University of West Bohemia

Time‐varying meshes (TVMs), that is mesh sequences with varying connectivity, are a greatly versatile representation of shapes evolving in time, as they allow a surface topology to change or details to appear or disappear at any time during the sequence. This, however, comes at the cost of large storage size. Since 2003, there have been attempts to compress such data efficiently. While the problem may seem trivial at first sight, considering the strong temporal coherence of shapes represented by the individual frames, it turns out that the varying connectivity and the absence of implicit correspondence information that stems from it makes it rather difficult to exploit the redundancies present in the data. Therefore, efficient and general TVM compression is still considered an open problem. We describe and categorize existing approaches while pointing out the current challenges in the field and hint at some related techniques that might be helpful in addressing them. We also provide an overview of the reported performance of the discussed methods and a list of datasets that are publicly available for experiments. Finally, we also discuss potential future trends in the field.

Introduction Upper limb (UL) impairment is common in people with multiple sclerosis (pwMS), and functional recovery of the UL is a key rehabilitation goal. Technology-based approaches, like virtual reality (VR), are increasingly promising. While most VR environments are task-oriented, our clinical approach integrates neuroproprioceptive ‘facilitation and inhibition’ (NFI) principles. To advance this, we developed immersive VR software based on NFI principles targeting UL function and sit-to-stand ability. This study aims to evaluate the effectiveness of this VR therapy compared with conventional NFI-based physical therapy in pwMS. Our study uniquely applies advanced imaging techniques, along with biological molecular assessments, to explore adaptive processes induced by VR rehabilitation. Methods and analysis This double-arm, randomised, assessor-blinded, controlled trial runs over 2 months (1 hour, 2 times per week). PwMS with mild to severe disability will receive either VR therapy or real-world physical therapy. Primary outcomes include the nine-hole peg test, box and block test, handgrip strength, tremor and five times sit-to-stand test. Secondary measures include the Multiple Sclerosis Impact Scale, the 5-level EQ-5D questionnaire and kinematic analysis. Adaptive processes will be monitored using imaging techniques (functional MRI and tractography), molecular genetic methods (long non-coding RNAs) and immune system markers (leukocytes, dendritic cells). The International Classification of Functioning, Disability and Health brief set for MS will map the bio-psycho-social context of participants. Ethics and dissemination This project and its amendments were approved by the Ethics Committee of the Institute for Clinical and Experimental Medicine and Thomayer Hospital (1983/21+4772/21 (G-21–02) and the Ethics Committee of Kralovske Vinohrady University Hospital (EK-VP/38/0/2021) in Prague, Czechia (with single enrolment). The findings of this project will be disseminated through scientific publications, conferences, professional networks, public engagement, educational materials and stakeholder briefings to ensure a broad impact across clinical, academic and public domains. Trial registration number clinicaltrials.gov ( NCT04807738 ).

Humans are associated with varied decision-making skills in their daily lives. These involve a range of metacognitive abilities that primarily focus on being aware consciously or unconsciously while engaging in a particular action. In a given societal condition, finance plays a crucial role in understanding socio-economic and emotional factors. The article aims to understand the fundamental aspects of decision-making skills across adulthood within the crossroads of understanding the elements of finance, cognitive abilities, behavioral responses, and beliefs while executing an action. Lastly, the article is an ongoing thesis and will be continuing with research which will eventually be supported by data collection, thus helping us understand human behavioral system.

This dataset originates from TeensLab, a consortium of Spanish Universities dedicated to behavioral research involving Spanish teenagers. The dataset contains data from 33 distinct educational institutions across Spain, accounting for a total of 5,890 students aged 10 to 23 (M = 14.10, SD = 1.94), representing various educational levels such as primary school, secondary school, sixth form and vocational training. The main dimensions covered in this dataset include (i) economic preferences, (ii) cognitive abilities and (iii) strategic thinking. Additionally, a range of supplementary variables is included alongside socio-demographic factors, capturing data on aspects like physical appearance, mood and expectations, among others.

Black aluminum is a material characterized by high surface porosity due to columnar growth and exhibits unique optical properties that make it attractive for applications such as light trapping, infrared detection, and passive thermal radiation cooling. In this study, we correlate the structural and optical properties of black aluminum by comparing it with conventional reflective aluminum layers. These layers of varying thicknesses were deposited on fused silica substrates, and their optical properties were analyzed. COMSOL simulations, supported by experimental data, reveal that black aluminum’s structure leads to a significant reduction in visible light reflectivity and an increase in emissivity in the near- and mid-infrared ranges. This enhanced emissivity is partly due to the presence of aluminum nitride (AlN)grain boundaries and an oxidized surface layer. Optically, black aluminum differs significantly from reflective aluminum by presenting a reflectivity below 5% in visible wavelength and an average emissivity of approximately 0.4−0.5 from 1.2 to 20 μm. Thermally, it possesses approximately ten times lower thermal conductivity and doubles the volumetric heat capacity. These differences are attributed to its porous structure, nanoscale crystallites, and the presence of aluminum nitrides and oxides within the material.

Point clouds have become a popular training data for many practical applications of machine learning in the fields of environmental modeling and precision agriculture. In order to reduce high space requirements and the effect of noise in the data, point clouds are often transformed to a structured representation such as a voxel grid. Storing, transmitting and consuming voxelized geometry, however, remains a challenging problem for machine learning pipelines running on devices with limited amount of on-chip memory with low access latency. A viable solution is to store the data in a compact encoded format, and perform on-the-fly decoding when it is needed for processing. Such on-demand expansion must be fast in order to avoid introducing substantial additional delay to the pipeline. This can be achieved by parallel decoding, which is particularly suitable for massively parallel architecture of GPUs on which the majority of machine learning is currently executed. In this paper, we present such method for efficient and parallelizable encoding/decoding of voxelized geometry. The method employs multi-level context-aware prediction of voxel occupancy based on the extracted binary feature prediction table, and encodes the residual grid with a pointerless sparse voxel octree (PSVO). We particularly focused on encoding the datasets of voxelized trees, obtained from both synthetic tree models and LiDAR point clouds of real trees. The method achieved 15.6% and 12.8% reduction of storage size with respect to plain PSVO on synthetic and real dataset, respectively. We also tested the method on a general set of diverse voxelized objects, where an average 11% improvement of storage space was achieved.

This chapter explores two groundbreaking energy transition initiatives in the Czech Republic, centred on the municipalities of Knezice and Jindrichovice pod Smrkem. It delves into Knezice's successful implementation of biogas and biomass projects, and Jindrichovice pod Smrkem's focus on wind energy, including the establishment of an Ecological Innovation Centre. These case studies serve as exemplars of how rural areas in the Czech Republic are advancing in renewable energy, showcasing the potential for sustainable development and energy independence through innovative approaches to utilising local resources.

The recent research has demonstrated both the potential of building information management (BIM) for the 3D cadastre, especially for legal spaces of building units and the advantage of the combination of physical and legal boundaries for their visualization. During the lifetime cycle of the building and the units within, the geometry of units can change. Then, it is necessary to promote the transition from the updated BIM model to connected systems. Considering this, the paper focuses on a unique identification of building units between BIM and GIS. The main idea is that each feature (in our case, the building unit) receives a unique identifier when it is created, which is then used in every system during its life cycle. Each of these domains has its own specific technical requirements for the unique identification. The aim is to have just one identifier of the building unit regardless of the selected information system to make an effective update. The paper further aims to model the 3D legal spaces of building units using BIM and their storage inside the external spatial database based on the CityGML standard data model. The proposed solution facilitates the storage of the 3D legal spaces of building units together with their physical counterparts in one environment based on the widely recognized OGC standard. Furthermore, as many countries still use 2D parcel-based cadastre, the proposed solution also allows for the connection of the existing cadastral information systems with these externally stored 3D legal spaces without the necessity of changing the systems.

The aim of this study was to investigate the potential of polymeric cell structures for the production of energy absorbers and to focus on the geometric optimization of polymeric cell structures producible by additive technologies to achieve the required deformation characteristics, high material efficiency and the low weight of the resulting absorber. A detailed analysis of different types of cell structures (different lattice structures and honeycombs) and their properties was performed. Honeycombs, which have been further examined in more detail, are best suited for absorbing large amounts of energy and high levels of material efficiency at known load directions. Honeycombs have the potential to absorb large amounts of energy relative to their low weight and their deformation characteristics have a relatively constant course. Honeycombs have the major disadvantage of an initial peak. However, this peak can be removed by appropriately adjusting the geometry of the honeycomb. Thanks to the possibilities that additive technology allows us, honeycombs with progressive wall thickness have been designed and researched. The output of this study is a detailed analysis of the properties and several design recommendations for the design of a honeycomb with a progressive wall thickness to achieve the required properties.

Can a machine understand the meanings of natural language? Recent developments in the generative large language models (LLMs) of artificial intelligence have led to the belief that traditional philosophical assumptions about machine understanding of language need to be revised. This article critically evaluates the prevailing tendency to regard machine language performance as mere syntactic manipulation and the imitations of understanding, which is only partial and very shallow, without sufficient grounding in the world. The article analyses the views on possible ways of grounding as a condition for successful understanding in LLMs and offers an alternative way in view of the prevailing belief that the success of understanding depends mainly on the referential grounding. An alternative conception seeks to show that semantic fragmentism offers a viable account of natural language understanding and explains how LLMs ground the meanings of linguistic expressions. Uncovering how meanings are grounded allows us to also explain why LLMs’ ability to understand is possible and so remarkably successful.

Metal powders for additive manufacturing are expensive, and producing new ones from mined metals has a negative ecological impact. In this work, recycled and reused metal powders from MS1 steel for direct metal laser sintering (DMLS) 3D printing were evaluated in the laboratory. The powders were recycled by melting followed by gas atomizing. Virgin, recycled, and reused metal powders were evaluated using scanning electron microscopy (SEM), energy-dispersive X-ray spectroscopy (EDS), X-ray diffraction (XRD), metallography analysis, microhardness measurements, particle size distribution (PSD), shape factor by digital image processing (DIP), and flowability testing. The results showed that the particle distribution was modified after recycling. Kurtosis analysis revealed a reduction from −0.64 for virgin powders to −1.29 for recycled powders. The results demonstrated a positive skewness, indicating that the recycled powder contained a greater proportion of smaller particles. The shape factor was also modified and changed from 1.57 for virgin powders to 1.28 for recycled powders. The microstructure also changed, and austenite was found in the recycled powders. The microhardness of recycled powder decreased by 39% compared to the virgin powder. Recycled powders did not flow, using two different funnels to evaluate their flowability. The flowability of used powder was reduced from 4.3 s to 2.9 s.

In this work we discuss stability and nondegeneracy properties of some special families of minimal hypersurfaces embedded in $\mathbb {R}^m\times \mathbb {R}^n$ with $m,n\ge 2$. These hypersurfaces are asymptotic at infinity to a fixed Lawson cone $C_{m,n}$. In the case $m+n\ge 8$, we show that such hypersurfaces are strictly stable and we provide a full classification of their bounded Jacobi fields, which in turn allows us to prove the non-degeneracy of such surfaces. In the case $m+n\le 7$, we prove that such hypersurfaces have infinite Morse index.

Surface roughness plays a critical role in ultrashort pulse laser ablation, particularly for industrial applications using burst mode operations, multi-pulse laser processing, and the generation of laser-induced periodic surface structures. Hence, we address the impact of surface roughness on the resulting laser ablation topography, comparing predictions from a simulation model to experimental results. We present a comprehensive multi-scale simulation framework that first employs finite-difference-time-domain simulations for calculating the surface fluence distribution on a rough surface measured by atomic-force-microscopy followed by the two-temperature model coupled with hydrodynamic/solid mechanics simulation for the initial material heating. Lastly, a computational fluid dynamics model for material relaxation and fluid flow is developed and employed. Final state results of aluminum and AISI 304 stainless steel simulations demonstrated alignment with established ablation models and crater dimension prediction. Notably, Al exhibited significant optical scattering effects due to initial surface roughness of 15 nm—being 70 times below the laser wavelength -leading to localized, selective ablation processes and substantially altered crater topography compared to idealized conditions. Contrary, AISI 304 with ${R}_{\text{q}}$ surface roughness of 2 nm showed no difference. Hence, we highlight the necessity of incorporating realistic, material-specific surface roughness values into large-scale ablation simulations. Furthermore, the induced local fluence variations demonstrated the inadequacy of neglecting lateral heat transport effects in this context.

Integral transformations are a useful mathematical apparatus for modelling the gravitational field. They represent the mathematical basis for formulating integral estimators of gravitational field values, including error propagation. The theoretical and practical aspects of integral transformations traditionally used for calculating geoid/quasigeoid heights in geodesy, such as Stokes’s and Hotine’s integral transformations, have already been studied. However, theoretical and practical concepts regarding other integral transformations, including non-isotropic (azimuth-dependent) transformations, have yet to be explored. One of the basic assumptions of integral transformations is global data coverage. However, the availability of ground measurements is frequently limited. In practice, the global integral is divided into two complementary regions: the near- and far–zones. Non–negligible systematic effects of data in the far-zone require accurate evaluation. For this purpose, a new software library is developed in the MatLab environment to calculate far–zone effects for integral transformations for gravitational potential gradients up to the third order. The library contains scripts for calculating integral kernels, error kernels, truncation error coefficients, and far-zone effects for a selected set of input parameters. This contribution deals with implementing equations defining far-zone effects and the subsequent numerical testing of the library functionality. Closed-loop tests were carried out using gravitational potential functionals generated from a global Earth’s gravitational field model to check the numerical correctness of the software library.

This paper examines (1) teacher candidates’ knowledge of recent migration trends and language accreditation policies, (2) their attitudes towards language testing for migration, and (3) changes in their perceptions after exposure to training and migrant testimonies. Conducted over two years (2021–2023), this mixed-methods study involved 395 candidates from Spain (166), Poland (125), and the Czech Republic (104). Participants completed a migration data test and a pre-survey employing The Language Accreditation for Migration Attitude s Scale (LAMAS), an ad-hoc ten-item scale with three dimensions. The intervention included a comprehensive learning module with readings and visual resources, and exposure to migrant testimonies, followed by a LAMAS-based post-survey, classroom discussions, and reflective reports. Data analysis via SPSS and QDA Miner revealed significant correlations between attitudes and home countries (Kruskal-Wallis test), and notable attitude changes post-treatment (Wilcoxon Sign-Ranked test). The study underscores the enhanced awareness and understanding of language assessment challenges in migration, highlighting the need to integrate these issues into teacher training programs.