Algorithmic bias in healthcare mirrors existing data biases. However, the factors driving unfairness are not always known. Medical devices capture significant amounts of data but are prone to errors;for instance, puls...
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Pharmacy Management System (PMS) is an advanced software solution designed to increase the efficiency, accuracy and safety of pharmaceutical operations. System automates important functions such as inventory managemen...
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This paper presents a novel approach for processing cycling data from TCX files using a custom-built Julia library, TCX2Graph.j1. The method includes constructing property graphs from GPS track points and detecting ov...
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ISBN:
(数字)9798350379365
ISBN:
(纸本)9798350379372
This paper presents a novel approach for processing cycling data from TCX files using a custom-built Julia library, TCX2Graph.j1. The method includes constructing property graphs from GPS track points and detecting overlapping path segments. These detected overlapping points are enriched with track point metrics, enabling detailed analysis of cycling sessions. The proposed approach processes large datasets efficiently, pro-viding insights into overlapping paths and athlete performance.
There is a significant correlation between depression, verbal behavior, and facial expressions. By analyzing patients’ audio and facial visuals, depression assessments can be conducted. However, existing work is pred...
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ISBN:
(数字)9798350368741
ISBN:
(纸本)9798350368758
There is a significant correlation between depression, verbal behavior, and facial expressions. By analyzing patients’ audio and facial visuals, depression assessments can be conducted. However, existing work is predominantly based on single modalities. Additionally, acquiring a sufficient amount of labeled data in clinical settings is challenging and costly. To leverage multimodal audio-visual data while addressing the issue of lacking trainable labeled data, we propose an audiovisual multimodal semi-supervised depression detection model based on Graph Neural Networks (AVS-GNN). This model first extracts dual-modality temporal information from audio features and facial visual features of patients and obtains modality-specific high-level embedding representations through graph representation learning. Subsequently, it utilizes graph-based contrastive unsupervised learning to capture consistency information between pairs of unlabeled samples across different modalities and to facilitate cross-modal interactions. We specifically designed a hybrid weighted pseudo-labeling strategy to assign high-confidence pseudo-labels to unlabeled data and further retraining the model. Experiments on two depression datasets show that this model outperforms baseline methods across all evaluation metrics.
Climate change is one of the biggest challenges of the 21st century, with profound environmental, economic, and social impacts. Machine learning (ML) has emerged as a transformative tool to address climate challenges ...
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Space docking is an important aspect of modern-day space exploration, enabling crewed missions, shipment transport, and spacecraft refuelling. Traditionally, manual docking has trusted astronaut precision and floor ma...
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Virtual Reality (VR) technology has the potential to enhance education by providing immersive and engaging learning experiences that can improve teaching and learning outcomes. While there is a growing interest in uti...
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A novel electrochemical aptasensor was designed for the simultaneous detection of aflatoxin B1 (AFB1) and deoxynivalenol (DON) using dual-working microelectrodes and PDMS-based microfluidic channels. The system provid...
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ISBN:
(数字)9798331508890
ISBN:
(纸本)9798331508906
A novel electrochemical aptasensor was designed for the simultaneous detection of aflatoxin B1 (AFB1) and deoxynivalenol (DON) using dual-working microelectrodes and PDMS-based microfluidic channels. The system provides high sensitivity, with detection limits of 7.74 pg/mL for AFB1 and 0.172 ng/mL for DON across broad ranges (0.03 − 300 ng/mL for AFB1, 0.3 − 3000 ng/mL for DON). Results can be obtained within 20 minutes, and the platform exhibits excellent reproducibility, selectivity, and stability. Validation with corn extract highlights its potential for on-site food safety monitoring.
The growing need for sustainable energy solutions,driven by rising energy shortages,environmental concerns,and the depletion of conventional energy sources,has led to a significant focus on renewable *** energy,among ...
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The growing need for sustainable energy solutions,driven by rising energy shortages,environmental concerns,and the depletion of conventional energy sources,has led to a significant focus on renewable *** energy,among the various renewable sources,is particularly appealing due to its abundant ***,the efficiency of commercial solar photovoltaic(PV)modules is hindered by several factors,notably their conversion efficiency,which averages around 19%.This efficiency can further decline to 10%–16%due to temperature increases during peak sunlight *** study investigates the cooling of PV modules by applying water to their front surface through Computational fluid dynamics(CFD).The study aimed to determine the optimal conditions for cooling the PV module by analyzing the interplay between water film thickness,Reynolds number,and their effects on temperature reduction and heat *** CFD analysis revealed that the most effective cooling condition occurred with a 5 mm thick water film and a Reynolds number of *** specific parameters were found to maximize the heat transfer and temperature reduction *** finding is crucial for the development of practical and efficient cooling systems for PV modules,potentially leading to improved performance and longevity of solar *** cooling fluids or advanced cooling techniques that might offer even better efficiency or practical benefits.
The COVID-19 pandemic exposed significant shortcomings in the ability of modern healthcare information systems to manage and mitigate pandemics, especially when faced with unforeseen events. This paper addresses these...
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