The service life of rails would be remarkably reduced owing to the increase of axle load, which can induce the occurrence of damages such as cracks, collapse, fat edges, etc. Laser cladding, which can enhance the mechanical properties of the rail by creating a coating, has received great attention in the area of the rails due to the attractive advantages such as low input heat, small heat-affected zone, and small deformation. In this paper, recent developments in the microstructural characteristics and mechanical properties of a cladded layer on the rail are reviewed. The method of process optimization for enhancing the properties of a cladded layer are discussed. Finally, the trend of future development is forecasted.Over time, the field of robotics has provided solutions to automate routine tasks in different scenarios. In particular, libraries are awakening great interest in automated tasks since they are semi-structured environments where machines coexist with humans and several repetitive operations could be automatically performed.  https://www.selleckchem.com/products/triptolide.html  In addition, multirotor aerial vehicles have become very popular in many applications over the past decade, however autonomous flight in confined spaces still presents a number of challenges and the use of small drones has not been reported as an automated inventory device within libraries. This paper presents the UJI aerial librarian robot that leverages computer vision techniques to autonomously self-localize and navigate in a library for automated inventory and book localization. A control strategy to navigate along the library bookcases is presented by using visual markers for self-localization during a visual inspection of bookshelves. An image-based book recognition technique is described that combines computer vision techniques to detect the tags on the book spines, followed by an optical character recognizer (OCR) to convert the book code on the tags into text. These data can be used for library inventory. Misplaced books can be automatically detected, and a particular book can be located within the library. Our quadrotor robot was tested in a real library with promising results. The problems encountered and limitation of the system are discussed, along with its relation to similar applications, such as automated inventory in warehouses.The ability of long-term thermo-oxidative resistance is very important for elastomers in application. However, many conventional antioxidants are difficult to realize the long-term thermo-oxidative resistance. To overcome this limitation, a design strategy is introduced by combing elastomers with MXene and natural rubber (NR) is chosen as a model material. MXene is efficient in absorbing oxygen and the generated free radicals in the NR matrix and can inhibit the diffusion of oxygen toward the interior. Moreover, MXene, like graphene and carbon black, absorbs molecular chains, inhibiting the migration of MXene toward the surface of the sample. Such characteristics of MXene endow NR/MXene with the long-term outstanding thermo-oxidative resistance. For example, after three days of the thermo-oxidative process for NR/MXene, the tensile strength is 19 MPa and the retention of tensile strength is 63%, which far exceeds the effects of conventional antioxidants. This work not only provides a good guide for the universal design of elastomers with long-term thermo-oxidative resistance but also expands the application of MXene.The recent refugee crisis presented a huge challenge for the Swedish mental health workforce. Hence, innovative mental health workforce solutions were needed. Unaccompanied refugee minors (URM) are a particularly vulnerable refugee group. Teaching Recovery Techniques (TRT) was introduced as a community-based intervention utilising trained lay counsellors in a stepped model of care for refugee youth experiencing trauma symptoms. Professionals (e.g., teachers, social workers) can deliver the Cognitive Behavioural Therapy-based intervention after a brief training. A point of debate in this workforce solution is the readiness of trained lay counsellors to deal with potentially demanding situations like disclosure of suicidal ideation. This study aimed to explore the TRT trained lay counsellors' experiences of procedures upon URM's disclosure of suicidal ideation. Individual semi-structured interviews with TRT trained lay counsellors were conducted, then analysed using systemic text condensation. The analysis revealed four themes "Importance of safety structures", "Collaboration is key", "Let sleeping dogs lie" and "Going the extra mile". Dealing with suicidal ideation is challenging and feelings of helplessness occur. Adding adequate supervision and specific training on suicidal ideation using role play is recommended. Collaboration between agencies and key stakeholders is essential when targeting refugee mental health in a stepped care model.Deep learning methods have been successfully applied to image processing, mainly using 2D vision sensors. Recently, the rise of depth cameras and other similar 3D sensors has opened the field for new perception techniques. Nevertheless, 3D convolutional neural networks perform slightly worse than other 3D deep learning methods, and even worse than their 2D version. In this paper, we propose to improve 3D deep learning results by transferring the pretrained weights learned in 2D networks to their corresponding 3D version. Using an industrial object recognition context, we have analyzed different combinations of 3D convolutional networks (VGG16, ResNet, Inception ResNet, and EfficientNet), comparing the recognition accuracy. The highest accuracy is obtained with EfficientNetB0 using extrusion with an accuracy of 0.9217, which gives comparable results to state-of-the art methods. We also observed that the transfer approach enabled to improve the accuracy of the Inception ResNet 3D version up to 18% with respect to the score of the 3D approach alone.Unmanned aerial vehicles (UAVs) have been widely used in search and rescue (SAR) missions due to their high flexibility. A key problem in SAR missions is to search and track moving targets in an area of interest. In this paper, we focus on the problem of Cooperative Multi-UAV Observation of Multiple Moving Targets (CMUOMMT). In contrast to the existing literature, we not only optimize the average observation rate of the discovered targets, but we also emphasize the fairness of the observation of the discovered targets and the continuous exploration of the undiscovered targets, under the assumption that the total number of targets is unknown. To achieve this objective, a deep reinforcement learning (DRL)-based method is proposed under the Partially Observable Markov Decision Process (POMDP) framework, where each UAV maintains four observation history maps, and maps from different UAVs within a communication range can be merged to enhance UAVs' awareness of the environment. A deep convolutional neural network (CNN) is used to process the merged maps and generate the control commands to UAVs.