Emergency departments (ED) in hospitals usually suffer from crowdedness and long waiting times for treatment. The complexity of the patient's path flows and their controls come from the patient's diverse acute level, personalized treatment process, and interconnected medical staff and resources. One of the factors, which has been controlled, is the dynamic situation change such as the patient's composition and resources' availability. The patient's scheduling is thus complicated in consideration of various factors to achieve ED efficiency. To address this issue, a deep reinforcement learning (RL) is designed and applied in an ED patients' scheduling process. Before applying the deep RL, the mathematical model and the Markov decision process (MDP) for the ED is presented and formulated. Then, the algorithm of the RL based on deep Q-networks (DQN) is designed to determine the optimal policy for scheduling patients. To evaluate the performance of the deep RL, it is compared with the dispatching rules presented in the study. The deep RL is shown to outperform the dispatching rules in terms of minimizing the weighted waiting time of the patients and the penalty of emergent patients in the suggested scenarios. This study demonstrates the successful implementation of the deep RL for ED applications, particularly in assisting decision-makers under the dynamic environment of an ED.Construction sites are dangerous due to the complex interaction of workers with equipment, building materials, vehicles, etc. As a kind of protective gear, hardhats are crucial for the safety of people on construction sites. Therefore, it is necessary for administrators to identify the people that do not wear hardhats and send out alarms to them. As manual inspection is labor-intensive and expensive, it is ideal to handle this issue by a real-time automatic detector. As such, in this paper, we present an end-to-end convolutional neural network to solve the problem of detecting if workers are wearing hardhats. The proposed method focuses on localizing a person's head and deciding whether they are wearing a hardhat. The MobileNet model is employed as the backbone network, which allows the detector to run in real time. A top-down module is leveraged to enhance the feature-extraction process. Finally, heads with and without hardhats are detected on multi-scale features using a residual-block-based prediction module. Experimental results on a dataset that we have established show that the proposed method could produce an average precision of 87.4%/89.4% at 62 frames per second for detecting people without/with a hardhat worn on the head.Amphibian skin is a multifunctional organ that plays key roles in defense, breathing, and water balance. In this study, skin secretion samples of the fire salamander (Salamandra salamandra) were separated using RP-HPLC and de novo sequenced using MALDI-TOF MS/MS. Next, we used an in silico platform to screen antioxidant molecules in the framework of density functional theory. One of the identified peptides, salamandrin-I, [M + H]+ = 1406.6 Da, was selected for solid-phase synthesis; it showed free radical scavenging activity against DPPH and ABTS radicals. Salamandrin-I did not show antimicrobial activity against Gram-positive and -negative bacteria. In vitro assays using human microglia and red blood cells showed that salamandrin-I has no cytotoxicity up to the concentration of 100 µM. In addition, in vivo toxicity tests on Galleria mellonella larvae resulted in no mortality at 20 and 40 mg/kg. Antioxidant peptides derived from natural sources are increasingly attracting interest. Calcitriol manufacturer Among several applications, these peptides, such as salamandrin-I, can be used as templates in the design of novel antioxidant molecules that may contribute to devising strategies for more effective control of neurological disease.Barely visible impact damage (BVID) is one of the most dangerous types of structural damage in composites, since in most practical cases the application of advanced non-destructive testing (NDT) methods is required to detect and identify it. Due to its character of propagation, there are minor signs of structural damage on a surface, while the internal damage can be broad and complex both in the point of view of fracture mechanisms and resulting geometry of damage. The most common NDT method applied e.g., in aircraft inspections is ultrasonic testing (UT), which enables effective damage detection and localization in various environments. However, the results of such inspections are usually misestimated with respect to the true damage extent, and the quantitative analysis is biased by an error. In order to determine the estimation error a comparative analysis was performed on NDT results obtained for artificially damaged carbon fiber-reinforced composite structures using two UT methods and X-ray computed tomography (CT). The latter method was considered here as the reference one, since it gives the best spatial resolution and estimation accuracy of internal damage among the available NDT methods. Fusing the NDT results for a set of pre-damaged composite structures with various energy values of impact and various types of impactor tips applied for introducing damage, the evaluation of estimation accuracy of UT was possible. The performed analysis allowed for evaluation of relations between UT and X-ray CT NDT results and for proposal of a correcting factor for UT results for BVID in the analyzed composite structures.The rapid and non-destructive detection of mechanical damage to fruit during postharvest supply chains is important for monitoring fruit deterioration in time and optimizing freshness preservation and packaging strategies. As fruit is usually packed during supply chain operations, it is difficult to detect whether it has suffered mechanical damage by visual observation and spectral imaging technologies. In this study, based on the volatile substances (VOCs) in yellow peaches, the electronic nose (e-nose) technology was applied to non-destructively predict the levels of compression damage in yellow peaches, discriminate the damaged fruit and predict the time after the damage. A comparison of the models, established based on the samples at different times after damage, was also carried out. The results show that, at 24 h after damage, the correct answer rate for identifying the damaged fruit was 93.33%, and the residual predictive deviation in predicting the levels of compression damage and the time after the damage, was 2.