VSB - Technical University of Ostrava

Introduction: produced by renewable resources, biodegradable polymers with their competitive mechanical properties, thermal stability and biocompatibility are important alternatives to other synthetic materials for use in medical devices, i.e. endotracheal suction catheters. However, infected catheters may lead to nosocomial infections, such as lower respiratory tract infections, with mechanical ventilation being a major risk for these. Antimicrobially coated endotracheal suction catheters may be one measure to reduce this risk. Methods: two procedures using ethanol and sodium hydroxide were tested to immobilize poly(hexamethylene biguanide) (PHMB) to polylactide-ε-caprolactone (PLA-ε-CL). The cytocompatibility of the coating was verified via the MTT assay and cytokine analysis in a cell monolayer and in a 3D mucosa model. The antimicrobial efficacy was tested using S. epidermidis; after this bacterial contamination and the adherence and viability of cells were tested. Chemical surface analysis has been performed with pristine and PHMB-coated specimens by means of infrared spectroscopy (ATR-FTIR). Results: with both applied coating procedures, PHMB could be immobilized onto the PLA-ε-CL surface. The biocompatibility of PLA-ε-CL was not impaired by the PHMB coating. IL-1α was slightly but significantly increased. Reduction of S. epidermidis was about 4 lg-levels after 6 h of incubation. Contamination of the surface prior to cell culture did not impair the adherence of the cells. Conclusion: we demonstrated that PLA-ε-CL coated with PHMB has good biocompatible properties with antimicrobial activity thus revealing the polymer to be a suitable material for the development of medical devices that are able to prevent bacterial contaminations and infections.

The sensitive nature of the data processed by the critical infrastructures of a shared platform like the internet of things (IoT) makes it vulnerable to a wide range of security risks. These infrastructures must have robust security measures to protect the privacy of the user data transmitted to the processing systems that utilize them. However, data loss and complexities are significant issues when handling enormous data in IoT applications. This paper uses a reptile search optimization algorithm to offer attuned data protection with privacy scheme (ADP2S). This study follows the reptiles’ hunting behaviours to find a vulnerability in our IoT service’s security. The system activates the reptile swarm after successfully gaining access to explode ice. An attack of protection and authentication measures explodes at the breach location. The number of swarm densities and the extent to which they explore a new area are both functions of the severity of the breach. Service response and related loss prevention time verify fitness according to the service-level fitness value. The user and the service provider contribute to the authentication, which is carried out via elliptic curve cryptography and two-factor authentication. The reptile’s exploration and exploitation stages are merged by sharing a similar search location across the initialized candidates. The proposed scheme leverages breach detection and protection recommendations by 11.37% and 8.04%, respectively. It reduces the data loss, estimation time, and complexity by 6.58%, 10.9%, and 11.21%, respectively.

Approach: Designed an outer loop controller for a series cascade scheme in the IMC framework using three approximations to integrate inverse response and dead time compensators.

PID controllers are widely favored in industry due to their simple tuning methods. The development of fractional calculus-based extensions to conventional integer-order PID controllers is recognized as a significant area for future research in controller design.

The effectiveness of cascade control in process industries is greatly influenced by how well the system is tuned. This tuning process, however, can become quite intricate when Non-Minimum Phase (NMP) zeros and dead time are present in the process transfer function.

Section 6.1 outlines the Bode ideal transfer function, which incorporates a non-minimum phase (NMP) zero.

Systems with Non-minimum phase behavior and dubious delay are universal in process industries. The non-minimum phase (NMP) characteristics contemporary a substantial task in designing controllers, as they impose constraints on the performance of the feedback system.

This chapter emphasizes the development of an enhanced parallel cascade control framework tailored for a non-minimum phase system. Within a parallel cascade configuration, disturbances and the manipulated variable can sequentially impact both loops (Uma et al. in Chem Eng Sci 65:1065–1075, 2010).

The NMP zero and dead-time systems are pervasive in process industries, significantly complicating controller design. Furthermore, when noise affects the system, it can lead to undesirable control actions, causing premature wear of actuators and undermining closed-loop performance. Using lower-order filters offers a compromise between setpoint tracking and robustness.

The inverse response occurs when initially opposing effects within a system result in an initial reaction that differs from the eventual steady-state outcome.

The utilization of additively manufactured materials has increased. Knowledge of the behaviour of this prepared material is crucial to designing safe structures and products. However, the properties are different from those of conventionally produced materials. Therefore, the focus is on widely used AISI 316L austenitic stainless steel to present its plasticity and ductile fracture, crucial in decision-making within the design process. The additively manufactured specimens were machined and also left as built, as it is not always economical to machine all the surfaces, which can even be impossible in some cases. However, it has been shown that the machining can be detrimental in some cases. First of all, the stress–strain behaviour was studied in order to simulate all the experiments. Then, several ductile fracture criteria were calibrated using these simulations and mutually compared for three studied material states—conventionally wrought (rolled), as built and machined after printing. The material prepared by the laser powder bed fusion technology exhibited higher yield strength compared to that of the wrought material. The results further show a significant difference when it comes to ductility, which is highest for wrought material and lowest for printed material that was machined. The study also provides information on the mechanisms of hardening and failure with fractography performed to support the findings for widespread austenitic stainless steel.

Concerns regarding chronic injuries (e.g., fibrosis and carcinogenesis) induced by nanoparticles raised public health concerns and need to be rapidly assessed in hazard identification. Although in silico analysis is commonly used for risk assessment of chemicals, predicting chronic in vivo nanotoxicity remains challenging due to the intricate interactions at multiple interfaces like nano-biofluids and nano-subcellular organelles. Herein, we develop a multimodal feature fusion analysis framework to predict the fibrogenic potential of metal oxide nanoparticles (MeONPs) in female mice. Treating each nano-bio interface as an independent entity, eighty-seven features derived from MeONP-lung interactions are used to develop a machine learning-based predictive framework for lung fibrosis. We identify cell damage and cytokine (IL-1β and TGF-β1) production in macrophages and epithelial cells as key events closely associated with particle size, surface charge, and lysosome interactions. Experimental validations show that the developed in silico model has 85% accuracy. Our findings demonstrate the potential usefulness of this predictive model for risk assessment of nanomaterials and in assisting regulatory decision-making. While the model is developed based on 52 MeONPs, further validation using a larger nanoparticle library is necessary to confirm its broader applicability.

Fast neutron activation analysis (FNAA) offers a new way to determine the bulk composition of Heusler alloys. Fast neutrons penetrate deep in the alloys, allowing to quantify the bulk elemental composition thanks to the characteristic gamma-rays emitted by the excited elements. In this work, a fast neutron generator (MP320) and a low-background high-efficiency well-type Ge gamma spectrometer are used to quantify the amount of Mn, Fe, Al, Si, and Sn in manufactured Heusler alloys. The alloy bulk composition is compared with the surface composition (micrometer depth range) obtained by classical energy dispersive X-ray analysis, demonstrating the interest of FNAA.

The need for high-order accurate and efficient numerical methods cannot be overemphasized. This article proposes such a method for initial value problems of ordinary differential equations by suggesting a fourth-order accurate algorithm with detailed theoretical analysis and numerical verification. First, the differential problem is converted to an integral equation. Then, numerical quadrature rule is used to transform the result to a fully discrete problem. The implicitness of the discrete problem necessitates the formulation of an explicit predictor which results to a four-step predictor-corrector method. Truncation error analysis is used to prove consistency; stability is also established with respect to perturbation in the initial data. Then, a new discrete Gronwall inequality is proposed, and used, to present a rigorous convergence analysis, establishing the fourth-order accuracy of the method. Seven numerical experiments are conducted and used to demonstrate that the method (i) is fourth-order accurate as theoretically proved, (ii) is very much more computationally efficient than the Runge-Kutta method, and (iii) is more competitive, in terms of accuracy, than the Hamming method. Therefore, the method achieves the desired objective of being very high-order accurate and efficient at the same time.

In the era of renewable energy integration, precise solar energy modeling in power systems is crucial for optimized generation planning and facilitating sustainable energy transitions. The present research proposes a comprehensive framework for assessing the operational reliability of solar integrated systems, validated using the IEEE RTS 96 test system. A robust uncertainty model has been developed to characterize variations in solar irradiance to address the uncertainties in solar panel output, followed by a multi-state modeling approach to account for the dynamic nature of solar panel output. The research introduces a time series-based ‘non-linear autoregressive neural network’ (NAR-Net) to forecast the solar irradiance levels five days ahead to optimize solar power efficiency. A comparative analysis has been conducted of three other state-of-the-art approaches, such as auto-regressive (AR), auto-regressive with moving average, and multi-layer perceptron, for predicting solar irradiance. Performance metrics, including mean square error, regression, and computational time, were evaluated to demonstrate the efficacy of the NAR-Net. The proposed prediction-based approach enhances the reliability of power generation planning by integrating modeling, which is based on forecasting. It is found that the proposed method achieves an accuracy of 98% w.r.t its counterpart. Moreover, the assessment to optimize the operational reliability of solar-integrated systems and improve generation planning for a sustainable energy future is achieved.

Improving the mechanical properties of copper and graphene composites is of a high interest. In accordance with the Hall–Petch law, the finer the grains, the higher the strength of material. Direct consolidation of fine powders is thus highly promising for preparation of (ultra)fine‐grained copper composites featuring more or less homogeneous distributions of graphene particles. This study is original as it investigates the feasibility of using the industrially applicable intensive plastic deformation method of rotary swaging for direct consolidation of copper–graphene composites featuring enhanced performance. The results show that the swaging ratio of 1.4 results in a satisfactory consolidation of the powders. However, the final consolidated piece swages with the swaging ratio of 2.8 features a relatively high microhardness of 108.2 HV0.05 and, simultaneously, the electric conductivity of 94.6% International Annealed Copper Standard (IACS). The microstructure, featuring graphene particles more or less homogeneously distributed along the grain boundaries, consists of fine grains and numerous strengthening twins, the formation of which is supported as the graphene particles aggravate the movement of dislocations along the preferential slip systems. The occurring structural phenomena (grain boundaries, twinning, texture, etc.) directly influence the mechanical (microhardness), physical (dilatation), and electric properties of the composite.

Applications for content-based image retrieval (CBIR) are found in a wide range of industries, including e-commerce, multimedia, and healthcare. CBIR is essential for organising and obtaining visual data from massive databases. Traditional techniques frequently fail to extract high-level, relevant information from images, producing retrieval results that are not ideal. This research introduces a novel Convolutional Fine-Tuned Threshold Adaboost (CFTAB) approach that integrates deep learning and machine learning techniques to enhance CBIR performance. This dataset comprises image-based data collected from multiple sources. This image data were pre-processed using Adaptive Histogram Equalization (AHE). The features of localized image data were extracted using VGG16. For an efficient CBIR process, a novel CFTAB approach was introduced. It combines both deep and machine learning (ML) methods in the proposed architecture to improve the excellence of image search. To further improve performance, CFTAB incorporates an improved AB algorithm. This algorithm adjusts the threshold levels dynamically within a robust classifier to optimize training outcomes.

Electromagnetic interference (EMI) significantly affects the performance and reliability of electronic devices. Although current metallic shielding materials are effective, they have drawbacks such as high density, limited flexibility, and poor corrosion resistance that limit their wider application in modern electronics. This study investigates the EMI shielding properties of 3D‐printed conductive structures made from polylactic acid (PLA) infused with 0D carbon black (CB) and 1D carbon nanotube (CNT) fillers. This study demonstrates that CNT/PLA composites exhibit superior EMI shielding effectiveness (SE), achieving 43 dB at 10 GHz, compared to 22 dB for CB/PLA structures. Further, conductive coating of polyaniline (PANI) electrodeposition onto the CNT/PLA structures improves the SE to 54.5 dB at 10 GHz. This strategy allows fine control of PANI loading and relevant tuning of SE. Additionally, the 3D‐printed PLA‐based composites offer several advantages, including lightweight construction and enhanced corrosion resistance, positioning them as a sustainable alternative to traditional metal‐based EMI shielding materials. These findings indicate that the SE of 3D‐printed materials can be substantially improved through low‐cost and straightforward PANI electrodeposition, enabling the production of customized EMI shielding materials with enhanced performance. This novel fabrication method offers promising potential for developing advanced shielding solutions in electronic devices.

The growing attention towards immersive technologies such as augmented reality (AR), virtual reality (VR), mixed reality (MR), extended reality (XR), and the metaverse are revolutionizing cultural heritage education and tourism. Such technologies offer immersive and interactive experiences that transform the user’s exploration of museums, cultural heritage sites, educational content, and historical landmarks. This article presents a structured framework that addresses the advancement and application of these technologies in cultural heritage education to improve user experience, learning, emotional connection, and motivation. To further explore recent trends, issues, and opportunities, the article offers a comprehensive overview of the impact of state-of-the-art immersive technology on user experience within heritage education environments . The study also outlined standard questionnaires and effective methodologies for user experience evaluations. Furthermore, the article addresses the influence of standards and guidelines recommended by standardized bodies and organizations on XR and metaverse applications. It discussed how these standards and recommendations can play a role in setting protocols that shape the development of immersive heritage education environments. Finally, we introduce an architecture model for XR and metaverse applications that can assess developers, researchers, and stakeholders to enable immersive and interactive educational experiences, bridging geographical and physical barriers. This research is intended to help academic and industry stakeholders understand the integration of digital heritage preservation tools and user experience standards critical to advancing educational engagement in cultural heritage.