Science Archaeology Who built Scandinavia's oldest wooden plank boat? An ancient fingerprint offers clues. Archeologists are closer to solving the Hjortspring Boat's mysteries. Breakthroughs, discoveries, and DIY tips sent every weekday. Archaeologists examining an ancient boat discovered in Denmark over a century ago are getting some help from a clue usually associated with crime scenes .

There is growing interest in automating surgical tasks using robotic systems, such as endoscopy for treating gastrointestinal (GI) cancer. However, previous studies have primarily focused on detecting and analyzing objects or robots, with limited attention to ensuring safety, which is critical for clinical applications, where accidents can be caused by unsafe robot motions. In this study, we propose a new control framework that can formally ensure the safety of automating certain processes involved in endoscopic submucosal dissection (ESD), a representative endoscopic surgical method for the treatment of early GI cancer, by using an endoscopic robot. The proposed framework utilizes Control Barrier Functions (CBFs) to accurately identify the boundaries of individual tumors, even in close proximity within the GI tract, ensuring precise treatment and removal while preserving the surrounding normal tissue. Additionally, by adopting a model-free control scheme, safety assurance is made possible even in endoscopic robotic systems where dynamic modeling is challenging. We demonstrate the proposed framework in cases where the tumors to be removed are close to each other, showing that the safety constraints are enforced. We show that the model-free CBF-based controlled robot eliminates one tumor completely without damaging it, while not invading another nearby tumor.

A mucus-excreting robot with a single large foot can effectively imitate the way snails crawl over surfaces – even steeply inclined ones. "I always say that snails are like Michael Jackson to me. You don't see how they move, but somehow gliding is happening," said Saravana Prashanth Murali Babu at the University of Southern Denmark during a presentation at the American Physical Society's March Meeting in Minneapolis, Minnesota, on 4 March. Fascinated by the shelled molluscs, Saravana and his colleagues decided to build a version of a snail's single large, soft foot and use it as the basis of a robot that moves like a snail. The unique promise of'biological computers' made from living things During his presentation, Saravana explained that the team chose to build the foot from a soft material that could be inflated in segments by small pneumatic pumps.

Lung cancer (LC) remains the primary cause of cancer-related mortality, largely due to late-stage diagnoses. Effective strategies for early detection are therefore of paramount importance. In recent years, machine learning (ML) has demonstrated considerable potential in healthcare by facilitating the detection of various diseases. In this retrospective development and validation study, we developed an ML model based on dynamic ensemble selection (DES) for LC detection. The model leverages standard blood sample analysis and smoking history data from a large population at risk in Denmark. The study includes all patients examined on suspicion of LC in the Region of Southern Denmark from 2009 to 2018. We validated and compared the predictions by the DES model with diagnoses provided by five pulmonologists. Among the 38,944 patients, 9,940 had complete data of which 2,505 (25\%) had LC. The DES model achieved an area under the roc curve of 0.77$\pm$0.01, sensitivity of 76.2\%$\pm$2.4\%, specificity of 63.8\%$\pm$2.3\%, positive predictive value of 41.6\%$\pm$1.2\%, and F\textsubscript{1}-score of 53.8\%$\pm$1.1\%. The DES model outperformed all five pulmonologists, achieving a sensitivity 9\% higher than their average. The model identified smoking status, age, total calcium levels, neutrophil count, and lactate dehydrogenase as the most important factors for the detection of LC. The results highlight the successful application of the ML approach in detecting LC, surpassing pulmonologists' performance. Incorporating clinical and laboratory data in future risk assessment models can improve decision-making and facilitate timely referrals.

Increasingly, social robots are being used for support in educational contexts. But does the sound of a social robot affect how well they perform, especially when dealing with teams of humans? Teamwork is a key factor in human creativity, boosting collaboration and new ideas. Danish scientists set out to understand whether robots using a voice designed to sound charismatic would be more successful as team creativity facilitators. "We had a robot instruct teams of students in a creativity task. The robot either used a confident, passionate -- ie charismatic -- tone of voice or a normal, matter-of-fact tone of voice," said Dr Kerstin Fischer of the University of Southern Denmark, corresponding author of the study in Frontiers in Communication.

Kurt "CyberGuy" Knutsson explains whether robot security guards are better or worse for society. A new study is suggesting that robots with more "charismatic" voices – as opposed to flat, matter-of-fact ones – can help people be more creative. Scientists from Denmark found that students who are given a task by a robot with a voice programmed to be more "engaging" and "inspiring" performed better. These students were also more creative than students who received instructions from an identical robot with a flat voice, according to the findings from researchers in Denmark as published by Frontiers in Communication, a peer-reviewed, open-access science journal. Increasingly, social robots are being used for support in educational settings, as SWNS, the British news service, noted.

We are seeking outstanding candidates with strong analytical and problem solving skills, who are strong in written and oral communication (in English), and have documented experience in the development of complex compute systems. The applicant should have provable skills in the state-of-the-art web-development frameworks, virtualization techniques as well as database technologies. Expertise in clinical data science and machine learning, as well as computer security and data privacy are welcome. A large roadblock of medical research is the difficult access to sensitive data which therefore hinders the training of complex and powerful machine learning concepts. This issue is amplified when considering rare diseases with low incidence numbers per hospital.

A robot that is able to take throat swabs from coronavirus patients using a 3D printed arm was developed by a team of researchers from Denmark in just four weeks. The University of Southern Denmark says the world's first fully automated throat swab robot will be be able to test the first COVID-19 patients by late June. Using disposable 3D printed parts, the robot holds a swab and hits the exact spot in the throat where a sample needs to be collected every time. It puts the swab in a glass and screws the lid on to seal the sample without human input - reducing the risk of exposing healthcare workers to the deadly virus. A team of ten researchers for the Industry 4.0 Lab at the University of Southern Denmark worked around the clock to produce the prototype of the robot.

The Region of Southern Denmark, with help from SAS, has become the first place in the world to implement a complete system for monitoring hospital-acquired infections. Professor Jens Kjølseth Møller at Lillebaelt Hospital is the brain behind the new system, which is made possible by SAS Analytics. Kjølseth Møller expects the system to reduce the number of infections during hospitalization by one-third, significantly increasing patient safety. "It is unsatisfying that patients admitted to Danish hospitals are at risk of further illness," says Peder Jest, Medical Director at Odense University Hospital. "The work of providing a high degree of patient safety and good infection hygiene is, therefore, a key focus area for the Region of Southern Denmark. With SAS, we now have the ability to monitor and predict the risk of hospital-acquired infections at a patient level."

This is a report of the second annual workshop on Optimizing Player Satisfaction (OPS), held in conjunction with the Artificial Intelligence and Interactive Digital Entertainment (AIIDE-07) conference. We discuss highlights of this year's workshop and include a discussion for next year's event. This was the second workshop in a series started in conjunction with the Simulation of Adaptive Behavior (SAB) conference in 2006. The primary goal of the OPS workshop series is to encourage a dialogue among researchers in AI, human-computer interaction, affective computing, and psychology disciplines who investigate dissimilar methodologies for improving gameplaying experiences. An additional aim of these events is to yield a better understanding of state-of-the-art approaches for optimizing player satisfaction in interactive entertainment systems.