Background: Psychological First Aid (PFA) is practiced worldwide. This practice in English is guided through a small collection of training manuals. Despite ubiquitous practice and formal training materials, little is known about what topics are covered and in what depth in these influential manuals. As such, we analyzed the topic structure of these training manuals.

The article presents an original method of communication and data exchange in a robotic machining station consisting of two robots, a positioner and a 3D optic scanner. The task of one of the robots, equipped with a 3D optic scanner, was to receive point cloud of a detail (mould) attached to the positioner table. After detail digitalisation, the received point cloud was adjusted to (compared with) a model detail in the form of a CAD file in the Atos Professional software. In the software, casting material excesses were received in places selected on the detail. Values of the excesses and their coordinates were saved in the script and sent to the robot controller using TCP/IP protocol. The other of robots, equipped with the force control addition and the option of obtaining various processing tools, received sent excess and its coordinates. The other robot adjusted the processing parameters to random excesses, the value of which was received from measurements of the optic scanner of the first robot.

The aim of the research presented in this article is analysis of the effect of the surface treatment method on the static and fatigue strength of flash butt welded bandsaw blades. A 1-mm-thick 75Cr1 cold-work tool steel sheet used for bandsaw blades was used as the test material. Fractographic studies of the fatigue fractures and fractures formed in static tests were also carried out. The static strength tests showed sandblasting the weld surface had no significant effect on the load capacity of the joint. However, the sandblasted specimens showed a higher repeatability of the load capacity (lower standard deviation). In the case of both analyzed sample variants of specimens, sandblasted and non-sandblasted, the number of cycles at which the sample was damaged decreases with the percentage increase of the stress amplitude. When loading the samples with a stress amplitude value in the range between 400 and 690 MPa, sandblasting of the weld surface increased the average value of destructive cycles by about 10–86% (depending on the stress amplitude) compared to non-sandblasted joints. The sandblasting process introduces compressive stresses in the surface layer of the welds, therefore the variable tensile load acting on the sample requires a greater number of cycles before the fatigue cracks initiate and propagate. In the case of all specimens, a ductile fracture was observed. It was also found that, regardless of the variable stress amplitude, sandblasting has a positive effect on reducing the standard deviation of fatigue test results.

The industrial X-ray inspection of turbine blade castings requires reliable and auditable decision support, yet defect indications are subtle, and data availability is limited. This study quantitatively assesses the diagnostic potential of handcrafted image descriptors by evaluating class separability in feature space, independently of any trained classifier. The dataset comprises 1600 16-bit DICOM radiograms of 200 blades (eight views per blade), including 156 defective images with 207 localized defects. Standardized 32 × 32 ROI patches were sampled randomly in the vicinity of indications and from defect-free regions to reduce sample correlation and to emulate localization uncertainty. Feature vectors were extracted using five descriptor families—first-order statistics, GLCM/Haralick, FFT and wavelet (DWT) features, Gabor filters, and LBP—and the standardized z-score. Separability was ranked using complementary distribution-based and distance-based metrics grouped into three sets, and the results were min–max-normalized to enable TOP-5 comparisons. Spectral descriptors, particularly DWT wavelets and FFT combined with DWT, consistently achieved the highest scores in distributional metrics, supporting a lightweight screening profile. In contrast, richer combinations dominated multidimensional geometric metrics, indicating benefits from multi-perspective representations for offline analysis. The proposed metric-driven framework provides an interpretable basis for representation selection prior to classifier development under industrial constraints.