Proper regulation of cell signaling and gene expression is crucial for maintaining cellular function, development, and adaptation to environmental changes. Reaction dynamics in cell populations is often noisy because ...
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Collaboration plays a key role in knowledge production. Here, we show that patterns of interaction during conferences can be used to predict who will subsequently form a new collaboration, even when interaction is pre...
In this study, we consider the budget allocation problem for binary classification with noisy labels. The classification accuracy can be improved by reducing the label noises which can be achieved by observing multipl...
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Introduction/ Background: Medical diagnoses have increasingly depended on digitized images obtained through cutting-edge technology. These algorithms offer a promising avenue to transform diagnostic processes in healt...
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Introduction/ Background: Medical diagnoses have increasingly depended on digitized images obtained through cutting-edge technology. These algorithms offer a promising avenue to transform diagnostic processes in healthcare, with their application scope continually widening due to ongoing advancements. This paper explores machine learning's role in clinical analysis and prediction, examining various studies that apply these techniques in clinical diagnosis, focusing on their use in analyzing images and providing accurate diagnoses. Materials and Methods: This study employs a comparative analysis approach, utilizing diverse machine learning algorithms like SVM, K-nearest neighbors, Random Forests, and Decision Trees to analyze digitized medical images and patient records. We extracted data from several medical databases, ensuring a varied and comprehensive dataset. We also evaluated the impact of different data characteristics on the algorithms' effectiveness, aiming to understand the variability in their diagnostic precision across various conditions. Results: The results indicate that machine learning algorithms, particularly SVM, K-nearest neighbors, Random Forests, and Decision Trees, demonstrate significant accuracy in diagnosing diseases from digitized images and medical records. SVM and Random Forests showed particularly high effectiveness in clinical diagnosis, suggesting their robustness across different medical conditions and datasets. These findings underscore the potential of machine learning to enhance diagnostic precision and predict illnesses early, aligning with the growing trend of technology-driven medical diagnostics. Discussion: The findings reinforce the pivotal role of machine learning in transforming medical diagnostics. The variability in algorithm performance highlights the necessity for tailored approaches, considering dataset specifics and the medical condition being diagnosed. This study underscores the potential of machine learning to enha
Introduction: In recent times, there has been a noticeable surge in the usage of artificial intelligence, including ChatGPT and other types, in the field of health sciences education. In this regard, an exploratory bi...
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Introduction: In recent times, there has been a noticeable surge in the usage of artificial intelligence, including ChatGPT and other types, in the field of health sciences education. In this regard, an exploratory bibliometric study was carried out to examine the utilization of smart conversational agents, ChatGPT, and artificial intelligence bots in medical education. Methods: A retrospective, observational, cross-sectional bibliometric analysis was employed to assess the scientific publications listed in Scopus. This study was conducted on March 11, 2023 in search for information in Scopus. A total of 220 relevant documents were identified that were available in the Scopus database during the period between 2017 and 2022. Elsevier's SciVal software was used. Subsequently, statistical tables and graphs were prepared for presentation in Bibliometrix software. Results: Among the authors, Timothy W. Bickmore, from the United States, has the highest number of publications (10) and citations received (172), and an h-index of 45, suggesting a significant influence in the field of study. The subcategory with the highest academic output is Health Informatics with 133 publications, while Geriatrics and Gerontology has the least with only 3. Most of the analyzed publications (44.2%) originated from collaborations within the same country. Notably, the Swiss Federal Institute of Technology Zurich and Imperial College London stood out with 12 publications each that received over 200 citations indicating their significant impact on their respective fields. Despite having the highest number of academic publications (15), Brazil had a relatively low field-weighted citation impact (0.64) and received the lowest number of citations (81). A clustering analysis was performed on a sample of 10 concepts using 2 dimensions. The results indicated that all terms were part of the same cluster. Notably, the terms 'conversational agents', 'chatbots', 'conversational agent', and 'chatbot' wer
Functionality of wearable electronics depends directly on the manufacturing process and the reliability of the device at different conditions. Nonwoven high-density polyethylene (HDPE) fibers shows promising propertie...
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ISBN:
(数字)9781728161808
ISBN:
(纸本)9781728161815
Functionality of wearable electronics depends directly on the manufacturing process and the reliability of the device at different conditions. Nonwoven high-density polyethylene (HDPE) fibers shows promising properties such as moisture resistance, which makes it an excellent candidate for washable smart garments. In this research, effect of screenprinting parameters on electrical resistance of printed silver trace on nonwoven HDPE fibers is studied. Reliability of the printed trace during isothermal fatigue cycling at different test conditions and ambient are investigated. The results of statistical analysis reveal that low pressure and high speed of squeegees lead to decrease in the electrical resistance and higher quality of the printed trace. Higher strain amplitude and higher initial resistance accelerate damage accumulation during fatigue cycling. Exposure of the printed silver trace to high humidity and temperature (85 RH%-85°C) does not have a negative impact on electromechanical behavior during cyclic loading.
Energy poverty has become a critical problem that demands innovative approaches to studying and solving it. This study consists of a systematic review of the nexus approach applied to energy poverty in scientific publ...
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This article is a call to action to address escalating threats to scientific progress that affect academic researchers across the *** threats include public mistrust of science,challenges in translating academic resea...
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This article is a call to action to address escalating threats to scientific progress that affect academic researchers across the *** threats include public mistrust of science,challenges in translating academic research to end-user applications,a disconnect between academics and policymakers,emerging barriers to international collaboration,and a reliance on conventional metrics to evaluate academic *** article presents various calls to action informed by exemplary approaches across the globe that serve as frameworks to drive beneficial transformation for researchers,academic institutions,and society.
The main goal of the present paper is to study the energy dissipation characteristic as the influence of parameters in the event of ship collisions. The review is conducted on accident case between Roll on-Roll off (R...
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The main goal of the present paper is to study the energy dissipation characteristic as the influence of parameters in the event of ship collisions. The review is conducted on accident case between Roll on-Roll off (Ro-Ro) Marisa Nusantara with Reefer Qi Hang at Sunda Strait, Indonesia May 3rd, 2014, as reference. It was later modeled and analyzed by nonlinear simulations finite element (FE) method was presented to be used as a reference to get the verified model. This collaboration study is considered as a good reference because it used real accident damage to compare the numerical results. The result in collision energy from the virtual simulation was later verified with the calculation result of the empirical formula. In the extended study, the collision region and material model are selected as influenced parameters to design collision scenarios. The characteristic of collision energy from these scenarios will be discussed. In the same section, the damage pattern is observed and reviewed to find the relation between several parameters to calculation results. It was shown that the influence of region and location of target points contributed significantly to energy dissipation. In contrast, the key finding in material properties is that when Young’s modulus between materials was the same and the difference of yield strength of materials was not significant, then other material properties contributed to the result.
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