID2019-106947RA-C22(FEDER) EQC2018-004508-PMinistry of Health, Spain, grant number PI16/00339Junta de Andalucía IE2017-5537 and PI-0107-201Erasmus

Detection, Disease Severity and Chlorophyll Prediction of Date Palm Leaf Spot Fungal Diseases

Date palm leaves are infected with the fungal pathogens genus viz., Alternaria, Curvularia, Aspergillius and Neoscytalidium causing leaf spot diseases. The evaluation of chlorophyll content in the infected seedlings possibly could provide a good indicator for a degree of disease or infection, and changes during pathogenesis. Date palm seedlings at three-month-old were infected with 6 pathogenic fungal inoculums were tested. Disease severity% (DS%) and chlorophyll (Chl) contents using a single-photon avalanche diode (SPAD) meter were recorded at 15. 30 and 45 days after inoculation. Pearson's correlation analysis, Durbin Watson and regression analysis were performed to evaluate the relationship between the variables. It was found that the relationship between DS% with fungi, chlorophyll and days were in multiple regression models (R2 =91.88 and 91.87%, respectively). While, the relationship between chlorophyll with fungi, DS% and days were in multiple regression models (R2 =92.22 and 92.20%, respectively). The SPAD chlorophyll value could be considered as a better alternative over the DS% as the SPAD chlorophyll value was strongly related to DS%, as well as able to detect physiological changes in the infected date palm at the early stages of leaf spot pathogenesis. The aim of this study was to examine the possibility of the relationship between disease severity % with fungi, chlorophyll and days for the detection and quantification of date palm leaf spot diseases This is the first research study done to study the relationship between DS%, chlorophyll and time on date palm leaf spot fungal diseases

Advanced Diagnostic Technique for Alzheimer’s Disease using MRI Top-Ranked Volume and Surface-based Features

Background: Alzheimer’s disease (AD) is the most dominant type of dementia that has not been treated completely yet. Few Alzheimer‘s patients are correctly diagnosed on time. Therefore, diagnostic tools are needed for better and more efficient diagnoses. Objective: This study aimed to develop an efficient automated method to differentiate Alzheimer’s patients from normal elderly and present the essential features with accurate Alzheimer’s diagnosis.Material and Methods: In this analytical study, 154 Magnetic Resonance Imaging (MRI) scans were obtained from the Alzheimer’s Disease Neuroimaging Initiative (ADNI) database, preprocessed, and normalized by the head size for extracting features (volume, cortical thickness, Sulci depth, and Gyrification Index Features (GIF). Relief-F algorithm, t-test, and one way-ANOVA were used for feature ranking to obtain the most effective features representing the AD for the classification process. Finally, in the classification step, four classifiers were used with 10 folds cross-validation as follows: Gaussian Support Vector Machine (GSVM), Linear Support Vector Machine (LSVM), Weighted K-Nearest Neighbors (W-KNN), and Decision Tree algorithm. Results: The LSVM classifier and W-KNN produce a testing accuracy of 100% with only seven features. Additionally, GSVM and decision tree produce a testing accuracy of 97.83% and 93.48%, respectively.  Conclusion: The proposed system represents an automatic and highly accurate AD detection with a few reliable and effective features and minimum time

A guide for monitoring the effects of climate change on heritage building materials and elements

This report is concerned with advanced tools and methods for monitoring the effects of climate change in buildings. It addresses the expected changes, the effects on the fabric of a heritage building, and the mechanisms of deterioration. This will be addressed only using the data and measurements that is being collected as part of the HBIM process.This report was produced as a part of a Newton Fund-sponsored research project 'Heritage Building Information Modelling and Smart Heritage Buildings Performance Measurements for Sustainability

Awareness of Basic Life Support among Egyptian Medical Students; a Cross-Sectional Study

Introduction: It is important for all medical and paramedical staff to be aware of basic life support (BLS) maneuvers. In this study, we aimed to evaluate the level of BLS awareness among Egyptian medical students.Methods: The level of BLS knowledge was assessed using a validated questionnaire and the results were analyzed using an answer key, prepared from the Advanced Cardiac Life Support (ACLS) manual. We used the Student's t-test to analyze the association between awareness level and year of study, previous BLS training and practical experience.Results: A total of 823 medical students with the mean age of 20.3 ± 2.7 years, from Al-Azhar medical schools completed the questionnaire (463 and 360 in academic and clinical years, respectively). About 72% and 84% of students failed to recognize the proper point of chest compression in adults and infants, respectively. Moreover, the majority (80%) did not know how to give rescue breathing in infants. Only 18% of students correctly identified early signs of shock and only 22% knew how to help patients with myocardial infarction. Being in clinical years, previous BLS training or practical experience were significantly associated with higher BLS knowledge scores (p < 0.001).Conclusion: The level of BLS awareness among Egyptian medical students is generally poor. Introduction of regular BLS courses into the undergraduate curriculum is a must to increase the level of BLS knowledge among Egyptian future physicians

Forecasting and Modelling the Uncertainty of Low Voltage Network Demand and the Effect of Renewable Energy Sources

More and more households are using renewable energy sources, and this will continue as the world moves towards a clean energy future and new patterns in demands for electricity. This creates significant novel challenges for Distribution Network Operators (DNOs) such as volatile net demand behavior and predicting Low Voltage (LV) demand. There is a lack of understanding of modern LV networks’ demand and renewable energy sources behavior. This article starts with an investigation into the unique characteristics of householder demand behavior in Jordan, connected to Photovoltaics (PV) systems. Previous studies have focused mostly on forecasting LV level demand without considering renewable energy sources, disaggregation demand and the weather conditions at the LV level. In this study, we provide detailed LV demand analysis and a variety of forecasting methods in terms of a probabilistic, new optimization learning algorithm called the Golden Ratio Optimization Method (GROM) for an Artificial Neural Network (ANN) model for rolling and point forecasting. Short-term forecasting models have been designed and developed to generate future scenarios for different disaggregation demand levels from households, small cities, net demands and PV system output. The results show that the volatile behavior of LV networks connected to the PV system creates substantial forecasting challenges. The mean absolute percentage error (MAPE) for the ANN-GROM model improved by 41.2% for household demand forecast compared to the traditional ANN model

Clinical outcomes of nonvariceal upper gastrointestinal bleeding in nonagenarians and octogenarians: a comparative nationwide analysis

Background/Aims Nonagenarians will purportedly account for 10% of the United States population by 2050. However, no studies have assessed the outcomes of nonvariceal upper gastrointestinal bleeding (NVUGIB) in this age group. Methods The National Inpatient Sample database between 2016 and 2020 was used to compare the clinical outcomes of NVUGIB in nonagenarians and octogenarians and evaluate predictors of mortality and the use of esophagogastroduodenoscopy (EGD). Results Nonagenarians had higher in-hospital mortality than that of octogenarians (4% vs. 3%, p<0.001). EGD utilization (30% vs. 48%, p<0.001) and blood transfusion (27% vs. 40%, p<0.001) was significantly lower in nonagenarians. Multivariate logistic regression analysis revealed that nonagenarians with NVUGIB had higher odds of mortality (odds ratio [OR], 1.5; 95% confidence interval [CI], 1.3–1.7) and lower odds of EGD utilization (OR, 0.86; 95% CI, 0.83–0.89) than those of octogenarians. Conclusions Nonagenarians admitted with NVUGIB have a higher mortality risk than that of octogenarians. EGD is used significantly in managing NVUGIB among nonagenarians; however, its utilization is comparatively lower than in octogenarians. More studies are needed to assess predictors of poor outcomes and the indications of EGD in this growing population