Silesian University of Technology

With the development of internet of things (IoT) technology and its wide application in urban traffic, the next-generation vehicle-to-everything (V2X) communication network should support high-capacity, ultra-reliable, and low-latency massive information exchange to provide unprecedentedly diverse user experiences. The development of the sixth-generation (6G) mobile communication technology will pave the way for realizing this vision. Reconfigurable intelligent surfaces (RISs), a critical 6G technology, is expected to make a big difference in V2X communications when used in conjunction with unmanned aerial vehicles (UAVs), allowing for extremely increased communication capacity and reduced latency. We propose a UAV-enhanced RIS-assisted V2X communication architecture (UR-V2X) suitable for urban three-dimensional (3D) IoT traffic and design an adapted MAC protocol UR-V2X-MAC to accomplish communication resource allocation and scheduling. The UAVs are used as access points and resource allocation centers, while the RISs are used as passive relays to assist V2X communication in proposed architecture. To improve the performance of UR-V2X-MAC, we use a distributed optimization algorithm in the message report phase of the protocol to maximize the system capacity by allocating the transmit power and alternately optimizing the RIS phase shift matrix. We analyze the delay and system capacity characteristics under different parameter settings through theoretical derivation and protocol performance simulation. Analysis and simulation results are presented to demonstrate that UR-V2X-MAC achieves a reduction in communication delay and a significant increase in system capacity through detailed design and alternate optimization compared to the existing V2X MAC protocol and no-RIS case.

Combining data from experiments on multispecies studies provides invaluable contributions to the understanding of basic disease mechanisms and pathophysiology of pathogens crossing species boundaries. The task of multispecies gene expression analysis, however, is often challenging given annotation inconsistencies and in cases of small sample sizes due to bias caused by batch effects. In this work we aim to demonstrate that an alternative approach to standard differential expression analysis in single cell RNA-sequencing (scRNA-seq) based on effect size profiles is suitable for the fusion of data from small samples and multiple organisms. The analysis pipeline is based on effect size metric profiles of samples in specific cell clusters. The effect size substitutes standard differentiation analyses based on p-values and profiles identified based on these effect size metrics serve as a tool to link cell type clusters between the studied organisms. The algorithms were tested on published scRNA-seq data sets derived from several species and subsequently validated on own data from human and bovine peripheral blood mononuclear cells stimulated with Mycobacterium tuberculosis. Correlation of the effect size profiles between clusters allowed for the linkage of human and bovine cell types. Moreover, effect size ratios were used to identify differentially regulated genes in control and stimulated samples. The genes identified through effect size profiling were confirmed experimentally using qPCR. We demonstrate that in situations where batch effects dominate cell type variation in single cell small sample size multispecies studies, effect size profiling is a valid alternative to traditional statistical inference techniques.

The use of waste foundry sand (WFS) in concrete production has gained attention as an eco-friendly approach to waste reduction and enhancing cementitious materials. However, testing the impact of WFS in concrete through experiments is costly and time-consuming. Therefore, this study employs machine learning (ML) models, including support vector regression (SVR), decision tree (DT), and AdaBoost regressor (AR) ensemble model to predict concrete properties accurately. Moreover, SVR was employed in conjunction with three robust optimization algorithms: the firefly algorithm (FFA), particle swarm optimization (PSO), and grey wolf optimization (GWO), to construct hybrid models. Using 397 experimental data points for compressive strength (CS), 146 for elastic modulus (E), and 242 for split tensile strength (STS), the models were evaluated with statistical metrics and interpreted using the SHapley Additive exPlanation (SHAP) technique. The SVR-GWO hybrid model demonstrated exceptional accuracy in predicting waste foundry sand concrete (WFSC) strength characteristics. The SVR-GWO hybrid model exhibited correlation coefficient values (R) of 0.999 for CS and E, and 0.998 for STS. Age was found to be a significant factor influencing WFSC properties. The ensemble model (AR) also exhibited comparable prediction accuracy to the SVR-GWO model. In addition, SHAP analysis revealed an optimal content of input variables in the concrete mix. Overall, the hybrid and ensemble models showed exceptional prediction accuracy compared to individual models. The application of these sophisticated soft computing prediction techniques holds the potential to stimulate the widespread adoption of WFS in sustainable concrete production, thereby fostering waste reduction and bolstering the adoption of environmentally conscious construction practices.

Accurate tuberculosis (TB) diagnosis remains challenging, especially in resource-limited settings. This study aims to assess the diagnostic performance of the QIAreach QuantiFERON-TB (QFT) assay, with a specific focus on comparing its diagnostic performance with the QuantiFERON-TB Gold Plus (QFT-Plus). We systematically reviewed relevant individual studies on PubMed, Scopus, and Web of Science up to January 20, 2024. The focus was on evaluating the diagnostic parameters of the QIAreach QFT assay for TB infection, which included sensitivity, specificity, positive predictive value (PPV), negative predictive value (NPV), positive likelihood ratio (PLR), negative likelihood ratio (NLR), and concordance with the QFT-Plus assay. QIAreach QFT demonstrated strong diagnostic performance with a pooled sensitivity of 99% (95% CI 95–100%) and specificity of 94% (95% CI 85–97%). Additionally, it showed a PLR of 15.6 (95% CI 6.5–37.5) and NLR of 0.01 (95% CI 0–0.03). The pooled PPV and NPV were 88% (95% CI 70–98%) and 100% (95% CI 99–100%), respectively. Concordance analysis with QFT-Plus revealed a pooled positive percent agreement of 98% (95% CI 88–100%) and pooled negative percent agreement of 91% (95% CI 81–97%), with a pooled overall percent agreement of 92% (95% CI 83–98). In conclusion, QIAreach QFT has shown promising diagnostic performance, with a strong concordance with QFT-Plus. However, further studies are needed to comprehensively evaluate its diagnostic performance in the context of TB infection.

In this work, a scalable quantum gate‐based algorithm for accelerating causal inference is introduced. Specifically, the formalism of causal hypothesis testing presented in [Nat Commun 10, 1472 (2019)] is considered. Through the algorithm, the existing definition of error probability is generalized, which is a metric to distinguish between two competing causal hypotheses, to a practical scenario. The results on the Qiskit validate the predicted speedup and show that in the realistic scenario, the error probability depends on the distance between the competing hypotheses. To achieve this, the causal hypotheses are embedded as a circuit construction of the oracle. Furthermore, by assessing the complexity involved in implementing the algorithm's subcomponents, a numerical estimation of the resources required for the algorithm is offered. Finally, applications of this framework for causal inference use cases in bioinformatics and artificial general intelligence are discussed.

The global shift towards sustainable transportation, exemplified by the increasing adoption of Electric Vehicles (EVs), represents a vital response to contemporary environmental and energy challenges. This study investigates the determinants of consumers' Desire to Purchase EVs within the unique context of the Medan market, where consumer preferences and market dynamics differ from more globally examined settings. Additionally, it explores the mediating roles of Consumer Awareness Campaigns and Local Industry Partnerships in shaping purchase intent. Through a comprehensive survey of 832 respondents in Medan, Indonesia, the study reveals crucial insights. Government incentives, including tax breaks and rebates, emerge as influential factors, significantly increasing consumers' inclination to adopt EVs. Equally vital is the accessibility and availability of charging infrastructure, which is pivotal in bolstering consumer confidence in EVs. Consumer perceptions and attitudes have a significant bearing on purchase intent, with positive perceptions regarding environmental benefits, cost savings, and driving experiences contributing to the heightened desire for EV ownership. Effective financial management strategies also play a positive role, emphasizing the significance of sound financial planning and resource allocation. Furthermore, this research emphasizes the instrumental role of Consumer Awareness Campaigns in shaping purchase intent. These campaigns are a powerful tool for elucidating the multifaceted advantages of EV ownership, encompassing environmental consciousness, economic feasibility, and enhanced driving experiences. Local Industry Partnerships within the Medan EV market equally contribute to heightened purchase intent, reflecting the synergistic effects of collaborative initiatives.

The development of the post-industrial society requires the acceleration of the integration of the national economy into the globalized economic space. This stage is characterized by active informatization of all spheres of life in society, which requires information security and cyber protection for high-quality information provision of the country’s population, intellectualization of economic processes and prevention of destructive informational influence on the social status of the individual. It is also necessary to consider that for Ukraine the specified stage is complicated by the hybrid war with Russia, which requires strengthening the protection of information from cyber-attacks and the formation of new approaches in preserving the quality of information on social networks. Therefore, the purpose of this article is the development of scientific and methodological approaches in information security management, strengthening its social significance. This requires solving a certain range of tasks of identifying and preventing socially dangerous information based on the use of economic and mathematical methods and models. The article highlights the main directions of socialization of modern technological development and theoretically substantiates its significant impact on human consciousness and behavior, puts a person in front of serious challenges and, under conditions of hybrid warfare, requires strengthening of information security in social networks. In the work, the components of information security are supplemented, its impact on social aspects of society’s life is highlighted. As a result of the research, the authors proposed a scientific and methodical approach to the construction of a system for countering the spread of socially dangerous information in social networks. Besides, its functional elements are highlighted. A method of combating the spread of harmful information in social networks has been developed, which solves the problem of information support of the decision-making process and includes providing the chosen and alternative options to the person making the decision, with the justification of the choice. This creates prerequisites for information support for making a well-founded management decision.

The article presents the conceptual assumptions of the Smart City in its different phases of development - Smart City 1.0, Smart City 2.0, Smart City 3.0, Smart City 4.0, Smart City 5.0 and Smart Sustainable City. With reference to „Transforming our world: the 2030 Agenda for Sustainable Development” containing 17 Sustainable Development Goals, the article specifically presents the assumptions and practical solutions for SDG Goal 11. A critical analysis of the literature on the subject and a content analysis of the SDG reports revealed a juxtaposition of the diverse capabilities of smart technologies and their insufficient implementation to meet the Sustainable Development Goals in Smart Cities. The reality is not encouraging. The population of people living in slums is growing rapidly, social inequalities are widening, and there is a lack of access to convenient urban transport. There is chaotic urban sprawl, air pollution and insufficient public open spaces. Solutions to urban problems are often interventionist rather than preventive. The rational and sustainable use of modern technology can change this.

With the concept of Industry 4.0 production processes are moving towards autonomy and intelligence. Technologies equipped with artificial intelligence (AI) are involved into processes that are more and more digitized. Collaborative technologies are a feature of discrete processes. The automotive industry has achieved many successes in the process innovation towards smart factories. Other plants, such as smelters or coal mining are also striving to develop smart manufacturing with integrated computer systems to support processes. A continuous production is different from a discrete or batch production. Industry 4.0 concept is focused on discrete production (with high level of automation and robotization of manufacturing) meanwhile there is a gap in implementation of these approach in the continuous production. The objective of the publication is to prepare and design the integrated computer management system based on processes realized in coal and steel manufacturing. Coal and steel production are key elements in a chain of any industrial manufacturing e.g. automotive or machinery engineering. These processes are crucial in building of smart value chain. In our paper we present the structure of processes for the continuous production. Based the processes model we proposed the next steps to build the smart manufacturing for continuous production.

The article addresses the issue of quality of life in cities based on developed methodology. A multidimensional approach was adopted, namely 28 subindices characterizing seven dimensions affecting the quality of life in 18 provincial cities in Poland. The developed methodology consisted of two stages. In the first stage, using the indicators, the values of quality-of-life indices for the studied dimensions were determined. In the second stage, using these results, the values of the total quality-of-life indices in the studied cities were measured. Then, rankings for the cities in question were created. In addition, based on the values of the dimension indices and total indices, the levels of quality of life were determined. Relationships between parameters characterizing the sizes of studied cities and their wealth and the determined quality of life were also measured. The Gray Rational Analysis method was used for ranking, and three objective analytical methods were used to determine index weights: Equal weight, Entropy and CRITIC, and the Laplace’s criterion. The results indicate that living standards in the studied cities vary widely, both in terms of the value of the total index and the indices of individual dimensions. The best living conditions were found in Warsaw (the capital of Poland), Białystok and Olsztyn, and the worst in Kielce and Szczecin.

Graphene nanoplatelets (GrNs) emerge as promising conductive fillers to significantly enhance the electrical conductivity and strength of cementitious composites, contributing to the development of highly efficient composites and the advancement of non-destructive structural health monitoring techniques. However, the complexities involved in these nanoscale cementitious composites are markedly intricate. Conventional regression models encounter limitations in fully understanding these intricate compositions. Thus, the current study employed four machine learning (ML) methods such as decision tree (DT), categorical boosting machine (CatBoost), adaptive neuro-fuzzy inference system (ANFIS), and light gradient boosting machine (LightGBM) to establish strong prediction models for compressive strength (CS) of graphene nanoplatelets-based materials. An extensive dataset containing 172 data points was gathered from published literature for model development. The majority portion (70%) of the database was utilized for training the model while 30% was used for validating the model efficacy on unseen data. Different metrics were employed to assess the performance of the established ML models. In addition, SHapley Additve explanation (SHAP) for model interpretability. The DT, CatBoost, LightGBM, and ANFIS models exhibited excellent prediction efficacy with R-values of 0.8708, 0.9999, 0.9043, and 0.8662, respectively. While all the suggested models demonstrated acceptable accuracy in predicting compressive strength, the CatBoost model exhibited exceptional prediction efficiency. Furthermore, the SHAP analysis provided that the thickness of GrN plays a pivotal role in GrNCC, significantly influencing CS and consequently exhibiting the highest SHAP value of + 9.39. The diameter of GrN, curing age, and w/c ratio are also prominent features in estimating the strength of graphene nanoplatelets-based cementitious materials. This research underscores the efficacy of ML methods in accurately forecasting the characteristics of concrete reinforced with graphene nanoplatelets, providing a swift and economical substitute for laborious experimental procedures. It is suggested that to improve the generalization of the study, more inputs with increased datasets should be considered in future studies.

Cities, in order to develop, should acquire data from various sources, properly process it, and skillfully use it for sustainable development. One such source is data from the surveys of residents’ quality of life. When processed as appropriate, the data may be a valuable source of information concerning sustainable development for the city. However, the very fact of carrying out the quality-of-life surveys does not guarantee that the information obtained from them will be used to manage the city. Based on studies entailing a sample of 29 city offices in Poland which declared regular studies of the residents’ quality of life, it was proved that the preparation and organization of such studies influences later use of such information for sustainable development purposes. Relying on the method of examining the quality of life and the number of specific indicators, the cities studied were divided into two groups. One was involved, and the other was not in the process of preparing quality-of-life surveys. A comparison was made between these groups of cities in terms of the areas included in the quality of life surveys, the decisions and actions taken based on the analysis of the results of these surveys, and the purposes for which the information is used. The areas in which the local government declares an impact on the quality of life of its residents were also compared. Attention was paid to significant differences between the two groups of cities and the effects of each of those two approaches were specified.

Penicillin binding proteins (PBPs) are involved in biosynthesis, remodeling and recycling of peptidoglycan (PG) in bacteria. PBP-A from Thermosynechococcus elongatus belongs to a cyanobacterial family of enzymes sharing close structural and phylogenetic proximity to class A β-lactamases. With the long-term aim of converting PBP-A into a β-lactamase by directed evolution, we simulated what may happen when an organism like Escherichia coli acquires such a new PBP and observed growth defect associated with the enzyme activity. To further explore the molecular origins of this harmful effect, we decided to characterize deeper the activity of PBP-A both in vitro and in vivo. We found that PBP-A is an enzyme endowed with dd-carboxypeptidase and dd-endopeptidase activities, featuring high specificity towards muropeptides amidated on the d-iso-glutamyl residue. We also show that a low promiscuous activity on non-amidated peptidoglycan deteriorates E. coli’s envelope, which is much higher under acidic conditions where substrate discrimination is mitigated. Besides expanding our knowledge of the biochemical activity of PBP-A, this work also highlights that promiscuity may depend on environmental conditions and how it may hinder rather than promote enzyme evolution in nature or in the laboratory.

The use of increasingly advanced energetic materials (EMs) in various branches of industry and military sectors increases the appropriate requirements for EMs, including: their durability, safety of use, chemical and high-energetic properties. Additionally, the impact of the products of the explosion of EMs on the natural environment is also crucial. Therefore, on-site mixture (OSM) energetic materials containing concentrated hydrogen peroxide (OSM-type energetic materials) are becoming increasingly popular. This is an extremely interesting group of materials that contains in excess of 50 wt.% hydrogen peroxide (HP) and not containing toxic compounds, and therefore is environmentally friendly. The main objective of the study was to investigate the various compositions of OSM-type energetic materials in terms of the evolution over time of their energetic properties (including the “raw” energetic material strength and the ability to sustain the propagation of a detonation wave) and the volume of the post-detonation gases. The obtained results show that the decomposition of hydrogen peroxide strongly affects the detonation parameters of OSM-type energetic material and the decomposition time of HP. In addition, it has been proven that rate of decomposition of HP significantly affects the detonation parameters of OSM-type energetic materials. It was also found that the concentration of NOx\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$_{\hbox {x}}$$\end{document} is low and decreases dramatically with the decomposition of hydrogen peroxide, but at the same time the concentration of carbon oxides increases.

This paper presents the results of a composite consisting mainly of industrial waste bound by a hydraulic binder. The composite consists of unburnt coal-mining slate, shredded rubber waste (SRW), fly ash and CEM I cement. The purpose of using the above components was to protect the unburnt coal-mining slate from the negative effects of water, which causes degradation of the aggregate grain size and significantly affects the load-bearing capacity of the aggregate. This was achieved through the use of a binder consisting of shredded waste rubber, fly ash and cement, which imparts hydrophobic properties to the composite. The composite is to be used in road pavement construction and earthworks as a substitute for standard materials. This paper focuses on testing the effects of 5, 10 and 15% additions of shredded rubber waste (SRW) on the physical and mechanical parameters of the composite, mainly compressive strength, water absorption by mass, capillary rise and deformability under cyclic loading. The composite was tested under cyclic loading conditions using a measurement system based on digital image correlation (DIC), with which the deformations occurring on the surface of the test specimens were determined. The results obtained showed the influence of shredded rubber waste additives on the decrease in compression strength (after 7 and 28 days of specimen care), mass water absorption and capillary rise, as well as an increase in the deformability of the composite under destructive loading and cyclic loading.

The article presents the results of a detailed experimental campaign including a compressive strength test, three- and four-point bending test (3PBT and 4PBT, respectively) of polymer fiber reinforced concrete with the addition of metakaolin. The comprehensive analysis included three Types of concrete mixture differing in amount and used polymer fibers. It was concluded that polymer fibers did not influence the maximum compressive and flexural tensile strength of concrete. On the other hand, they had a beneficial effect on the ductility, residual and equivalent flexural tensile strengths, and fracture energy of samples. The mixtures of Type 1 and 2 were characterized by softening behaviour but the mixture of Type 3 by soft-hardening behaviour. In the 3PBT, the residual flexural tensile strengths obtained according to EN 14651 did not correspond clearly with equivalent flexural tensile strengths calculated in compliance with RILEM TC 162-TDF. It is noteworthy that the effectiveness and correctness of equations presented in other work of the authors referring to dependencies between deflection, crack and tip mouth opening displacements for the 3PBT were confirmed on samples with different composition and fibers. Based on the 4PBT, the equivalent flexural tensile strengths according to JCI-SF4 standard were calculated and the correlations with the results from 3PBT were defined.

In the case analysed, a glass fibre mesh was applied under the asphalt layer during a rehabilitation treatment. Because only one lane was reinforced, the test section can be used to observe the influence of glass fibre mesh on the relationship between the selected deflection basin parameters (RoC, BLI, MLI, and LLI) and back-calculated pavement layer moduli. The FWD measures were used to determine the bowl of deflection indicators and to back-calculate the layer’s moduli. The values of DBP-s allowed confirmation of the technical condition of pavement construction. The first measures were carried out in 2019 and repeated in 2021; the results were then compared and analysed. Influence was observed on the relationship between the deflection basin and moduli, especially for the base course and subgrade. The reinforced lane showed a better coefficient of determination between DBPs and moduli in 2019, but in 2021 relationships were observed only for LLI and subgrade moduli. The unreinforced lane, however, showed the mentioned relationships in both 2019 and 2021. Because of a relatively small number of measurement points, the presented analyses and observations should be considered as preliminary. Presented results and relationships are another step into developing an alternative approach to determining the initial pavement moduli i.e. to use as a seed moduli.