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Tuesday, 31 October 2023
Should you wipe snow off solar panels?
Sunday, 29 October 2023
What is a biogas?
Saturday, 28 October 2023
There are 486,713 routes to Rome
Ranveer Singh revealed about RaamLeela first actress
Saturday, 21 October 2023
What is a power factor?
Wednesday, 18 October 2023
Force-sensing ‘smart scalpel’ helps hone doctors’ surgical skills
While the level of force applied to the scalpel by its human operator is – obviously – important during surgery, there have been few tools capable of measuring it in real-life settings. Now, researchers at the University of Edinburgh in the UK have developed a ‘smart scalpel’ with built-in sensors to measure force.
“We are excited to develop this new system, which uses a combination of real-life sensing technology and machine learning methods to quantitatively assess surgical skill,” said Ram Ramamoorthy, the study’s corresponding author. “This system will enable the development of new systems for skill assessment and training and could one day lead to the creation of automated surgical devices that can assist surgical teams.”
The low-cost, easy-to-replicate device consists of a scalpel connected to a sensor-loaded circuit board fitted inside its handle. The researchers designed a machine-learning model to analyze the force applied by the users. Twelve medical students and two professional surgeons tested their innovative scalpel by performing a series of 12 elliptical incisions on a multilayered skin replica made of gelatin and silicone.
Each procedure, which involved making two curved cuts to the skin, such as those used to remove moles and melanomas, was video-recorded and assessed by four expert surgeons – two neurosurgeons and two plastic surgeons – who rated the participants’ proficiency. The researchers then analyzed the relationships between the subjective expert evaluations and the objective force-based metrics data.
Results broadly matched the surgical experts’ assessment of each medical student’s ability, suggesting that this technology could simplify the process of assessing surgical skills. Some discrepancies arose, which, the researchers say, are partly because neurosurgeons and plastic surgeons use different instrument and tissue handling techniques.
The researchers say their findings open up possibilities for future studies, including using more participants for a more comprehensive analysis. Mapping objective measurements and patient outcomes would also be instructive. They say their method shows promise as a way of analyzing highly procedural tasks such as suturing.
The study was published in the journal Communications Engineering.
Amazon river hits century-low water levels; record drought disrupts lives in Brazilian rainforest
Tuesday, 17 October 2023
What is and Auto - Recloser?
Saturday, 14 October 2023
Mild sleep restriction increases endothelial oxidative stress in female persons
Friday, 13 October 2023
How do I keep my engine healthy?
Wednesday, 11 October 2023
The power available in the wind
Tuesday, 10 October 2023
World 1920 vs World 2023
Monday, 9 October 2023
Dependent on your phone? It could be the gateway to addiction
Sunday, 8 October 2023
How the grounding conductor protects against electric shock and equipment damage?
Life Course Patterns of Prescription Drug Use in the United States
Will holding back sneeze kill you?
How to find the square root of such math problems within seconds?
Saturday, 7 October 2023
⚠️What is the Purpose of Hazard Lights and How to Use Them Properly?
Types of electric vehicles
Breast implants have a positive impact on female sexuality, according to new research
Friday, 6 October 2023
Asian Games 2023, Medal Table
New engineered bacteria could destroy plastics in seawater
Thursday, 5 October 2023
Child drug poisonings and firearm deaths have skyrocketed in America: Study
AI translates 5,000-year-old cuneiform tablets into English | A new technology meets old languages.
Abstract
Cuneiform is one of the earliest writing systems in recorded human history (ca. 3,400 BCE–75 CE). Hundreds of thousands of such texts were found over the last two centuries, most of which are written in Sumerian and Akkadian. We show the high potential in assisting scholars and interested laypeople alike, by using natural language processing (NLP) methods such as convolutional neural networks (CNN), to automatically translate Akkadian from cuneiform Unicode glyphs directly to English (C2E) and from transliteration to English (T2E). We show that high-quality translations can be obtained when translating directly from cuneiform to English, as we get 36.52 and 37.47 Best Bilingual Evaluation Understudy 4 (BLEU4) scores for C2E and T2E, respectively. For C2E, our model is better than the translation memory baseline in 9.43, and for T2E, the difference is even higher and stands at 13.96. The model achieves best results in short- and medium-length sentences (c. 118 or less characters). As the number of digitized texts grows, the model can be improved by further training as part of a human-in-the-loop system which corrects the results.
Wednesday, 4 October 2023
What are possible specialities in power system engineering?
Calls for verbal abuse of children by adults to be formally recognised as form of child maltreatment
Tuesday, 3 October 2023
Romantic relationships linked to disturbed sleep patterns in adolescents, study finds
New study shines a light on men's unconscious attraction to fertility cues in women's faces
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Afghanistan Electricity and Rivers Afghanistan is a major country in the central Asian region and its ability to become a bridge between so...
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The source of solar power Deep in the center of the sun, intense nuclear activity generates huge amounts of radiation. In turn, this radiati...
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How can I connect a generator with an automatic transfer switch to a house with 2 electric meters? A lot depends on the topology you already...