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The impact of labeling automotive AI as trustworthy or reliable on user evaluation and technology acceptance
Abstract This study explores whether labeling AI as either “trustworthy” or “reliable” influences user perceptions and acceptance of automotive AI technologies. Utilizing a one-way between-subjects design, the research presented online participants (N = 478) with a text presenting guidelines for either trustworthy or reliable AI, before asking them to evaluate 3 vignette scenarios and fill in a modified version of the Technology Acceptance Model which covers different variables, such as perceived ease of use, human-like trust, and overall attitude. While labeling AI as “trustworthy” did not significantly influence people’s judgements on specific scenarios, it increased perceived ease of use and human-like trust, namely benevolence, suggesting a facilitating influence on usability and an anthropomorphic effect on user perceptions. The study provides insights into how specific labels affect adopting certain perceptions of AI technology.
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A new-year round-up of the science stories you may have missed
Graduate-student stipends in Canada below the poverty line
Erythema nodosum, malignant melanoma and non-melanoma skin cancer in relation to inflammatory bowel disease: a Mendelian randomization study
Live birth prevalence of major congenital anomalies in the United Arab Emirates
Poplar transformation with variable explant sources to maximize transformation efficiency
Clustering-based binary Grey Wolf Optimisation model with 6LDCNNet for prediction of heart disease using patient data
Improved S-bend optical waveguide loss formula verified by experiments
Experimental investigation into the Impact of Cyclic High Low Pressure Water Immersion on the Pore structure and Mechanical properties of coal
Predecting power transformer health index and life expectation based on digital twins and multitask LSTM-GRU model
AbstractPower transformers play a crucial role in enabling the integration of renewable energy sources and improving the overall efficiency and reliability of smart grid systems. They facilitate the conversion, transmission, and distribution of power from various sources and help to balance the load between different parts of the grid. The Transformer Health Index (THI) is one of the most important indicators of ensuring their reliability and preventing unplanned outages. To this end, this study introduces a proposed new architecture called a Smart Electricity Monitoring System based on Fog Computing and Digital Twins (SEMS-FDT) for monitoring the health performance of transformers by measuring the THI rate in real time. The SEMS-FDT is specifically designed to enable the observation of the transformer’s health and performance purposes in real time. The study investigates the role of machine learning (ML) models, including traditional and ensemble methods, in predicting THI and LI (Heat Load Index) by exploring the use of the entire set of features and optimized feature subsets for prediction. To improve the forecasting prediction process and achieve optimal performance a novel multitasks LSTM_GRU model is also proposed. The experimental results demonstrate that there is a promising performance of 2.543, 0.13646, 0.0284, and 0.985 for MSE, MAE, MedAE, and R2 scores respectively. Moreover, the framework is extended by incorporating model explanations, which include global explanations, which provide insights based on the entire dataset, and local explanations, which offer instance-specific explanations. The integration of the proposed model and explainability features provides engineers with comprehensive outcomes regarding the model’s result.
Predictive modeling of air quality in the Tehran megacity via deep learning techniques
Ten-year outcomes after DMEK, DSAEK, and PK: insights on graft survival, endothelial cell density loss, rejection and visual acuity
AbstractFuchs Endothelial Corneal Dystrophy (FECD) is the most frequent indication for corneal transplantation, with Descemet membrane endothelial keratoplasty (DMEK), Descemet stripping automated endothelial keratoplasty (DSAEK), and penetrating keratoplasty (PK) being viable options. This retrospective study compared 10-year outcomes of these techniques in a large cohort of 2956 first-time keratoplasty eyes treated for FECD at a high-volume corneal transplant center in Germany. While DMEK and DSAEK provided faster visual recovery (median time to BSCVA ≥ 6/12 Snellen: DMEK 7.8 months, DSAEK 12.4 months, PK 37.9 months; cumulative probability of BSCVA ≥ 6/12 Snellen within 5 years: DMEK 93%, DSAEK 83%, PK 63%), PK surprisingly exhibited superior long-term graft survival (92% vs. 75% for DMEK and 73% for DSAEK at 10 years). Endothelial cell density (ECD) decreased significantly faster after DMEK and DSAEK, potentially contributing to their lower graft survival (10-year ECD > 1000 cells/mm2 probability: DMEK 3%, DSAEK 8%, PK 18%). DMEK demonstrated the lowest rejection rate (10% at 10 years vs. 13% for PK and 19% for DSAEK). These findings challenge the perceived superiority of endothelial keratoplasty and highlight the need for further investigation into the long-term implications of accelerated endothelial cell loss after DMEK and DSAEK.
Enhanced antibody responses in CD19-Cre mice
Privacy protection of sexually transmitted infections information from Chinese electronic medical records
Novel primers drive accurate SYBR Green PCR detection of Listeria monocytogenes and Listeria innocua in cultures and mushrooms
Harnessing light-activated gallium porphyrins to combat intracellular Staphylococcus aureus using an in vitro keratinocyte infection model
Abstract Staphylococcus aureus ( S. aureus ) can survive inside nonprofessional phagocytes such as keratinocytes, enabling it to evade antibiotics and cause recurrent infections once treatment stops. New antibacterial strategies to eliminate intracellular, multidrug-resistant bacteria are needed. This study used a keratinocyte model infected with methicillin-resistant S. aureus (MRSA) to test light-activated compounds, specifically heme-mimetic gallium (III) porphyrin (Ga 3+ CHP) and visible light, known as antimicrobial photodynamic inactivation (aPDI), for eliminating intracellular MRSA. Ga 3+ CHP was found to accumulate more in infected cells, particularly within lysosomal structures where MRSA resides. Flow cytometry and fluorescence microscopy revealed significant colocalization of MRSA and Ga 3+ CHP. Under aPDI, MRSA showed reduced adhesion to host cells and a 70% reduction in the GFP signal from intracellular bacteria. Additionally, light-activated Ga 3+ CHP significantly decreased the number of extracellular bacteria, reducing the potential for further infection. This study is the first to analyze aPDI toxicity in real time within an infection model, demonstrating that this method is neither cytotoxic nor phototoxic.