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The article presents issues concerning processes of wear of devices taking part in the production process in a concrete mixing plant and in the supplying of concrete to the construction place. Producers put the strength of concrete as the main indicator of quality. The processes of interaction of each component of concrete mixtures for operating the equipment are placed on a further location. This article pays attention to the processes that cause wear of individual components of equipment, which in turn can significantly affect the cost and quality of the concrete. The article presents studies results of the operation of a real object on the example of the concrete plant and concrete pump.

The main argument of the thesis is that the dominant form of economics and correlative form of economy - despite its apparently secular character - contains an inherently cryptotheological dimension. A theological analysis exposes the dominant economics/economy as an instance of ‘law’ understood (after saint Paul Franz Kafka and Walter Benjamin) as a process engaging the subject into an infinite endeavour of justifying oneself by one’s own works. Within the framework of the dominant economics/economy, all labour is formalised as steaming from lack and unrest and the final end of action is formalised as non-action. Therefore peace can only be conceptualised as a perfect lack of action (viz. death). As a consequence death itself becomes the final end, that cannot be achieved as long as the subject lives. The analysis is based on a close-reading of the works prominent economists, focusing on the exponents of the Austrian School - Mises and Hayek - who as I try to prove, express the theological prejudgements of the dominant economics/economy in the most radical and philosophically stimulating manner. The thesis is also a polemic with these critics of the dominant economics/economy who state that it could be effectively criticised for being simply anti-natural, atemporal and value-free science/practice. My point is that a viable critique of the dominant mode of economic acting and thinking cannot be constructed, unless the fact that the hegemonic economic model actually makes use of the concepts of time, judgement and nature is taken into consideration. Only when the way the dominant economics/economy uses the concepts of economy as natural environment, economics as an art of allocation of time and as a value-saturated theory - elaboration of alternatives (including an alternative formalisation of productive labour) might become possible.

What computational structures emerge in transformers trained on next-token prediction? In this work, we provide evidence that transformers implement constrained Bayesian belief updating -- a parallelized version of partial Bayesian inference shaped by architectural constraints. We integrate the model-agnostic theory of optimal prediction with mechanistic interpretability to analyze transformers trained on a tractable family of hidden Markov models that generate rich geometric patterns in neural activations. Our primary analysis focuses on single-layer transformers, revealing how the first attention layer implements these constrained updates, with extensions to multi-layer architectures demonstrating how subsequent layers refine these representations. We find that attention carries out an algorithm with a natural interpretation in the probability simplex, and create representations with distinctive geometric structure. We show how both the algorithmic behavior and the underlying geometry of these representations can be theoretically predicted in detail -- including the attention pattern, OV-vectors, and embedding vectors -- by modifying the equations for optimal future token predictions to account for the architectural constraints of attention. Our approach provides a principled lens on how architectural constraints shape the implementation of optimal prediction, revealing why transformers develop specific intermediate geometric structures.

The vast majority of real-world domains feature both discrete and continuous be-haviour to some extent. Translating them into Automated Planning problems is diÿcult, and requires a very expressive modelling language. Furthermore, solving problems in these hybrid domains is challenging for planners due to non-linear sys-tem dynamics, vast search spaces, and a wide range of domain features. Despite these obstacles, planning in hybrid domains has been a growing area in Artificial Intelligence. Eÿcient heuristics are key to solving problems in hybrid domains. This dissertation describes research into domain-independent heuristics designed specifically for mixed discrete-continuous planning problems defined in the PDDL+ modelling language with a particular focus on aerospace applications. To tackle hybrid planning problems, we exploit the planning-via-discretisation approach where the continuous dynamics of a model is approximated with uniform time steps and step-functions. Building on previous research in Automated Plan-ning and model checking, we define a set of domain-independent heuristics designed to reason with all aspects of the PDDL+ feature set as well as non-linear system dynamics. First, we present a relaxation-based heuristic, Staged Relaxed Planning Graph+ (SRPG+) inspired by the Relaxed Planning Graph (RPG) approach used in temporal and numerical planning. We also extend the SRPG+ to validation-free discretisation-based planning. Second, we describe the Policy Abstraction Database (PADB), an extension to the Pattern Database (PDB) heuristic for PDDL+ do-mains. It relies on solving an abstracted and relaxed version of the problem and uses the relaxed solution as a guide to solving the original problem. Next, we define the Polyhedra-based PDB (PolyPDB), an abstraction-based heuristic adapted from state-of-the-art model checking techniques and Pattern Databases. Finally, given that while the field of planning in hybrid domains is growing, the range of avail-able benchmark domains is significantly underdeveloped, we discuss the modelling of novel hybrid domains in PDDL+ and innovative uses for the PDDL+ language. The novel heuristics have been implemented in DiNo, a new heuristic PDDL+ planner. It is based on UPMurphi, a planner set in the planning-as-model-checking paradigm. Results show that our heuristics significantly improve the rate of explo-ration of the search space and facilitate eÿciently finding the goal on a range of novel and existing benchmark PDDL+ domains.

Chest X-rays (CXRs) are widely used for diagnosing respiratory diseases, including the recent example of COVID-19. Supervised deep learning techniques can help detect cases faster and monitor disease progression. However, they are usually developed using coarser data annotations, which may insufficiently capture the heterogeneous disease portrait. We propose the pipeline called CIRCA ( https://circa.aei.polsl.pl ) for a CXR-based screening support system, developed using 6 diverse datasets. Our tool includes lung segmentation, quantitative assessment of data heterogeneity, and a hierarchical three-class decision system using a convolutional network and radiomic features. Lung segmentation showed an accuracy of ~ 94% in the validation and test sets, while classification accuracy was equal 86%, 83%, and 72% for normal, COVID-19, and other pneumonia classes in the independent test set. Three radiomically distinct subtypes were identified per class. In the hold-out set, the classification subtype-specific cross-dataset NPV ranged from 95 to 100%, with PPV from 86 to 100% for all subtypes except N3 (early stage or convalescent) and both C3 and P3 (probable co-occurrence of COVID-19). Using an independent test set gave similar results. The dataset-specific subtype proportions combined with various predictive qualities of subtypes partly explain the widely reported poor generalization of AI-based prediction systems.

We describe in detail the construction and characterization of a Peltier-cooled long-wavelength infrared (LWIR) position-sensitive detector (PSD) based on the lateral effect. The device was recently reported for the first time to the authors’ knowledge. It is a modified PIN HgCdTe photodiode, forming the tetra-lateral PSD, with a photosensitive area of 1 × 1 mm2, operating at 205 K in the 3–11 µm spectral range, capable of achieving a position resolution of 0.3–0.6 µm using 10.5 µm 2.6 mW radiation focused on a spot of the 1/e2 diameter 240 µm, with a box-car integration time of 1 µs and correlated double sampling.

Effective placement and compaction of the concrete mixture within the spans of prestressed bridges are essential for the proper anchoring and prestressing of tendons. The high density of reinforcement and location of the cable ducts present significant challenges, increasing the risk of void formation and structural irregularities, which can lead to failures during the prestressing process. Ground Penetrating Radar (GPR) emerges as a pivotal non-destructive testing method for diagnosing such complex prestressed structures. Utilizing high-frequency electromagnetic waves, GPR accurately detects and maps anomalies within hardened concrete, enabling precise identification of defect locations and their dimensions. The detailed imaging provided by GPR facilitates the development of targeted repair strategies and allows for the exclusion of concrete voids through selective invasive inspections in designated boreholes. This study presents the use of GPR for the investigation of anomalies and damage in prestressing tendons of a newly built concrete bridge. It underscores the critical role of GPR in enhancing the diagnostic and maintenance programs for prestressed bridge structures, thereby improving their overall integrity and longevity.

Awareness of social biases is crucial as they impact both individual behavior and societal outcomes. Whereas previous research indicates that self-awareness of ego-nonthreatening biases enhances self-regulation, the effects of self-awareness of ego-threatening biases remain underexplored. Preliminary findings suggest that awareness of ego-threatening biases related to rumination may lead to maladaptive states. However, these findings await replication with standardized instruments. To address this gap, we conducted two studies. In Study 1 ( N  = 1609), we developed and validated the 12-item Self-Awareness of Ego-Threatening Biases Questionnaire (SAETBQ). Consistent with our hypotheses, self-awareness of ego-threatening biases (as measured by the SAETBQ) correlated with higher moral disengagement, lower self-diagnostic motive, and lower integrative self-knowledge, indicating a tendency towards ego deterioration, whereas self-awareness of ego-nonthreatening biases (as measured by the Metacognitive Self questionnaire) showed the opposite pattern of correlations, indicating a tendency towards beneficial self-regulation. In Study 2 ( N  = 681), Dark Triad traits correlated positively and Light Triad traits negatively with self-awareness of ego-threatening biases. These results underscore the complex role of self-awareness in managing cognitive biases.

Background: Sleep disorders are a widespread phenomenon, and the number of individuals suffering from them is increasing every year, especially among young adults. Currently, the literature lacks studies that cover both countries with different levels of development and a period before the announcement of the ongoing COVID-19 pandemic. Therefore, this study aims to globally assess the prevalence of insomnia and daytime sleepiness among students and assess their quality of life. Methods: For this purpose, our own questionnaire was distributed online via Facebook.com. In addition to the questions that assessed socioeconomic status, the survey included psychometric tools, such as the Athens insomnia scale (AIS), the Epworth sleepiness scale (ESS), and the Manchester short assessment of the quality of life (MANSA). The survey distribution period covered 31 January 2016 to 30 April 2021. Results: The survey involved 20,139 students from 60 countries around the world. The vast majority of the students were women (78.2%) and also those residing in countries with very high levels of development and/or high GDP (gross domestic product) per capita at 90.4% and 87.9%, respectively. More than half (50.6%) of the respondents (10,187) took the survey before the COVID-19 pandemic was announced. In the group analyzed, 11,597 (57.6%) students obtained a score indicative of insomnia and 5442 (27.0%) a score indicative of daytime sleepiness. Women, low-income residents, and nonmedical students were significantly more likely to have scores indicating the presence of insomnia. Individuals experiencing both sleepiness (B = −3.142; p < 0.001) and daytime sleepiness (B = −1.331; p < 0.001) rated their quality of life significantly lower. Conclusions: Insomnia and excessive daytime sleepiness are common conditions among students worldwide and are closely related. The COVID-19 pandemic significantly altered students’ diurnal rhythms, which contributed to an increase in insomnia. Students in countries with a high GDP per capita index are significantly less likely to develop insomnia compared to the residents of countries with a low GDP per capita index. Sleep disorders definitely reduce the quality of life of students.

At the end of the last century, genetic studies reported that genetic information is not transmitted solely by DNA, but is also transmitted by other mechanisms, named as epigenetics. The well-described epigenetic mechanisms include DNA methylation, biochemical modifications of histones, and microRNAs. The role of altered epigenetics in the biology of various fibrotic diseases is well-established, and recent advances demonstrate its importance in the pathogenesis of pulmonary fibrosis—predominantly referring to idiopathic pulmonary fibrosis, the most lethal of the interstitial lung diseases. The deficiency in effective medications suggests an urgent need to better understand the underlying pathobiology. This review summarizes the current knowledge concerning epigenetic changes in pulmonary fibrosis and associations of these changes with several cellular pathways of known significance in its pathogenesis. It also designates the most promising substances for further research that may bring us closer to new therapeutic options.