OBJECTIVES:To determine whether knowledge-based deficiencies are adequately addressed at the AO North America Basic Principles of Fracture Management course.
DESIGN:Pretest, posttest.
...SETTING:Eighteen national trauma courses.
PARTICIPANTS:Two thousand one hundred forty-nine learners.
INTERVENTION:Pre- and postcourse 20-item tests of basic fracture knowledge, including 14 trauma topics.
MAIN OUTCOME MEASURES:Deficiencies were defined as <60% correct answers on the precourse test. Postcourse knowledge gaps were defined as <75% correct responses.
RESULTS:Deficiencies were noted in 7 of the 14 topics on the precourse test. All topics with deficiencies on the precourse test were shown to have statistically significant improvement in postcourse test scores. All topics without deficiencies were shown to have statistically significant improvement in postcourse test scores. The average overall precourse test score was 63% (95% confidence interval, 61%–65%), and the average overall postcourse test score was 81% (95% confidence interval, 79%–83%). The pretest to posttest difference was statistically significant (P < 0.05). The control questions, covering material that was not discussed in the course, did not have statistically significant improvement in scores.
CONCLUSIONS:Residents are entering residency programs with limited knowledge of fracture care, and significant gaps remain at the junior level at the time of course participation, suggesting that supplemental fracture courses play an important role in resident education. Validation of short-term learning is possible through a pretest and posttest technique, and it can guide design changes, as opposed to relying on satisfaction surveys alone.
With the development of Chinese sports, many sports training researchers try to use artificial intelligence technology to study the training methods and training elements of athletes. However, in ...reality, these methods are often based on different basic training principles, resulting in the reduction in the generalization ability of artificial intelligence networks. This paper studies the complexity of sports training principles by using an artificial intelligence network model. Based on the improved model of dropout optimization algorithm, this paper proposes an artificial intelligence sports training node prediction method based on the combination of dropout optimization algorithm and short-term memory neural network (LSTM), which avoids the establishment of complex sports training models. Based on artificial intelligence operation and maintenance records and sports training core capacity experimental data, the maximum node static estimation of artificial intelligence sports training is realized. The research shows that the node prediction model is established by using the method described in this paper. Through experimental comparison and analysis, the model has high prediction accuracy. Due to the state memory function of LSTM, it has advantages in the prediction of 2000 data on a long time scale. The mean absolute error percentage of the prediction results is less than 3.4%, and the maximum absolute error percentage is less than 5.2%. The artificial intelligence network model in this paper has good generalization ability. Compared with other models, the model proposed in this paper can get more accurate prediction results in sports training of different groups and effectively alleviate the problem of overfitting. Therefore, traditional stadiums and gymnasiums should actively introduce artificial intelligence technology with a more positive attitude, to realize the development and innovation in technology application, service innovation, management efficiency, and function integration.
The appearance of ultrasound images depends critically on the physical interactions of sound with the tissues in the body. The basic principles of ultrasound imaging and the physical reasons for many ...common artifacts are described.
Destroying image integrity in scientific papers may result in serious consequences. Inappropriate duplication and fabrication of images are two common misconducts in this aspect. The rapid ...development of artificial-intelligence technology has brought to us promising image-generation models that can produce realistic fake images. Here, we show that such advanced generative models threaten the publishing system in academia as they may be used to generate fake scientific images that cannot be effectively identified. We demonstrate the disturbing potential of these generative models in synthesizing fake images, plagiarizing existing images, and deliberately modifying images. It is very difficult to identify images generated by these models by visual inspection, image-forensic tools, and detection tools due to the unique paradigm of the generative models for processing images. This perspective reveals vast risks and arouses the vigilance of the scientific community on fake scientific images generated by artificial intelligence (AI) models.
This perspective reports on the vast risk of potential image fraud based on artificial intelligence (AI) generative technologies in academic publications that have been neglected. This article discusses the scenarios, capabilities, and effects of AI algorithms used in academic fraud. The issue described in this perspective is not only relevant to computer scientists. As members of the scientific community, each of us will be deeply involved in the peer-review process. Each of us may be deceived by the AI image-fraud methods described in this article. Although the algorithm developing itself belongs to the field of computer science, its impact, as mentioned in this perspective, is more related to a wider range of scientific fields, such as biology, medicine, and natural science. Arousing their attention to this threat is a necessary condition to resist this threat. Combined with state-of-the-art AI research, this perspective also discusses possible preventive measures to respond to this potential threat.
In this perspective, we report on the vast risk of potential image fraud based on artificial intelligence (AI) generative technologies in academic publications that have been neglected. We discuss the scenarios, capabilities, and effects of generative models used in academic fraud. We demonstrate this risk through visualized interdisciplinary cases and conduct a user study. Combined with state-of-the-art AI research, we also discussed possible preventive measures to respond to this potential threat.
This chapter describes the use of ultrasound in remediation of wastewater contaminated with organic pollutants in the absence and presence of other advanced oxidation processes (AOPs) such as ...sonolysis, sono-ozone process, sonophotocatalysis, sonoFenton systems and sonophoto-Fenton methods in detail. All these methods are explained with the suitable literature illustrations. In most of the cases, hybrid AOPs (combination of ultrasound with one or more AOPs) resulted in superior efficacy to that of individual AOP. The advantageous effects such as additive and synergistic effects obtained by operating the hybrid AOPs are highlighted with appropriate examples. It is worth to mention here that the utilization of ultrasound is not only restricted in preparation of modern active catalysts but also extensively used for the wastewater treatment. Interestingly, ultrasound coupled AOPs are operationally simple, efficient, and environmentally benign, and can be readily applied for large scale industrial processes which make them economically viable.
Optical Coherence Tomography Angiography (OCTA) is a relatively new imaging technique in ophthalmology for the visualization of the retinal microcirculation and other tissues of the human eye. This ...review paper aims to describe the basic definitions and principles of OCT and OCTA in the most straightforward possible language without complex mathematical and engineering analysis. This is done to help health professionals of various disciplines improve their understanding of OCTA and design further clinical research more efficiently. First, the basic technical principles of OCT and OCTA and related terminology are described. Then, a list of OCTA advantages and disadvantages, with a special reference to blood flow quantification limitations. Finally, an updated list of the basic hardware and software specifications of some of the commercially available OCTA devices is presented.
Disaster risk management (DRM) seeks to help societies prepare for, mitigate, or recover from the adverse impacts of disasters and climate change. Core to DRM are disaster risk models that rely ...heavily on geospatial data about the natural and built environments. Developers are increasingly turning to artificial intelligence (AI) to improve the quality of these models. Yet, there is still little understanding of how the extent of hidden geospatial biases affects disaster risk models and how accountability relationships are affected by these emerging actors and methods. In many cases, there is also a disconnect between the algorithm designers and the communities where the research is conducted or algorithms are implemented. This perspective highlights emerging concerns about the use of AI in DRM. We discuss potential concerns and illustrate what must be considered from a data science, ethical, and social perspective to ensure the responsible usage of AI in this field.
Artificial Intelligence (AI) is increasingly being used in disaster risk management applications to predict the effect of upcoming disasters, plan for mitigation strategies, and determine who needs how much aid after a disaster strikes. The media is filled with unintended ethical concerns of AI algorithms, such as image recognition algorithms not recognizing persons of color or racist algorithmic predictions of whether offenders will recidivate. We know such unintended ethical consequences must play a role in DRM as well, yet there is surprisingly little research on exactly what the unintended consequences are and what we can do to mitigate them. The aim of this perspective is to call researchers working on fairness, accountability, and transparency to work with DRM and local experts—so we can ensure that disaster mitigation and relief is accountable, considers local values, and is not unintentionally biased.
Disaster risk management increasingly relies on artificial intelligence algorithms to help societies mitigate and overcome the adverse impacts of disasters. Yet, there is little research on the ethical implications of using these algorithms. This perspective illustrates concerns voiced by DRM practitioners and highlights the need for research on how to adapt technical solutions for mitigating bias and define new accountability relations. Finally, we emphasize the need to include local experts in prioritizing ethical values.
In this article we first present the foundations of ultrafast photoacoustics, a technique where the acoustic wavelength in play can be considerably shorter than the optical wavelength. The physics ...primarily involved in the conversion of short light pulses into high frequency sound is described. The mechanical disturbances following the relaxation of hot electrons in metals and other processes leading to the breaking of the mechanical balance are presented, and the generation of bulk shear-waves, of surface and interface waves and of guided waves is discussed. Then, efforts to overcome the limitations imposed by optical diffraction are described. Next, the principles behind the detection of the so generated coherent acoustic phonons with short light pulses are introduced for both opaque and transparent materials. The striking instrumental advances, in the detection of acoustic displacements, ultrafast acquisition, frequency and space resolution are discussed. Then secondly, we introduce picosecond opto-acoustics as a remote and label-free novel modality with an excellent capacity for quantitative evaluation and imaging of the cell’s mechanical properties, currently with micron in-plane and sub-optical in depth resolution. We present the methods for time domain Brillouin spectroscopy in cells and for cell ultrasonography. The current applications of this unconventional means of addressing biological questions are presented. This microscopy of the nanoscale intra-cell mechanics, based on the optical monitoring of coherent phonons, is currently emerging as a breakthrough method offering new insights into the supra-molecular structural changes that accompany cell response to a myriad of biological events.
High-rise construction volume increase and new wall materials use require changing the approach to the design of plastermortar compositions. The analysis has showed that it is possible to reduce the ...number of cracks in the plaster coating by increasingthe water holding capacity of the mortar mixture. To optimize the prescription parameters of the mortar mixture, thefive-factor experiment with fine aggregate and the filler with a low modulus of elasticity, disperse polymeric powders andcellulose ethers, a polymer fiber for microdispersed reinforcement has been used. The obtained data indicate that the proposedapproach enables to obtain plaster mortars with physic mechanical characteristics that provide optimal working conditions“masonry - plaster coatings”.
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