Sequentially Timed All-Optical Mapping Photography for Real- Time Monitoring of Laser Ablation: Breakdown and Filamentation in Picosecond and Femtosecond Regimes

To investigate ultrafast phenomena, a novel, ultrafast imaging technique was developed. Sequentially timed all-optical mapping photography (STAMP) performs single-shot image acquisition without the need for repetitive measurements and without sacrificing high-temporal resolution and image quality. The principle of this imaging method is based on the all-optical approach, and therefore it overcomes the temporal resolution in conventional high-speed cameras. Also, STAMP’s single-shot movie-shooting capability allows us to obtain sequential images of non-repetitive ultrafast dynamic phenomena. Here, we present the motion pictures of early stage dynamics during femtosecond laser ablation captured by two types of STAMP setup. Breakdown was induced by intense femtosecond laser pulse and monitored with a frame interval of 15.3 ps and a total of six frames. The movie clearly shows the plasma generation and expansion on glass surface. Also, filamentation was generated inside a glass and observed with a frame interval of 230 fs and total of 25 frames. These phenomena have previously only been observed by pump-probe imaging. STAMP is a powerful tool to understand precise processes of complex dynamics in ultrashort laser ablation

Practical purification scheme for decohered coherent-state superpositions via partial homodyne detection

We present a simple protocol to purify a coherent-state superposition that has undergone a linear lossy channel. The scheme constitutes only a single beam splitter and a homodyne detector, and thus is experimentally feasible. In practice, a superposition of coherent states is transformed into a classical mixture of coherent states by linear loss, which is usually the dominant decoherence mechanism in optical systems. We also address the possibility of producing a larger amplitude superposition state from decohered states, and show that in most cases the decoherence of the states are amplified along with the amplitude.Comment: 8 pages, 10 figure

Dental injuries in paediatric mandibular fracture patients

Purpose Dental injuries (DIs) are associated with facial fractures, particularly mandibular fractures. As paediatric mandibular fractures have special features, we sought to clarify the occurrence and types of DIs among this patient group. We assessed how age, injury type, and fracture location affects the occurrence of DIs and thereby defined which patients are most susceptible. Methods This retrospective study included patients < 18 years with a recent mandibular fracture. Predictor variables were gender, age group, mechanism of injury, type of mandibular fracture, and other associated facial fracture(s). Types and locations of DIs and tooth loss due to injury were also reported. Results DIs were detected in 34.7% (n = 41) out of 118 patients. Patients with tooth injury had on average 3.5 injured teeth. A total of 16.2% of injured teeth were lost, typically at the time of the injury. Loss of at least one tooth was seen in approximately 10% of patients. Avulsion was the most common cause of tooth loss (52.2%). Non-complicated crown fracture (50.7%) was the most common DI type. Statistically significant associations between studied variables and DIs were not detected. Conclusion DIs are common and often multiple in paediatric mandibular fracture patients regardless of background factors. DIs often lead to tooth loss. Prompt replantation of an avulsed tooth, early detection of DIs, and prevention of tooth loss whenever possible are important to avoid permanent tooth defects.Peer reviewe

Building Heat Demand Forecasting by Training a Common Machine Learning Model with Physics-Based Simulator

Accurate short-term forecasts of building energy consumption are necessary for profitable demand response. Short-term forecasting methods can be roughly classified into physics-based modelling and data-based modelling. Both of these approaches have their advantages and disadvantages and it would be therefore ideal to combine them. This paper proposes a novel approach that allows us to combine the best parts of physics-based modelling and machine learning while avoiding many of their drawbacks. A key idea in the approach is to provide a variety of building parameters as input for an Artificial Neural Network (ANN) and train the model with data from a large group of simulated buildings. The hypothesis is that this forces the ANN model to learn the underlying simulation model-based physics, and thus enables the ANN model to be used in place of the simulator. The advantages of this type of model is the combination of robustness and accuracy from a high-detail physics-based model with the inference speed, ease of deployment, and support for gradient based optimization provided by the ANN model. To evaluate the approach, an ANN model was developed and trained with simulated data from 900–11,700 buildings, including equal distribution of office buildings, apartment buildings, and detached houses. The performance of the ANN model was evaluated with a test set consisting of 60 buildings (20 buildings for each category). The normalized root mean square errors (NRMSE) were on average 0.050, 0.026, 0.052 for apartment buildings, office buildings, and detached houses, respectively. The results show that the model was able to approximate the simulator with good accuracy also outside of the training data distribution and generalize to new buildings in new geographical locations without any building specific heat demand data

Mandibular fractures in aged patients - Challenges in diagnosis

Background/Aims Delayed treatment of a mandibular fracture can lead to complications. Therefore, early diagnosis is important. The aim of this study was to clarify the specific features of mandibular fractures in aged patients and the effect of age on possible missed diagnoses. Material and Methods Patients aged over 60 years with a recent mandibular fracture were included in the study. The outcome variable was a missed mandibular fracture during the patient's first assessment in the primary health care facility. Predictor variables were age group, categorized as older adults (aged >= 60 and 80 years), patient's age as a continuous variable and age sub-group divided into decades. Additional predictor variables were the patient's memory disease and injury associated with intracranial injury. Explanatory variables were gender, injury mechanism, type of mandibular facture, combined other facial fracture, edentulous mandible/maxilla/both, surgical treatment of the mandibular fracture, and scene of injury. Results Mandibular fractures were missed in 20.0% of the 135 patients during their first healthcare assessment. Significant associations between missed fractures and age group, gender, fracture type, or injury mechanism were not found. By contrast, memory disorder (p = .02) and site of injury (p = .02) were significantly associated with missed fractures. Fractures were missed more frequently in patients who were in hospital or in a nursing home at the time of injury. Conclusions There is an increased risk of undiagnosed mandibular fractures in the aged population. Small injury force accidents may cause fractures in old and fragile individuals. Careful examination is necessary, especially in patients with memory disorder.Peer reviewe

Decision Support Tool to Enable Real-Time Data-Driven Building Energy Retrofitting Design

The availability of near-real-time data on energy performance is opening new opportunities to optimize buildings&rsquo; energy efficiency and flexibility capabilities and to support the decision-making and planning process of building retrofitting infrastructure investment. Existing tools can support retrofitting design and energy performance contracting. However, there are well-recognized shortcomings of these tools related to their usability, complexity, and ability to perform calculations based on the real-time energy performance of buildings. To address this gap, the advanced retrofitting decision support tool is developed and presented in this study. The strengths of our solution rely on easy usability, accuracy, and transparency of results. The automatic collection of real-time building energy consumption data gathered from the building management systems, combined with data analytics techniques, ensures ease of use and quickness of calculation. These results support step-by-step thinking for retrofitting design and hopefully enable a larger utilization rate for deep building retrofits