The Impact of Postures and Moving Directions in Fire Evacuation in a Low-Visibility Environment

Walking speed is a significant aspect of evacuation efficiency, and this speed varies during fire emergencies due to individual physical abilities. However, in evacuations, it is not always possible to keep an upright posture, hence atypical postures, such as stoop walking or crawling, may be required for survival. In this study, a novel 3D passive vision-aided inertial system (3D PVINS) for indoor positioning was used to track the movement of 20 volunteers during an evacuation in a low visibility environment. Participants’ walking speeds using trunk flexion, trunk–knee flexion, and upright postures were measured. The investigations were carried out under emergency and non-emergency scenarios in vertical and horizontal directions, respectively. Results show that different moving directions led to a roughly 43.90% speed reduction, while posture accounted for over 17%. Gender, one of the key categories in evacuation models, accounted for less than 10% of the differences in speed. The speeds of participants under emergency scenarios when compared to non-emergency scenarios was also found to increase by 53.92–60% when moving in the horizontal direction, and by about 48.28–50% when moving in the vertical direction and descending downstairs. Our results also support the social force theory of the warming-up period, as well as the effect of panic on the facilitating occupants’ moving speed

Domain-Specific Deep Learning Feature Extractor for Diabetic Foot Ulcer Detection

Diabetic Foot Ulcer (DFU) is a condition requiring constant monitoring and evaluations for treatment. DFU patient population is on the rise and will soon outpace the available health resources. Autonomous monitoring and evaluation of DFU wounds is a much-needed area in health care. In this paper, we evaluate and identify the most accurate feature extractor that is the core basis for developing a deep-learning wound detection network. For the evaluation, we used mAP and F1-score on the publicly available DFU2020 dataset. A combination of UNet and EfficientNetb3 feature extractor resulted in the best evaluation among the 14 networks compared. UNet and Efficientnetb3 can be used as the classifier in the development of a comprehensive DFU domain-specific autonomous wound detection pipeline.Comment: 5 pages, 2 figures, 3 tables, 2022 IEEE International Conference on Data Mining Workshop

Tunable Non-Hermitian Acoustic Filter

We propose, design, and experimentally test a non-Hermitian acoustic superlattice that acts as a tunable precise filter. The superlattice is composed of two concatenated sublattices. The first sublattice is Hermitian, while the other can be adjusted to be Hermitian or non-Hermitian. The existence of non-Hermiticity, in terms of an induced loss in the second sublattice, results in the generation of absorption resonances that appear in the reflected spectrum. This provides us with a powerful knob to absorb or reflect several frequencies at will with high accuracy. The number of filtered frequencies can be controlled by designing the resonances in the first sublattice. Our proposed tunable acoustic filter can be extended to higher-frequency ranges, such as ultrasound, and other areas, such as photonics

Synthesizing Diabetic Foot Ulcer Images with Diffusion Model

Diabetic Foot Ulcer (DFU) is a serious skin wound requiring specialized care. However, real DFU datasets are limited, hindering clinical training and research activities. In recent years, generative adversarial networks and diffusion models have emerged as powerful tools for generating synthetic images with remarkable realism and diversity in many applications. This paper explores the potential of diffusion models for synthesizing DFU images and evaluates their authenticity through expert clinician assessments. Additionally, evaluation metrics such as Frechet Inception Distance (FID) and Kernel Inception Distance (KID) are examined to assess the quality of the synthetic DFU images. A dataset of 2,000 DFU images is used for training the diffusion model, and the synthetic images are generated by applying diffusion processes. The results indicate that the diffusion model successfully synthesizes visually indistinguishable DFU images. 70% of the time, clinicians marked synthetic DFU images as real DFUs. However, clinicians demonstrate higher unanimous confidence in rating real images than synthetic ones. The study also reveals that FID and KID metrics do not significantly align with clinicians' assessments, suggesting alternative evaluation approaches are needed. The findings highlight the potential of diffusion models for generating synthetic DFU images and their impact on medical training programs and research in wound detection and classification.Comment: 8 pages, 3 figures, 6th Workshop on AI for Aging, Rehabilitation and Intelligent Assisted Living at European Conference on Machine Learning, Italy, 202

Effectiveness of Lesson Study on Teachers Professional Skills in Hamadan Province Exceptional Education

The purpose of this study was to determine the effectiveness of the lesson study on professional skills in Hamadan province Exceptional Education Teachers’. Research method was ex-post facto. The population was all of exceptional teachers’ in Hamadan Province in 2013-2014 academic year. Through stratified random sampling method, 320 teachers were selected as sample. Data gathering tools included questionnaires of providing learning opportunities, interest in increasing knowledge and job skills, class organization and management, and observation forms included applying learning theories, use of teaching aids and education supplies, use of correct methods of assessment, encourage students to work in groups and test of familiar with theories of learning. Results showed that lesson study groups and other have significant differences in variables of providing learning opportunities (t=2.23;

Quantum and classical control of single photon states via a mechanical resonator

Optomechanical systems typically use light to control the quantum state of a mechanical resonator. In this paper, we propose a scheme for controlling the quantum state of light using the mechanical degree of freedom as a controlled beam splitter. Preparing the mechanical resonator in non-classical states enables an optomechanical Stern–Gerlach interferometer. When the mechanical resonator has a small coherent amplitude it acts as a quantum control, entangling the optical and mechanical degrees of freedom. As the coherent amplitude of the resonator increases, we recover single photon and two-photon interference via a classically controlled beam splitter. The visibility of the two-photon interference is particularly sensitive to coherent excitations in the mechanical resonator and this could form the basis of an optically transduced weak-force sensor

Making tourist guidance systems more intelligent, adaptive and personalised using crowd sourced movement data

Ambient intelligence (AmI) provides adaptive, personalized, intelligent, ubiquitous and interactive services to wide range of users. AmI can have a variety of applications, including smart shops, health care, smart home, assisted living, and location-based services. Tourist guidance is one of the applications where AmI can have a great contribution to the quality of the service, as the tourists, who may not be very familiar with the visiting site, need a location-aware, ubiquitous, personalised and informative service. Such services should be able to understand the preferences of the users without requiring the users to specify them, predict their interests, and provide relevant and tailored services in the most appropriate way, including audio, visual, and haptic. This paper shows the use of crowd sourced trajectory data in the detection of points of interests and providing ambient tourist guidance based on the patterns recognised over such data

Concomitant pulmonary tuberculosis and tuberculous appendicitis in a recipient of a renal transplant: a case report

<p>Abstract</p> <p>Introduction</p> <p>Tuberculosis is still a serious infection among recipients of renal transplants. Although the ileocecal region is the most affected part in intestinal tuberculosis, acute tuberculous appendicitis is quite a rare entity. We report a case of concomitant pulmonary tuberculosis and tuberculous appendicitis in a recipient of a renal transplant.</p> <p>Case presentation</p> <p>A 27-year-old Iranian woman, who had been the recipient of a renal transplant five years earlier, presented with a two-week history of coughing, fever and weight loss. The cause of her end-stage renal disease was chronic pyelonephritis. There were fine crackles noted during a chest examination, and a plain chest radiography showed fine miliary nodules throughout her entire lung fields. Sputum and bronchial aspirate examination was positive for acid-fast bacilli, suggestive of <it>Mycobacterium tuberculosis </it>infection. A chest computed tomography scan revealed widespread miliary nodules, compatible with miliary tuberculosis. She developed severe abdominal pain and abdominal surgery disclosed a perforated appendicitis. Histopathological examination of the resected appendix revealed widespread caseating epithelioid granulomas, suggestive of tuberculosis.</p> <p>Conclusion</p> <p>Our case report highlights a rare presentation of tuberculosis in a patient who has undergone renal transplant. Such unusual presentation of tuberculosis, particularly among patients receiving potent immunosuppressive protocols, should be considered by clinicians.</p