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  • Corrosion damage identification based on the symmetry of propagating wavefield measured by a circular array of piezoelectric transducers: Theoretical, experimental and numerical studies
    • Beata Zima
    • Jochen Moll
    2024 Pełny tekst MECHANICAL SYSTEMS AND SIGNAL PROCESSING

    The article investigates the results obtained from numerical simulations and experimental tests concerning the propagation of guided waves in corroded steel plates. Developing innovative methodologies for assessing corrosion-induced degradation is crucial for accurately diagnosing offshore and ship structures exposed to harsh environmental conditions. The main aim of the research is to analyze how surface irregularities affect wave propagation characteristics. An investigation was conducted for antisymmetric fundamental mode A0. Specifically, the study examines the asymmetrical wavefronts generated by nonuniform thickness in damaged specimens. Initially, numerical analysis explores the impact of thickness variation on wave field symmetry. Corroded plates with varying levels of degradation are modeled using the random fields approach, with degradation levels ranging from 0 % to 60 %. Subsequently, the research investigates how the standard deviation of thickness distribution (from 5 % to 20 % of the initial thickness) and excitation frequency (from 50 to 150 kHz) influence recorded signals and the shape of reconstructed wavefronts. Each scenario compares wavefront symmetry levels estimated using rotational and bilateral symmetry degrees as indicative parameters. The numerical simulations are complemented by experimental tests conducted on plates with three different degradation levels. The results demonstrate the efficacy of the proposed wave field analysis approach for assessing structural integrity, as evidenced by the agreement between numerical predictions and experimental observations.

  • Corrosion Monitoring in Petroleum Installations—Practical Analysis of the Methods
    • Juliusz Orlikowski
    • Agata Jazdzewska
    • Iwona Łuksa
    • Michał Szociński
    • Kazimierz Darowicki
    2024 Pełny tekst Materials

    This paper presents the most typical corrosion mechanisms occurring in the petroleum industry. The methods of corrosion monitoring are described for particular corrosion mechanisms. The field and scope of the application of given corrosion-monitoring methods are provided in detail. The main advantages and disadvantages of particular methods are highlighted. Measurement difficulties and obstacles are identified and widely discussed based on actual results. Presented information will allow the corrosion personnel in refineries to extract more reliable data from corrosion-monitoring systems.

  • Corrosion of AISI1018 and AISI304 steel exposed to sulfates
    • Ginneth Millan Ramirez
    • Miguel Angel Baltazar-Zamora
    • Ce Tochtli Méndez Ramírez
    • Maciej Niedostatkiewicz
    • Hubert Byliński
    2024 Inżynieria Bezpieczeństwa Obiektów Antropogenicznych

    This research analyses the behavior of corrosion, durability, and quality of reinforced concrete samples coated with two different materials when exposed to contaminated soil with sulfates. The initial assessment involved evaluating the water absorption rate of the coating materials before and after exposure to a solution containing 3%푁푎2푆푂4+3%푀푔푆푂4+3%퐾2푆푂4+3%퐶푎푆푂4to determine their durability. the corrosion potential and linear polarization resistance technique were employed to measure the corrosion rate. Carbon steel and AISI 304 steel bars were tested alongside a stainless counter electrode. The findings indicate that the solvent-based coating exhibited superior performance, demonstrating reduced corrosion and water absorption rates. Additionally, the presence of sulfates led to the formation of a surface layer on the concrete, initially aiding in limiting waterpenetration. However, over time, this layer eventually causes damage to the concrete from the inside out.

  • Corrosion performance of super duplex stainless steel and pipeline steel dissimilar welded joints: a comprehensive investigation for marine structures
    • Anup Kumar Maurya
    • Shailesh M. Pandey
    • Rahul Chhibber
    • Dariusz Fydrych
    • Chandan Pandey
    2024 INTERNATIONAL JOURNAL OF ADVANCED MANUFACTURING TECHNOLOGY

    This study investigates the corrosion behavior of dissimilar gas tungsten arc (GTA) welded joints between super duplex stainless steel (sDSS 2507) and pipeline steel (X-70) using electrochemical and immersion corrosion tests. The GTA welds were fabricated using ER2594 and ER309L fller metals. The study examined the electrochemical characteristics and continuous corrosion behavior of samples extracted from various zones of the weldments in a 3.5 wt.% NaCl solution, employing electrochemical impedance spectroscopy, potentiodynamic polarization methods, and an immersion corrosion test. EIS and immersion investigations revealed pitting corrosion in the X-70 base metal/X-70 heat-afected zone, indicating inferior overall corrosion resistance due to galvanic coupling. The corrosion byproducts identifed in complete immersion comprised α-FeOOH, γ-FeOOH, Fe3O4, and Fe2O3, whereas γ-FeOOH and Fe3O4 were predominant in dry/wet cyclic conditions. Corrosion escalated with dry/wet cycle conditions while maintaining a lower level in complete immersion. The corrosion mechanism involves three wet surface stages in dry/wet cycles and typical oxygen absorption during complete immersion. Proposed corrosion models highlight the infuence of Cl−, O2, and rust layers.

  • Cost-effective methods of fabricating thin rare-earth element layers on SOC interconnects based on low-chromium ferritic stainless steel and exposed to air, humidified air or humidified hydrogen atmospheres
    • Łukasz Mazur
    • Paweł Winiarski
    • Bartosz Kamecki
    • Justyna Ignaczak
    • Sebastian Molin
    • Tomasz Brylewski
    2024 INTERNATIONAL JOURNAL OF HYDROGEN ENERGY

    Most oxidation studies involving interconnects are conducted in air under isothermal conditions, but during real-life solid oxide cell (SOC) operation, cells are also exposed a mixture of hydrogen and water vapor. For this study, an Fe–16Cr low-chromium ferritic stainless steel was coated with different reactive element oxides – Gd2O3, CeO2, Ce0.9Y0.1O2 – using an array of methods: dip coating, electrodeposition and spray pyrolysis. The samples underwent oxidation experiments carried out over 100 h in three different atmospheres at 800 °C: air, an air/H2O mixture, and an Ar/H2/H2O mixture. The influence of different atmospheres on the corrosion of the Fe–16Cr steel was determined via oxidation kinetics studies; the corrosion product was evaluated using X-ray diffraction, scanning electron microscopy and area-specific resistance (ASR) measurements. All coated samples exhibited lower parabolic oxidation rate constants than bare steel and most also had lower ASR. The applied modifications were found to be sufficiently effective to allow the investigated low-chromium steel to be considered for application as an interconnect material for SOCs.

  • Cost-Effective Piggyback Forward dc-dc Converter
    • Oleksandr Matiushkin
    • Oleksandr Husev
    • Hossein Afshari
    • Dmitri Vinnikov
    • Ryszard Strzelecki
    2024

    The novel piggyback dc-dc converter as a cost-effective solution is presented in this work. It provides a wide input voltage range of regulation with a low component count. The novel solution is an advanced forward dc-dc converter with an additional clamp output capacitor. The idea of such a type of converter is to transfer magnetizing energy of transformer to the output side, instead of using input clamp circuit. The design guidelines of the passive component of the proposed solution are discussed. A digital domain proportional integral controller is designed for the off-grid system validation, and it provides a stable output voltage in a wide range of the input voltage and power. Experimental prototype of the proposed piggyback converter along with experimental results of critical points are presented. The efficiency study of the proposed solution is done.

  • Cost-Efficient Globalized Parameter Optimization of Microwave Components through Response-Feature Surrogates and Nature-Inspired Metaheuristics
    • Anna Pietrenko-Dąbrowska
    • Sławomir Kozieł
    • Łukasz Gołuński
    2024 Pełny tekst IEEE Access

    Design of contemporary microwave devices predominantly utilizes computational models, including both circuit simulators, and full-wave electromagnetic (EM) evaluation. The latter constitutes the sole generic way of rendering accurate assessment of the system outputs that considers phenomena such as cross-coupling or radiation and dielectric losses. Consequently, for reliability reasons, the final tuning of microwave device parameters is commonly performed utilizing EM simulation software. As EM analysis is computationally heavy, parametric optimization entails significant costs, also for local algorithms. The expenses generated by global search procedures are incomparably higher, and often prohibitive. Still, global optimization is more and more often necessary, for example, when re-designing a structure over extended ranges of operating conditions (bandwidth, power split ratios, etc.), when more than a single local optimum exists (e.g., optimization of frequency selective surfaces), or simply due to the absence of quality initial design (e.g., compact circuits obtained using the slow-wave phenomenon). A possible workaround is surrogate-assisted optimization, yet a construction of accurate replacement models is a challenge by itself. This paper offers an innovative approach to a rapid globalized optimization of passive microwave components. It combines a machine learning procedure, specifically, an iterative construction and refinement of fast surrogates (with infill criterion being a minimization of the predictor-yielded objective improvement) with a response feature technology, where the metamodel targets suitably appointed characteristic points of the circuit outputs. These so-called response features are in a nearly linear relationship with the geometry parameters, which facilitates the search process and reduces the expenditures associated with surrogate model construction. Identification of the infill points is executed using a particle swarm optimization algorithm. Numerical experiments carried out using two microstrip circuits demonstrate the capability for a global search of the proposed algorithm, and its superior performance over direct nature-inspired-based optimization and surrogate-assisted search at the level of complete circuit characteristics.

  • Cost-Efficient Measurement Platform and Machine-Learning-Based Sensor Calibration for Precise NO2 Pollution Monitoring
    • Anna Pietrenko-Dąbrowska
    • Sławomir Kozieł
    • Marek Wójcikowski
    • Bogdan Pankiewicz
    • Artur Rydosz
    • Tuan-Vu Cao
    • Krystian Wojtkiewicz
    2024 MEASUREMENT

    Air quality significantly impacts human health, the environment, and the economy. Precise real-time monitoring of air pollution is crucial for managing associated risks and developing appropriate short- and long-term measures. Nitrogen dioxide (NO2) stands as a common pollutant, with elevated levels posing risks to the human respiratory tract, exacerbating respiratory infections and asthma, and potentially leading to chronic lung diseases. Notwithstanding, precise NO2 detection typically demands complex and costly equipment. This paper explores NO2 monitoring using low-cost platforms, meticulously calibrated for reliability. An integrated measurement unit is first presented that contains primary and supplementary nitrogen dioxide sensors, as well as auxiliary detectors for evaluating outside and inside temperature and humidity. The calibration process utilizes data acquired over the period of five months from various reference stations. Employing machine learning with an artificial neural network (ANN)-based and kriging interpolation surrogate models, the correction strategy integrates additive and multiplicative enhancement, predicted by the ANN through auxiliary sensor data such as temperature, humidity, and the sensor-detected NO2 levels. Extensive verification studies showcase that this calibration approach notably enhances monitoring precision (r2 correlation coefficient surpassing 0.85 concerning reference data, and RMSE of less than four g/m3), rendering low-cost NO2 detection practical and dependable.

  • Cost-Efficient Multi-Objective Design of Miniaturized Microwave Circuits Using Machine Learning and Artificial Neural Network
    • Sławomir Kozieł
    • Anna Pietrenko-Dąbrowska
    • Leifur Leifsson
    2024

    Designing microwave components involves managing multiple objectives such as center frequencies, impedance matching, and size reduction for miniaturized structures. Traditional multi-objective optimization (MO) approaches heavily rely on computationally expensive population-based methods, especially when exe-cuted with full-wave electromagnetic (EM) analysis to guarantee reliability. This paper introduces a novel and cost-effective MO technique for microwave passive components utilizing a machine learning (ML) framework with artificial neural network (ANN) surrogates as the primary prediction tool. In this approach, mul-tiple candidate solutions are extracted from the Pareto set via optimization using a multi-objective evolutionary algorithm (MOEA) applied to the current ANN model. These solutions expand the dataset of available (EM-simulated) parameter vectors and refine the surrogate model iteratively. To enhance computational effi-ciency, we employ variable-resolution EM models. Tested on two microstrip cir-cuits, our methodology competes effectively against several surrogate-based ap-proaches. The average computational cost of the algorithm is below three hundred EM analyses of the circuit, with the quality of generated Pareto sets surpassing those produced by the benchmark methods.

  • Coupled DEM/CFD analysis of impact of free water on the static and dynamic response of concrete in tension regime
    • Marek Krzaczek
    • Andrzej Tejchman-Konarzewski
    • Michał Nitka
    2024 Pełny tekst COMPUTERS AND GEOTECHNICS

    W tym artykule zbadano numerycznie quasi-statyczne i dynamiczne zachowanie częściowo nasyconego płynem betonu w warunkach dwuwymiarowego (2D) jednoosiowego rozciągania w mezoskali. Obliczono, jaki wpływ ma zawartość wolnego płynu porowego (gazu i wody) na proces pękania i wytrzymałość betonu w rozciąganiu. Do symulacji zachowania betonu całkowicie i częściowo nasyconego płynem w warunkach quasi-statycznych i dynamicznych wykorzystano ulepszony model hydromechaniczny w skali porów, oparty na DEM/CFD. Podstawą koncepcji przepływu płynu była sieć kanałów na ciągłym obszarze pomiędzy dyskretnymi elementami. W bardzo porowatym, częściowo nasyconym betonie przyjęto dwufazowy laminarny przepływ płynu. Aby śledzić zawartość cieczy/gazu, wzięto pod uwagę położenie i objętość porów i rys. Symulacje numeryczne spójnych próbek ziarnistych o uproszczonej mezostrukturze sferycznej przypominającej beton przeprowadzono w warunkach suchych i mokrych dla dwóch różnych szybkości odkształcenia. Przeprowadzono badania wpływu ciśnienia porów płynu, nasycenia płynu i lepkości płynu na wytrzymałość i proces pękania betonu. Kwasi-statyczna wytrzymałość na rozciąganie spadała nieliniowo wraz ze wzrostem nasycenia płynu i lepkości płynu podczas migracji płynu przez pory i pęknięcia wskutek przyspieszenia procesu pękania. Jednakże podczas szybkiego dynamicznego odkształcenia przy rozciąganiu proces pękania został osłabiony z powodu ograniczenia migracji płynu wynikającego z niewystarczającego czasu płynu na opuszczenie porów. Spowodowało to nieliniowy wzrost dynamicznej wytrzymałości na rozciąganie wraz ze wzrostem nasycenia płynu i lepkości płynu.

  • Crack monitoring in concrete beams under bending using ultrasonic waves and coda wave interferometry: the effect of excitation frequency on coda
    • Magdalena Knak
    • Erwin Wojtczak
    • Magdalena Rucka
    2024 Pełny tekst

    Concrete is one of the most widely used construction materials in the world. In recent years, various non-destructive testing (NDT) and structural health monitoring (SHM) techniques have been investigated to improve the safety and control of the current condition of concrete structures. This study focuses on micro-crack monitoring in concrete beams. The experimental analysis was carried out on concrete elements subjected to three-point bending in a testing machine under monotonic quasi-static loading. During the tests, the fracture process was characterized using ultrasonic waves. The recorded signals were further processed by coda wave interferometry (CWI). This technique allowed the detection of cracks using the decorrelation between ultrasonic wave signals collected at different stages of degradation. Different values of excitation frequencies in the range from 100 kHz to 400 kHz were used to investigate the influence of frequency selection on the effectiveness of the damage indication based on the decorrelation of coda waves. The results obtained from the experiments were intended to highlight the effect of the applied frequencies on the coda wave interferometry.

  • Crank–Nicolson FDTD Method in Media Described by Time-Fractional Constitutive Relations
    • Damian Trofimowicz
    • Tomasz Stefański
    • Jacek Gulgowski
    2024

    In this contribution, we present the Crank-Nicolson finite-difference time-domain (CN-FDTD) method, implemented for simulations of wave propagation in media described by time-fractional (TF) constitutive relations. That is, the considered constitutive relations involve fractional-order (FO) derivatives based on the Grünwald-Letnikov definition, allowing for description of hereditary properties and memory effects of media and processes. Therefore, the TF constitutive relations make it possible to include, in a dielectric response, diffusion processes which are modelled mathematically by the diffusion-wave equation. We formulate fundamental equations of the proposed CN-FDTD method, and then we execute simulations which confirm its accuracy and applicability. Additionally, we perform numerical tests of stability, which confirm unconditional stability of the method. The proposed method is useful for researchers investigating numerical techniques in media described by FO derivatives.

  • Creating private and public value in data-related management projects: a cross-border case study from Switzerland and Italy
    • Elide Garbani-Nerini
    • Elena Marchiori
    • Nadzeya Sabatini
    • Lorenzo Cantoni
    2024 Pełny tekst

    The literature in the field of smart cities shows a continuous emphasis and interest in the topic of big data due to the extensive use of Information and Communication Technologies by public and private institutions within each city. There is undoubtedly value in big data: in data lie insights on the city, its stakeholders, citizens, products, and services. Challenges, though, lie in data’s variety, volume, and velocity, but also in managing them, considering the complex interplay between stakeholders inside a city or a country. Another layer of complexity is added when we consider a smart city as a smart destination where the visitor - often an international tourist - becomes an additional stakeholder of a smart city bringing in additional data. Such challenges, though, are even stronger when tourists do not stop at geographical borders: smart destinations become cross-border destinations. While there is a physical border between them, but most importantly, a legal difference in how data should be collected, stored, managed, and re-used [56, 59], data flows do not stop at this border. This complexity has to be managed both by governmental and tourism agencies. However, the literature between eGovernment and tourism is often theoretical in nature, and while it highlights the potential benefits of smart destinations and data-management processes, it does not provide detailed guidelines on how to implement these concepts in practice [41], especially in the context of cross-border smart destinations. With regards to this, not only has the need for guidelines risen to help tourism destinations tackle smart data- and technology-related projects, but also to define how stakeholders can come together to determine data policies and governance in order to create private as well as public value [60]. This paper responds to such a need by presenting the results of a cross-border research project conducted in Switzerland and Italy, where the model of a smart destination’s structure proposed by Ivars-Baidal et al. [35] has been applied, and its dimensions have been operationalized in a data-related management project. This allowed the authors to understand how to create public and private value managing data flows in a cross-border context, while also elaborating on the model reflecting on data’s dual role as a starting point but also as a central component impacting other dimensions.

  • Creep rupture study of dissimilar welded joints of P92 and 304L steels
    • Gaurav Dak
    • Krishna Guguloth
    • R. S. Vidyarthy
    • Dariusz Fydrych
    • Chandan Pandey
    2024 Welding in the World

    The present work investigates the high-temperature tensile and creep properties of the dissimilar metal weld joints of 304L austenitic stainless steel (SS) and P92 creep strength-enhanced ferritic-martensitic (CSEF/M) steel under diferent testing condition. Thermanit MTS 616 fller rod (P92 fller) and the multi-pass tungsten inert gas (TIG) welding process were used to create the dissimilar weld connection. The ultimate tensile strength (UTS) was evaluated in the temperature range of 450–850 °C. Creep testing was conducted at a temperature of 650 °C while applying stress levels of 130 MPa, 150 MPa, 180 MPa, and 200 MPa. The shortest creep life (2.53 h) was recorded for the specimen tested at 650 °C and subjected to 200 MPa, whereas the longest creep life (~242 h) was observed for the specimen tested at 650 °C with a stress of 130 MPa. The linear regression model was developed using an applied stress (σ) v/s rupture time (tR) plot at 650 °C. The applied stress and rupture time followed the logarithmic equation: log(tR)=22.57566+(-9.55294) log (σ). The detailed microstructural characterization and micro-hardness distribution across the fractured specimens was carried out. The fndings demonstrated that the service life span of this weld joint at high temperature and stress conditions is infuenced by the undesired microstructural changes at elevated temperature, such as coarsening of the precipitates, development of the Laves phase, softening of the matrix, and strain-ageing phenomenon.

  • Cryptocurrencies as a Speculative Asset: How Much Uncertainty is Included in Cryptocurrency Price?
    • Tayyaba Ahsan
    • Krystian Zawadzki
    • Khan Mubashir
    2024 Pełny tekst SAGE Open

    The aim of this paper is to examine the relationship between uncertainty indices (Geopolitical Uncertainty Index and Global Economic Policy Uncertainty Index) and cryptocurrencies. This study evaluated the behavior of cryptocurrencies with the evolution of uncertainties (GPU, EPU) on returns and volatility in terms of safe heaven as in traditional specualtive assets it increases their volaitility and reduces risk. For this purpose, this study examines the relationship between uncertanities indices, gold returns and crptocurrency by using the OLS regression for the monthly data from April 2017 to April 2022. The findings of this study indicate that the return and volatility of cryptocurrency increases. In particular, we note that the cryptocurrency market could serve as a weak hedge and safe against GEPU during a bull market; It could be considered a strong hedge, but in most cases could not serve as a safety against GPR. However, in case of Gold it is found that it serves as weak hedge against uncertainity indices and is not considered as safe heaven against GEPU and GPR. This study expands the current research on uncertainity indices and provides unique insight about the speculative nature of cryptocurrencies and safe heaven.

  • CuMn1.7Fe0.3O4 – RE2O3 (RE=Y, Gd) bilayers as protective interconnect coatings for Solid Oxide Cells
    • Bartłomiej Lemieszek
    • Justyna Ignaczak
    • Krystian Lankauf
    • Patryk Błaszczak
    • Maciej Bik
    • Marcin Zajac
    • Maciej Sitarz
    • Piotr Jasiński
    • Sebastian Molin
    2024 Pełny tekst JOURNAL OF THE EUROPEAN CERAMIC SOCIETY

    Efficient replacement of materials based on critical elements such as cobalt is one of the greatest challenges facing the field of solid oxide cells. New generation materials, free of cobalt show potential to replace conventional materials. However, these materials are characterized by poor ability to block chromium diffusion. This article described the study of CuMn1.7Fe0.3O4 (CMFO) spinel combined with single metal oxide (Y2O3 or Gd2O3) thin films as protective coatings for steel interconnects. CMFO was examined using XRD and TPR. Coated steel samples were oxidized in an air atmosphere at 700 °C for 4000 h. The coatings and oxide scale microstructures and cross-sections were examined by CRI, XRD, and SEM-EDX. The electrical properties of the steel-coating system were evaluated using Area Specific Resistance measurements. Based on the results obtained, it can be concluded that the use of thin layers of rare earth oxides allowed for better blocking of chromium diffusion.

  • Current advances in membrane processing of wines: A comprehensive review
    • Youssef El Rayess
    • Roberto Castro Munoz
    • Alfredo Cassano
    2024 Pełny tekst TRENDS IN FOOD SCIENCE & TECHNOLOGY

    Background Membrane-based operations, especially pressure-driven membrane operations, are today well-established procedures for various applications in the wine industry thanks to their intrinsic properties and undoubted advantages over traditional methods. Emerging membrane processes, such as pervaporation, electrodialysis and osmotic distillation, forward osmosis, membrane contactors, offer new and interesting perspectives to improve quality and develop new products without compromising organoleptic properties. Scope and approach This review provides a comprehensive overview on the use of membrane operations in wine processing. A bibliometric and scientometric analysis has been done to provide the current advances dealing with the application of these operations in different steps of wine manufacture, including clarification, stabilization, concentration, acidification, deacidification and dealcoholization. The current challenges and perspectives are highlighted to guide further advancements of membrane technology in this field. Key findings and conclusions The use of conventional and emerging membrane systems offers interesting opportunities to improve and optimize current practices of the wine processing industry. Considerable progress has been done concerning the development of low-fouling materials, identification of wine molecules responsible for membrane fouling and methods to mitigate such phenomenon in the clarification of wines by microfiltration membranes. Technological progress in electrodialysis makes this process a very attractive method for tartrate stabilization, acidification and deacidification of wines. Different conventional and emerging membrane processes offer valid post-fermentation strategies to remove ethanol in wines while preserving their original characteristics. The global results provide interesting perspectives for a wider implementation of membrane processes in the winemaking industry and to redesign the traditional vinification process under the process intensification strategy.

  • Cybersecurity Assessment Methods—Why Aren’t They Used?
    • Rafał Leszczyna
    2024 IT Professional

    A recent survey of cybersecurity assessment methods proposed in academic and research environments revealed that their adoption in operational settings was extremely scarce. At the same time, the frameworks developed by industrial communities have been met with broad reception. The question arises of what contributed to the success of the methods. To answer it, three-part research that employed evaluation criteria, qualitative metrics, and continuity of support assessment was conducted. Among other findings, it shows that the continuity of support plays an important role in the adoption of a method. This, in turn, is connected to a sound funding model and a well-developed and active community of supporters.

  • Cyclic behaviour modelling of additively manufactured Ti-6Al-4V lattice structures
    • Michał Doroszko
    • Andrzej Seweryn
    2024 INTERNATIONAL JOURNAL OF MECHANICAL SCIENCES

    The present work is concerned with the numerical modelling of the cyclic behaviour of Ti-6Al-4V lattice structures. In the study, diamond structures of titanium alloy produced by the additive laser powder bed fusion (LPBF) method with different degrees of relative density were used. Realistic geometric models of the studied mesostructures were generated using computed microtomography, taking into account the imperfections of the material resulting from the manufacturing process. The numerical calculations also took into account the actual material hardening curve in the elastic-plastic strain range. One of the achievements of this work is the numerical modelling of cyclic loading of realistic mesostructures with their imperfections. The areas of the mesostructures most susceptible to fatigue cracking have been identified and analysed. True hysteresis loops and values of local stress and strain amplitude were determined at the locations of highest stress concentration in cyclically loaded diamond structures. The main achievement of the present work was the modelling of the macroscopic fatigue life of the investigated mesostructures based on the true values of stress and strain at the locations most exposed to fatigue cracking. For this purpose, a stress criterion for fatigue cracking of Ti-6Al-4V lattice structures fabricated by the additive LPBF method was proposed.

  • Cyclic deformation and fracture behaviour of additive manufactured maraging steel under variable-amplitude loading
    • Zbigniew Marciniak
    • Ricardo Branco
    • Wojciech Macek
    • Michał Dobrzyński
    • C. Malça
    2024 THEORETICAL AND APPLIED FRACTURE MECHANICS

    The cyclic deformation and fracture behaviour of 18Ni300 maraging steel produced by laser beam powder bed fusion is studied under variable-amplitude loading. The tests were conducted under fully-reversed strain-controlled conditions with a loading sequence comprising three ascending cycles and three descending cycles repeated sequentially until failure. After the tests, fracture surfaces were examined using height and volume surface topography parameters to characterise the fractographic features. Fracture surfaces were also analysed through scanning electron microscopy to identify the main failure modes. Fatigue life was predicted by using the Smith-Watson-Topper and the Basquin-Coffin-Manson models with the Palmgren-Miner damage rule. The former approach was more accurate leading to mean errors close to zero. The values of the kurtosis parameter obtained from both sides of the fracture surfaces correlated well with the fatigue life. SEM analysis showed a mixed ductile-brittle mode of fracture with a predominance of brittle fracture. Crack initiation occurred from manufacturing defects located at the surface or near-surface.

  • Cytocompatibility, antibacterial, and corrosion properties of chitosan/polymethacrylates and chitosan/poly(4‐vinylpyridine) smart coatings, electrophoretically deposited on nanosilver‐decorated titania nanotubes
    • Łukasz Pawłowski
    • Michał Bartmański
    • Anna Ronowska
    • Adrianna Banach-Kopeć
    • Szymon Mania
    • Bartłomiej Cieślik
    • Aleksandra Mielewczyk-Gryń
    • Jakub Karczewski
    • Andrzej Zieliński
    2024 JOURNAL OF BIOMEDICAL MATERIALS RESEARCH PART B-APPLIED BIOMATERIALS

    The development of novel implants subjected to surface modification to achieve high osteointegration properties at simultaneous antimicrobial activity is a highly current problem. This study involved different surface treatments of titanium surface, mainly by electrochemical oxidation to produce a nanotubular oxide layer (TNTs), a subsequent electrochemical reduction of silver nitrate and decoration of a nanotubular surface with silver nanoparticles (AgNPs), and finally electrophoretic deposition (EPD) of a composite of chitosan (CS) and either polymethacrylate-based copolymer Eudragit E 100 (EE100) or poly(4-vinylpyridine) (P4VP) coating. The effects of each stage of this multi-step modification were examined in terms of morphology, roughness, wettability, corrosion resistance, coating-substrate adhesion, antibacterial properties, and osteoblast cell adhesion and proliferation. The results showed that the titanium surface formed nanotubes (inner diameter of 97 ± 12 nm, length of 342 ± 36 nm) subsequently covered with silver nanoparticles (with a diameter of 88 ± 8 nm). Further, the silver-decorated nanotubes were tightly coated with biopolymer films. Most of the applied modifications increased both the roughness and the surface contact angle of the samples. The deposition of biopolymer coatings resulted in reduced burst release of silver. The coated samples revealed potent antimicrobial activity against both Gram-positive and Gram-negative bacteria. Total elimination (99.9%) of E. coli was recorded for a sample with CS/P4VP coating. Cytotoxicity results using hFOB 1.19, a human osteoblast cell line, showed that after 3 days the tested modifications did not affect the cellular growth according to the titanium control. The proposed innovative multilayer antibacterial coatings can be successful for titanium implants as effective postoperative anti-inflammation protection.

  • Damage detection in 3D printed plates using ultrasonic wave propagation supported with weighted root mean square calculation and wavefield curvature imaging
    • Erwin Wojtczak
    • Magdalena Rucka
    • Angela Andrzejewska
    2024 Pełny tekst

    3D printing (additive manufacturing, AM) is a promising approach to producing light and strong structures with many successful applications, e.g., in dentistry and orthopaedics. Many types of filaments differing in mechanical properties can be used to produce 3D printed structures, including polymers, metals or ceramics. Due to the simplicity of the manufacturing process, biodegradable polymers are widely used, e.g., polylactide (polylactide – PLA) with a practical application for manufacturing complex-shaped elements. The current work dealt with the application of ultrasonic guided waves for non-destructive damage detection and imaging in AM plates. Two specimens with defects were manufactured from PLA filament. Different sizes of damage areas were considered. The specimens were tested using the guided wave propagation technique. The waves were excited using a PZT actuator and recorded contactless with the scanning laser Doppler vibrometry (SLDV) in a set of points located at one surface of the sample. The collected signals were processed with two methods. The first was the weighted root mean square (WRMS) algorithm. Different values of the calculation parameters, namely, averaging time and weighting factor were considered. The WRMS damage maps for both samples were prepared to differentiate between intact and damaged areas. The second approach was wavefield curvature imaging (WCI) which allowed the determination of damage maps based on the curvature of the wavefront. The compensation of wave signals was performed to enhance the quality of results. It was observed that the size of the defect strongly influenced the efficiency of imaging with both methods. The limitations of the proposed approaches were characterized. The presented results confirmed that guided waves are promising for non-destructive damage imaging in AM elements.

  • Damage of a post-tensioned concrete bridge – Unwanted cracks of the girders
    • Bartosz Sobczyk
    • Łukasz Pyrzowski
    • Mikołaj Miśkiewicz
    2024 ENGINEERING FAILURE ANALYSIS

    The cracking of a post-tensioned T-beam superstructure, which was built using the incremental launching method, is analyzed in the paper. The problem is studied in detail, as specific damage was observed in the form of longitudinal cracks, especially in the mid-height zone of the girder at the interface of two assembly sections. The paper is a case study. A detailed inspection is done and non-destructive testing results of the girders are briefly discussed. The attention is especially focused on advanced and comprehensive numerical simulations of the bridge mechanical behavior. Linear and nonlinear static calculations are performed employing the Finite Element Method at global and local levels of precision, enabling deep insight into the bridge response during all the stages of bridge construction and after it is opened to traffic. The crack propagation process in local analyses is described by the application of the Concrete Damage Plasticity law, the parameters of which were carefully chosen. The predicted damage patterns closely resemble those observed at the site. The results reveal, that the girder damage process was initiated when centric prestressing was applied, because vertical reinforcement of the assembly section front-end surface was not designed. However, the damage did not compromise the safety of the bridge. Finally, the repair methods employed are described and also a discussion is presented on how to prevent the occurrence of such cracking.

  • Data Domain Adaptation in Federated Learning in the Breast Mammography Image Classification Problem
    • Łukasz Erimus
    • Aleksandra Borowska
    • Adrian Jaromin
    • Agnieszka Lewko
    • Jacek Rumiński
    2024

    We are increasingly striving to introduce modern artificial intelligence techniques in medicine and elevate medical care, catering to both patients and specialists. An essential aspect that warrants concurrent development is the protection of personal data, especially with technology's advancement, along with addressing data disparities to ensure model efficacy. This study assesses various domain adaptation techniques and federated learning to determine optimal integration strategies for enhanced security and the challenges posed by diverse datasets. Experiments utilized deep learning models, three domain adaptation methods, and a federated learning framework, focusing on mammography imaging for breast cancer detection. Results indicate a notable improvement of up to 20% with domain adaptation and an additional 10% with federated learning integration.

  • Data fusion of sparse, heterogeneous, and mobile sensor devices using adaptive distance attention
    • Jean-Marie Lepioufle
    • Philipp Schneider
    • Paul David Hamer
    • Rune Odegard
    • Islen Vallejo
    • Tuan-Vu Cao
    • Amir Taherkordi
    • Marek Wójcikowski
    2024 Environmental Data Science

    In environmental science, where information from sensor devices are sparse, data fusion for mapping purposes is often based on geostatistical approaches. We propose a methodology called adaptive distance attention that enables us to fuse sparse, heterogeneous, and mobile sensor devices and predict values at locations with no previous measurement. The approach allows for automatically weighting the measurements according to a priori quality information about the sensor device without using complex and resource-demanding data assimilation techniques. Both ordinary kriging and the general regression neural network (GRNN) are integrated into this attention with their learnable parameters based on deep learning architectures. We evaluate this method using three static phenomena with different complexities: a case related to a simplistic phenomenon, topography over an area of 196 km2 and to the annual hourly NO2 concentration in 2019 over the Oslo metropolitan region (1026 km2 ). We simulate networks of 100 synthetic sensor devices with six characteristics related to measurement quality and measurement spatial resolution. Generally, outcomes are promising: we significantly improve the metrics from baseline geostatistical models. Besides, distance attention using the Nadaraya–Watson kernel provides as good metrics as the attention based on the kriging system enabling the possibility to alleviate the processing cost for fusion of sparse data. The encouraging results motivate us in keeping adapting distance attention to space-time phenomena evolving in complex and isolated areas.

  • Data on LEGO sets release dates and worldwide retail prices combined with aftermarket transaction prices in Poland between June 2018 and June 2023
    • Wiktor Oczkoś
    • Bartosz Podgórski
    • Wiktoria Szczepańska
    • Tomasz Maria Boiński
    2024 Pełny tekst Data in Brief

    The dataset contains LEGO bricks sets item count and pricing history for AI-based set pricing prediction. The data spans the timeframe from June 2018 to June 2023. The data was obtained from three sources: Brickset.com (LEGO sets retail prices, release dates, and IDs), Lego.com official web page (ID number of each set that was released by Lego, its retail prices, the current status of the set) and promoklocki.pl web page (the retail prices for Poland, prices from aftermarket transactions). The data was merged based on the official LEGO set ID. With high granularity of the data (averaged monthly prices per LEGO set) the dataset permits the computation of variables at the set level and could support both aggregate and time-series analyses whereas the sparseness of the data permits the analysis of collector behavior allowing pinpointing of expected qualities from the purchased products and their resale potential. This may be useful to a broad range of researchers and data scientists using statistical methods and machine-learning techniques for price prediction.

  • Data-Driven Modeling of Mechanical Properties of Fiber-Reinforced Concrete: A Critical Review
    • Farzin Kazemi
    • Torkan Shafighfard
    • Doo-Yeol Yoo
    2024 ARCHIVES OF COMPUTATIONAL METHODS IN ENGINEERING

    Fiber-reinforced concrete (FRC) is extensively used in diverse structural engineering applications, and its mechanical properties are crucial for designing and evaluating its performance. The compressive, flexural, splitting tensile, and shear strengths of FRCs are among the most important attributes, which have been discussed more extensively than other properties. The accurate prediction of these properties, which are required for design criteria, has been a challenge because of their high uncertainties. Statistical and empirical models have been extensively utilized. However, such models require extensive experimental work and can produce incorrect outcomes when there are complicated interactions among the qualities of concrete, the makeup of the blend, and the curing environment. To address this issue, machine learning (ML) methods have been increasingly applied in recent years to solve complex structural engineering problems. Predictive models can provide a strong solution for time-consuming numerical simulations and expensive experiments. This study explores the ML techniques applied in this context and provides a comprehensive analysis of artificial intelligence methods used for predicting the mechanical properties of FRCs. It also highlights the key observations, challenges, and future trends in this field. This study serves as a valuable resource for researchers in selecting accurate models that match their applications. It also encourages material engineers to become familiar with and employ ML methods to design FRC mixtures appropriately.

  • Data-driven Models for Predicting Compressive Strength of 3D-printed Fiber-Reinforced Concrete using Interpretable Machine Learning Algorithms
    • Muhammad Arif
    • Faizullah Jan
    • Aïssa Rezzoug
    • Muhammad Ali Afridi
    • Muhammad Luqman
    • Waseem Akhtar Khan
    • Marcin Kujawa
    • Hisham Alabduljabbar
    • Majid Khan
    2024 Case Studies in Construction Materials

    3D printing technology is growing swiftly in the construction sector due to its numerous benefits, such as intricate designs, quicker construction, waste reduction, environmental friendliness, cost savings, and enhanced safety. Nevertheless, optimizing the concrete mix for 3D printing is a challenging task due to the numerous factors involved, requiring extensive experimentation. Therefore, this study used three machine learning techniques, including Gene Expression Programming (GEP), Multi-Expression Programming (MEP), and Decision Tree (DT), to forecast the compressive strength of 3D printed fiber-reinforced concrete (3DP-FRC). The dataset comprises 299 data points with sixteen variables gathered from experimental research studies. For training the model, 70% of the dataset was used, while the remaining 30% was reserved for model testing. Several statistical metrics were utilized to evaluate the accuracy and applicability of the models. In addition, SHapley Additive exPlanations (SHAP), partial dependence plots, and individual conditional expectations approach were employed for the interpretability of the models. The proposed GEP, MEP, and DT models indicated enhanced efficacy, exhibiting correlation coefficient (R) scores of 0.996, 0.987, and 0.990, with mean absolute errors (MAE) of 1.029, 4.832, and 2.513, respectively. Overall, the established GEP model demonstrated exceptional performance compared to MEP and DT, showcasing high prediction precision in assessing the strength of 3DP-FRC. Moreover, a simple empirical formulation has been devised using GEP to predict the compressive strength, offering a simplified and efficient approach for predicting 3DP-FRC strength. The SHAP approach identified water, silica fume, fiber diameter, curing age, and loading directions as leading controlling parameters in predicting strength of 3DP-FRC. In summary, the proposed models can potentially minimize both the computational workload and the need for experimental trials in formulating the mixed design of 3D-printed concrete.

  • Dataset Characteristics and Their Impact on Offline Policy Learning of Contextual Multi-Armed Bandits
    • Piotr Januszewski
    • Dominik Grzegorzek
    • Paweł Czarnul
    2024 Pełny tekst

    The Contextual Multi-Armed Bandits (CMAB) framework is pivotal for learning to make decisions. However, due to challenges in deploying online algorithms, there is a shift towards offline policy learning, which relies on pre-existing datasets. This study examines the relationship between the quality of these datasets and the performance of offline policy learning algorithms, specifically, Neural Greedy and NeuraLCB. Our results demonstrate that NeuraLCB can learn from various datasets, while Neural Greedy necessitates extensive coverage of the action-space for effective learning. Moreover, the way data is collected significantly affects offline methods’ efficiency. This underscores the critical role of dataset quality in offline policy learning.

  • Daylight metrics and requirements: A review of reference documents for architectural practice
    • Amanda Pinheiro, Moura
    • Claudia David Naves
    • Natalia Sokół
    • Justyna Martyniuk-Pęczek
    2024 Pełny tekst

    Daylight has always been part of architectural practice since architects have used it to define spaces and create complex structures. Daylighting is, nowadays, seen as key strategy for sustainability, energy efficiency and resilience in buildings. This article aims to investigate daylight requirements in reference documents for architectural practice through the collection and qualitative analysis of documents. 117 reference documents were analysed and divided into standards, rating systems, building and urban codes, regulations and guidelines. Results show that static and dynamic metrics are common within standards and rating systems while building and urban codes and regulations often use metrics based on building and urban geometry. Among standards and rating systems, Daylight Factor (DF) is still one of the most used metrics, even if dynamic metrics offer advanced analyses; building and urban codes and regulations are very specific for each location, with a predominant use of geometric metrics; and guidelines can use both types of metrics.

  • D-Band High Gain Planer Slot Array Antenna using Gap Waveguide Technology
    • Ali Farahbakhsh
    • Davood Zarifi
    • Ashraf Uz Zaman
    2024 IEEE TRANSACTIONS ON ANTENNAS AND PROPAGATION

    A D-band high gain slot array antenna with corporate-fed distribution network based on gap waveguide structures is proposed at 140GHz. To overcome the fabrication challenges at such high frequency, the gap waveguide technology is deployed in which good electrical contact between different parts of the waveguide structure is not required. The proposed sub-array has four radiating slots that are excited by a groove gap cavity and the cavity is coupled to an E-plane groove gap waveguide via a rectangular coupling slot. A wideband and low-loss corporate feeding network based on the combined ridge gap waveguide and E-plane groove gap waveguide is designed for this case and the whole array antenna consists of 16×16 radiating slots. A standard WR6 waveguide flange is embedded at the bottom side of the feeding structure to excite the array antenna. To evaluate the design, a prototype is fabricated in Aluminum using standard CNC milling technique. The measurement results show that an impedance bandwidth of 20% (124.1-151.7 GHz), a peak gain of 31.5 dBi and maximum efficiency of 94% are achieved for the 16×16-element array antenna. The results show that the proposed array antenna has an excellent performance among the previously published D-band planar array antennas and could be a promising candidate to be used in the development of D-band front-end modules.

  • Dc Leakage Current in Isolated Grid-Connected dc Nanogrid - Origins and Elimination Methods
    • Mohammadreza Azizi
    • Oleksandr Husev
    • Oleksandr Veligorskyi
    • Marek Turzyński
    • Ryszard Strzelecki
    2024

    The LV dc system is a relatively new trend in the distribution sector, which seems to grow widely in the near future due to its promising advantages. In this context, LV dc protection and grounding are challenging issues. Although the galvanically isolated connection mode of dc nanogrid to the ac grid has high reliability, the leakage current can still be injected into the ac grid through the interwinding capacitors and the insulation resistance between the primary and secondary windings of the transformer. The way of grounding the dc nanogrid can also be a determining factor in the leakage current and its dc components. This study deals with the leakage current in the galvanically isolated dc nanogrid. Then, it examines the dc leakage current and its relationship with the dc nanogrid grounding and finally provides solutions to remove the dc components in the leakage current.

  • Dead time effects compensation strategy by third harmonic injection for a five-phase inverter
    • Krzysztof Łuksza
    • Dmytro Kondratenko
    • Arkadiusz Lewicki
    2024 Pełny tekst Archives of Electrical Engineering

    This paper proposes a method for compensation of dead-time effects for a fivephase inverter. In the proposed method an additional control subsystem was added to the field-oriented control (FOC) scheme in the coordinate system mapped to the third harmonic. The additional control loop operates in the fixed, orthogonal reference frame ( α - β coordinates) without the need for additional Park transformations. The purpose of this method is to minimize the dead-time effects by third harmonic injection in two modes of operation of the FOC control system: with sinusoidal supply and with trapezoidal supply. The effectiveness of the proposed control method was verified experimentally on a laboratory setup with a prototype five-phase interior permanent magnet synchronous machine (IPMSM). All experimental results were presented and discussed in the following paper.

  • Decisional-DNA-Based Digital Twin Implementation Architecture for Virtual Engineering Objects
    • Syed Imran Shafiq
    • Cesar Sanin
    • Edward Szczerbicki
    2024 Pełny tekst CYBERNETICS AND SYSTEMS

    Digital twin (DT) is an enabling technology that integrates cyber and physical spaces. It is well-fitted for manufacturing setup since it can support digitalized assets and data analytics for product and process control. Conventional manufacturing setups are still widely used all around the world for the fabrication of large-scale production. This article proposes a general DT implementation architecture for engineering objects/artifacts used in conventional manufacturing. It will empower manufacturers to leverage DT for real-time decision-making, control, and prediction for efficient production. An application scenario of Decisional-DNA based anomaly detection for conventional manufacturing tools is demonstrated as a case study to explain the architecture.

  • Decoding imagined speech for EEG-based BCI
    • Carlos A. Reyes-García
    • Alejandro A. Torres-García
    • Tonatiuh Hernández-del-Toro
    • Jesus Garcia Salinas
    • Luis Villaseñor-Pineda
    2024

    Brain–computer interfaces (BCIs) are systems that transform the brain's electrical activity into commands to control a device. To create a BCI, it is necessary to establish the relationship between a certain stimulus, internal or external, and the brain activity it provokes. A common approach in BCIs is motor imagery, which involves imagining limb movement. Unfortunately, this approach allows few commands. As an alternative, this chapter presents another approach, an internal language-related stimulus known as imagined speech, which is the action of imagining the diction of a word without emitting any sound or articulating any movement. This neuroparadigm is more intuitive, less subjective, and ambiguous, which are very relevant advantages; however, the cost to properly process the brain signal is not trivial. This chapter describes the main components of an EEG-based imagined speech BCI, along with key works, emerging trends, and challenges in this research area. Regarding the challenges, we present four of them in the pursuit of decoding imagined speech. The first challenge involves accurately recognizing isolated words. The second one is the automatic selection of a subset of EEG channels aiming to reduce computational cost and provide evidence of promising locations for studying imagined speech. The third challenge introduces an innovative approach to addressing scenarios where a new word needs to be added to the vocabulary after the computational model has been trained. Lastly, the fourth challenge concerns the online recognition of words from continuous EEG signals. Despite advances in the area, there is still much work to be done. Important initial steps have been taken in terms of the application of novel techniques for preprocessing, artifact removal, feature extraction, and classification which are the stages to be taken to process the collected signal. Additionally, the community has shared datasets and organized evaluation forums to accelerate the search for solutions.

  • Decoding soundscape stimuli and their impact on ASMR studies
    • Tomasz Piernicki
    • Sahar Seifzadeh
    • Bożena Kostek
    2024 International Journal of Electronics and Telecommunications

    This paper focuses on extracting and understanding the acoustical features embedded in the soundscape used in ASMR (Autonomous Sensory Meridian Response) studies. To this aim, a dataset of the most common sound effects employed in ASMR studies is gathered, containing whispering stimuli but also sound effects such as tapping and scratching. Further, a comparative analytical survey is performed based on various acoustical features and two-dimensional representations in the form of mel spectrogram. A special interest is in whispering sounds uttered in different languages. That is why whispering sounds are compared in the language context, and the characteristics of speaking and whispering are investigated within languages. The results of the 2D analyses are shown in the form of similarity measures, such as Normalized Root Mean Squared Error (NRMSE), PSNR (peak signal-to-noise ratio), and SSIM (structural similarity index measure). The summary is produced, showing that the analytical aspect of the inherently experiential nature of ASMR is highly affected by the subjective, personal experience, so the evidence behind triggering certain brain waves cannot be unambiguous.

  • Deep eutectic solvent-based shaking-assisted extraction for determination of bioactive compounds from Norway spruce roots
    • Alina Kalyniukova
    • Alica Varfalvyová
    • Justyna Płotka-Wasylka
    • Tomasz Majchrzak
    • Patrycja Makoś-Chełstowska
    • Ivana Tomášková
    • Vítězslava Pešková
    • Filip Pastierovič
    • Anna Jirošová
    • Vasil Andruch
    2024 Pełny tekst Frontiers in Chemistry

    Polyphenolic compounds play an essential role in plant growth, reproduction, and defense mechanisms against pathogens and environmental stresses. Extracting these compounds is the initial step in assessing phytochemical changes, where the choice of extraction method significantly influences the extracted analytes. However, due to environmental factors, analyzing numerous samples is necessary for statistically significant results, often leading to the use of harmful organic solvents for extraction. Therefore, in this study, a novel DESbased shaking-assisted extraction procedure for the separation of polyphenolic compounds from plant samples followed by LC-ESI-QTOF-MS analysis was developed. The DES was prepared from choline chloride (ChCl) as the hydrogen bond acceptor (HBA) and fructose (Fru) as the hydrogen bond donor (HBD) at various molar ratios with the addition of 30% water to reduce viscosity. Several experimental variables affecting extraction efficiency were studied and optimized using one-variable-at-a-time (OVAT) and confirmed by response surface design (RS). Nearly the same experimental conditions were obtained using both optimization methods and were set as follows: 30 mg of sample, 300 mg of ChCl:Fru 1:2 DES containing 30% w/w of water, 500 rpm shaking speed, 30 min extraction time, 10°C extraction temperature. The results were compared with those obtained using conventional solvents, such as ethanol, methanol and water, whereby the DES-based shaking-assisted extraction method showed a higher efficiency than the classical procedures. The greenness of the developed method was compared with the greenness of existing procedures for the extraction of polyphenolic substances from solid plant samples using the complementary green analytical procedure index (ComplexGAPI) approach, while the results for the developed method were better or comparable to the existing ones. In addition, the practicability of the developed procedure was evaluated by application of the blue applicability grade index (BAGI) metric. The developed procedure was applied to the determination of spruce root samples with satisfactory results and has the potential for use in the analysis of similar plant samples.

  • Deep eutectic solvents for the food industry: extraction, processing, analysis, and packaging applications – a review
    • Roberto Castro Munoz
    • Aslı Can Karaça
    • Mohammad Saeed Kharazmi
    • Grzegorz Boczkaj
    • Fernanda Jimena Hernández-Pinto
    • Shahida Anusha Siddiqui
    • Seid Mahdi Jafari
    2024 CRITICAL REVIEWS IN FOOD SCIENCE AND NUTRITION

    Food factories seek the application of natural products, green feedstock and eco-friendly processes, which minimally affect the properties of the food item and products. Today, water and conventional polar solvents are used in many areas of food science and technology. As modern chemistry evolves, new green items for building eco-friendly processes are being developed. This is the case of deep eutectic solvents (DESs), named the next generation of green solvents, which can be involved in many food industries. In this review, we timely analyzed the progress on applying DES toward the development of formulations, extraction of target biomolecules, food processing, extraction of undesired molecules, analysis and determination of specific analytes in food samples (heavy metals, pesticides), food microbiology, and synthesis of new packaging materials, among many other applications. For this, the latest developments (over the last 2-3 years) have been discussed emphasizing innovative ideas and outcomes. Relevantly, we discuss the hypothesis and the key features of using DES in the mentioned applications. To some extent, the advantages and limitations of implementing DES in the food industry are also elucidated. Finally, based on the findings of this review, the perspectives, research gaps and potentialities of DESs are stated.

  • Deep eutectic solvents with solid supports used in microextraction processes applied for endocrine-disrupting chemicals
    • Jose Grau
    • Aneta Chabowska
    • Justyna Werner
    • Agnieszka Zgoła-Grześkowiak
    • Magdalena Fabjanowicz
    • Natalia Jatkowska
    • Alberto Chisvert
    • Justyna Płotka-Wasylka
    2024 TALANTA.The International Journal of Pure and Applied Analytical Chemistry

    The determination of endocrine-disrupting chemicals (EDCs) has become one of the biggest challenges in Analytical Chemistry. Due to the low concentration of these compounds in different kinds of samples, it becomes necessary to employ efficient sample preparation methods and sensitive measurement techniques to achieve low limits of detection. This issue becomes even more struggling when the principles of the Green Analytical Chemistry are added to the equation, since finding an efficient sample preparation method with low damaging properties for health and environment may become laborious. Recently, deep eutectic solvents (DESs) have been proposed as the most promising green kind of solvents, but also with excellent analytical properties due to the possibility of custom preparation with different components to modify their polarity, viscosity or aromaticity among others. However, conventional extraction techniques using DESs as extraction solvents may not be enough to overcome challenges in analysing trace levels of EDCs. In this sense, combination of DESs with solid supports could be seen as a potential solution to this issue allowing, in different ways, to determine lower concentrations of EDCs. In that aim, the main purpose of this review is the study of the different strategies with solid supports used along with DESs to perform the determination of EDCs, comparing their advantages and drawbacks against conventional DES-based extraction methods.

  • Deep learning techniques for biometric security: A systematic review of presentation attack detection systems
    • Kashif Shaheed
    • Piotr Szczuko
    • Munish Kumar
    • Imran Qureshi
    • Qaisar Abbas
    • Ihsan Ullah
    2024 ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE

    Biometric technology, including finger vein, fingerprint, iris, and face recognition, is widely used to enhance security in various devices. In the past decade, significant progress has been made in improving biometric sys- tems, thanks to advancements in deep convolutional neural networks (DCNN) and computer vision (CV), along with large-scale training datasets. However, these systems have become targets of various attacks, with pre- sentation attacks (PAs) being prevalent and easily executed. PAs involve displaying videos, images, or full-face masks to trick biometric systems and gain unauthorized access. Many authors are currently focusing on detecting these presentation attacks (PAD) and have developed several methods, particularly those based on deep learning (DL), which have shown superior performance compared to other techniques. This survey article focuses on manuscripts related to deep learning presentation attack detection, spoof attack detection using deep learning, and anti-spoofing deep learning methods for biometric finger vein, fingerprint, iris, and face recognition. The studies were primarily sourced from four digital research libraries: ACM, Science Direct, Springer, and IEEE Xplore. The article presents a comprehensive review of DL-based PAD systems, examining recent literature on DL-based PAD methods in finger vein, fingerprint, iris, and face detection systems. Through extensive research of the literature, recent algorithms and their solutions for relevant PAD approaches are thoroughly analyzed. Additionally, the article provides a performance analysis and highlights the most promising research findings. The discussion section addresses current issues, opportunities for advancement, and potential solutions associ- ated with deep learning-based PAD methods. This study is valuable to various community users seeking to understand the significance of this technology and its recent applicability in the development of biometric technology for deep learning.

  • Deep Learning-Based Cellular Nuclei Segmentation Using Transformer Model
    • Mateusz Erezman
    • Tomasz Dziubich
    2024

    Accurate segmentation of cellular nuclei is imperative for various biological and medical applications, such as cancer diagnosis and drug discovery. Histopathology, a discipline employing microscopic examination of bodily tissues, serves as a cornerstone for cancer diagnosis. Nonetheless, the conventional histopathological diagnosis process is frequently marred by time constraints and potential inaccuracies. Consequently, there arises a pressing need for automated image analysis tools to augment medical practitioners’ efforts. In this paper, we present a novel approach utilising Transformer model, originally designed for natural language processing tasks, for automated cellular nuclei segmentation in whole-slide microscopic images. Specifically targeting cell nuclei, this methodology holds significance as the initial phase in diagnosing various illnesses, streamlining the analysis and quantification process. The study introduces a novel model that combines a U-Net architecture with a Transformer-based network functioning as a parallel encoder. This model was compared against three other popular architectures in the literature: U-Net, ResU-Net, and LinkNet-34. The impact of augmentation and colour normalisation techniques was investigated. The average Dice similarity coefficient for the considered images was found to be 0.8041.

  • Deep Learning-Based, Multiclass Approach to Cancer Classification on Liquid Biopsy Data
    • Maksym Albin Jopek
    • Krzysztof Pastuszak
    • Sebastian Cygert
    • Myron G Best
    • Thomas Würdinger
    • Jacek Jassem
    • Anna Żaczek
    • Anna Supernat
    2024 Pełny tekst IEEE Journal of Translational Engineering in Health and Medicine-JTEHM

    The field of cancer diagnostics has been revolutionized by liquid biopsies, which offer a bridge between laboratory research and clinical settings. These tests are less invasive than traditional biopsies and more convenient than routine imaging methods. Liquid biopsies allow studying of tumor-derived markers in bodily fluids, enabling the development of more precise cancer diagnostic tests for screening, disease monitoring, and therapy personalization. This study presents a multiclass approach based on deep learning to analyze and classify diseases based on blood platelet RNA. Its primary objective is to enhance cancer-type diagnosis in clinical settings by leveraging the power of deep learning combined with high-throughput sequencing of liquid biopsy. Ultimately, the study demonstrates the potential of this approach to accurately identify the patient’s type of cancer. Methods: The developed method classifies patients using heatmap images, generated based on gene expression arranged according to the Kyoto Encyclopedia of Genes and Genomes pathways. The images represent samples of patients with ovarian cancer, endometrial cancer, glioblastoma, non-small cell lung cancer, and sarcoma, as well as cancer patients with brain metastasis. Results: Our deep learning-based models reached 66.51% balanced accuracy when distinguishing between those 6 sites of cancer origin and 90.5% balanced accuracy on a location-specific dataset where cancer types from close locations were grouped. The developed models were further investigated with an explainable artificial intelligence-based approach (XAI) - SHAP. They returned a set of 60 genes with the highest impact on the models’ decision-making process. Conclusions: Our results show that deep-learning methods are a promising opportunity for cancer detection and could support clinicians’ decision-making process in finding the solution for the black-box problem. Clinical and Translational Impact Statement— Utilizing TEPs-based liquid biopsies and deep learning, our study offers a novel approach to early cancer detection, highlighting cancer origin. The integration of Explainable AI reinforces trust in predictive outcomes. Category: Early/Pre-Clinical Research.

  • Deep learning-enabled integration of renewable energy sources through photovoltaics in buildings
    • Munusamy Arun
    • Thanh Tuan Le
    • Debabrata Barik
    • Prabhakar Sharma
    • Sameh M. Osman
    • Van Kiet Huynh
    • Jerzy Kowalski
    • Van Huong Dong
    • Viet Vinh Le
    2024 Pełny tekst Case Studies in Thermal Engineering

    Installing photovoltaic (PV) systems in buildings is one of the most effective strategies for achieving sustainable energy goals and reducing carbon emissions. However, the requirement for efficient energy management, the fluctuating energy demands, and the intermittent nature of solar power are a few of the obstacles to the seamless integration of PV systems into buildings. These complexities surpass the capabilities of rule-based systems, necessitating innovative solutions. The research proposes a deep learning-based optimal energy management system designed specifically for home micro-grids that incorporate PV systems with battery energy storage, Enhanced Long Short-Term Memory (LSTM)-Based Optimal Home Micro-Grid Energy Management (OHM-GEM). Integrating an improved type of LSTM neural network called LSTM into the energy management system improves the reliability of PV power output predictions. The dependability of PV power production forecasts is increased by including a refined version of the LSTM neural network in the energy management system. The efficiency of the OHM-GEM system in maximizing PV system integration into buildings is shown by the authors using simulated data. With considerable gains in energy efficiency, cost savings, and decreased reliance on non-renewable energy sources, the results highlight the possibility of this approach to forward sustainable energy practices.

  • Deep Video Multi-task Learning Towards Generalized Visual Scene Enhancement and Understanding
    • Efkleidis Katsaros
    2024 Pełny tekst

    The goal of this thesis was to develop efficient video multi-task convolutional architectures for a range of diverse vision tasks, on RGB scenes, leveraging i) task relationships and ii) motion information to improve multi-task performance. The approach we take starts from the integration of diverse tasks within video multi-task learning networks. We present the first two datasets of their kind in the existing literature, featuring frame-level annotations for both visual scene enhancement and understanding. This thesis proposes novel architectures, capable of accommodating multiple tasks across various hierarchy levels. The second contribution of this thesis extends those findings into the MOST (Multi-Output, -Scale, -Task) model which exploits the inherent multi-scale nature of convolutional networks in a manner that benefits video multi- tasking. Thereafter, we propose a principled pruning approach inspired by NAS (Neural Architecture Search), named NSS (Neural Structure Search). NSS discovers a more effective MOST network, which boosts performance while simultaneously reducing computational requirements and parameter count. Lastly, we introduce ATB (Adaptive Task Balancing), an efficient training method that ensures tasks are trained at consistent rates with almost no additional computational cost, enabling a more balanced multi-task training process.

  • Defected Ag/Cu-MOF as a modifier of polyethersulfone membranes for enhancing permeability, antifouling properties and heavy metal and dye pollutant removal
    • Vahid Vatanpour
    • Rabia Ardic
    • Berk Esenli
    • Bahriye Eryildiz-Yesir
    • Parisa Yaqubnezhad Pazoki
    • Atefeh Jarahiyan
    • Firouz Matloubi Moghaddam
    • Roberto Castro Munoz
    • Ismail Koyuncu
    2024 SEPARATION AND PURIFICATION TECHNOLOGY

    In this study, a novel bimetallic metal-organic framework (MOF) i.e. Ag/Cu-MOF was synthesized using a solvothermal method and later incorporated at different concentrations (0.1–2 wt%) using a phase inversion method for modification and antifouling property improvement of polyethersulfone (PES) membranes. The resulting Ag/Cu-MOF characteristics were investigated using different techniques, such as FTIR, XRD, FE-SEM and EDX. The membranes were characterized by FE-SEM, contact angle, porosity, mean pore size, surface roughness and zeta potential. Furthermore, membrane performance was examined using pure water flux, BSA, Pb(II), dye removal and fouling properties. In particular, the results showed that the addition of 1.0 wt% of the Ag/Cu-MOF decreased the water contact angle from 68.5° to 59.6° while enhancing overall porosity from 45.1 % to 56.0 %. The maximum water permeability was obtained with 1.0 wt% Ag/Cu-MOF (ca. 100 L/m2.h.bar) representing 1.9 times higher flux than that of the bare PES membrane due to the hydrophilic nature of the bimetallic MOF. As for the rejection performance, high Pb(II), BSA, reactive black 5 and reactive red 120 rejections values were observed as 92.6 %, 99.5 %, 96.4 % and 98.4 %, respectively. The Ag/Cu-MOF embedded membrane showed antibacterial behavior against Escherichia coli and antifouling properties, causing a considerable decrease in fouling resistance parameters and significant improvement in the antifouling properties of the PES membrane. The results of this study demonstrated that the Ag/Cu-MOF could be a promising material for boosting the polymeric membrane properties.

  • Deformation mitigation and twisting moment control in space frames
    • Ahmed Manguri
    • Najmadeen Saeed
    • Robert Jankowski
    2024 Pełny tekst Structures

    Over the last five decades, space frames have centered on the modernization of touristic zones in view of architectural attractions. Although attempts to control joint movement and minimize axial force and bending moment in such structures were made sufficiently, twisting moments in space frames have been underestimated so far. In space frames, external load or restoring the misshapen shape may cause twisting in members. We herein developed a robust computational algorithm to reduce the induced torsional moment through shape restoration within a desired limit by changing the length of active bars that are placed in space frames. Applying optimization algorithms like interior-point and Sequential quadratic programming (SQP), a direct correlation was pursued between bar length alteration and twisting in structural members. A numerical model of a single-layer space frame resembling an egg captures the twisting moment in all members, achieving a specified limit. The overall length change of the active members using an iterative process based on a heuristic that considers a threshold on the minimum length change of the active members.

  • Degradacja i uszkodzenia podbudowy jako przyczyny awarii betonowych posadzek przemysłowych
    • Sylwia Świątek-Żołyńska
    • Maciej Niedostatkiewicz
    • Władysław Ryżyński
    2024

    Posadzki i nawierzchnie betonowe doznają podczas użytkowania zróżnicowanych w swojej skali i czasie degradacji takich jak spękania, zarysowania i klawiszowanie płyt. Część z tych uszkodzeń jest spowodowana wadami wykonania, niską jakością betonu lub błędami projektowymi płyty konstrukcyjnej, ale w znacznej części jest to związane z wadliwą podbudową. Podbudowa jest bowiem istotnym elementem składowym posadzki, zapewniającym wymaganą nośność i sztywność całego układu

  • DEM modelling of concrete fracture based on its structure micro-CT images
    • Michał Nitka
    • Andrzej Tejchman-Konarzewski
    2024

    W rozdziale książki zawarto numeryczne wyniki mezoskopowe dotyczące postępującego pękania betonu na poziomie kruszywa. Do badania procesu pękania belki betonowej z karbem w trzech (3D) i dwóch (2D) wymiarach zastosowano metodę elementów dyskretnych (DEM). Niejednorodność betonu uwzględniono stosując czterofazowy opis: kruszywo, zaprawa, makropustki i międzyfazowe strefy przejściowe. W obliczeniach DEM na podstawie zdjęć rentgenowskich mikro-CT przyjęto rzeczywistą postać i rozmieszczenie kruszywa w betonie. Osiągnięto dobry poziom zgodności w odniesieniu do siły pionowej wpływającej na ewolucję przemieszczenia otworu wylotowego pęknięcia i kształtu pęknięcia pomiędzy analizą DEM a pomiarami laboratoryjnymi. Ewolucja zerwanych styków, sił normalnych kontaktu, rotacji cząstek, energii wewnętrznych, kształtu kruszywa oraz porowatości i szerokości ITZ były szeroko zbadane numerycznie na poziomie agregatów. Wyniki 3D również mocno kontrastowały z wynikami 2D. Pokazano, że model 3D DEM jest potencjalnym narzędziem do modelowania umożliwiającym przewidywanie i zrozumienie pękania betonu na poziomie mezoskopowym i makroskopowym.

  • DESIGN AND EXECUTION ERRORS AS A CAUSE OF DAMAGE TO ANTI- ELECTROSTATIC FLOORING
    • Sylwia Świątek-Żołyńska
    • Maciej Niedostatkiewicz
    • Władysław Ryżyński
    2024

    Apart from technological lines, industrial floors are a key element in the scope of maintaining the continuity of work of both production plants and logistics centers. The constantly developing industry of industrial flooring includes both classic design and technological flooring solutions, as well as specialist solutions used in facilities where technological processes or storage require system protection against static electricity. The basic design and implementation activity, apart from the use of earthing systems for the elements of the supporting structure of the facility, is the execution of anti-electrostatic floors with parameters and functional features adapted to the function of the facility.

  • Design and Experimental Validation of a Metamaterial-Based Sensor for Microwave Imaging in Breast, Lung, and Brain Cancer Detection
    • Musa Hamza
    • Sławomir Kozieł
    • Anna Pietrenko-Dąbrowska
    2024 Pełny tekst Scientific Reports

    This study proposes an innovative geometry of a microstrip sensor for high-resolution microwave imaging (MWI). The main intended application of the sensor is early detection of breast, lung, and brain cancer. The proposed design consists of a microstrip patch antenna fed by a coplanar waveguide with a metamaterial layer-based lens implemented on the back side, and an artificial magnetic conductor (AMC) realized on as a separate layer. The analysis of the AMC’s permeability and permittivity demonstrate that the structure exhibits negative epsilon (ENG) qualities near the antenna resonance point. In addition, reflectivity, transmittance, and absorption are also studied. The sensor prototype has been manufactures using the FR4 laminate. Excellent electrical and field characteristics of the structure are confirmed through experimental validation. At the resonance frequency of 4.56 GHz, the realized gain reaches 8.5 dBi, with 3.8 dBi gain enhancement contributed by the AMC. The suitability of the presented sensor for detecting brain tumors, lung cancer, and breast cancer has been corroborated through extensive simulation-based experiments performed using the MWI system model, which employs four copies of the proposed sensor, as well as the breast, lung, and brain phantoms. As demonstrated, the directional radiation pattern and enhanced gain of the sensor enable precise tumor size discrimination. The proposed sensor offers competitive performance in comparison the state-of-the-art sensors described in the recent literature, especially with respect to as gain, pattern directivity, and impedance matching, all being critical for MWI.