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Gdańsk University of Technology

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  • Kompleksowa ocena nośności ramowego wiaduktu żelbetowego. Uaktualnienie parametrów modelu na podstawie badań eksperymentalnych
    • Krzysztof Żółtowski
    • Przemysław Kalitowski
    • Mikołaj Binczyk
    2024 Inżynieria i Budownictwo

    W artykule opisano stosunkowo prostą metodę aktualizacji parametrów modelu na podstawie obciążeń próbnych. Podczas ponownych badań obciążeniowych, przeprowadzonych po 12 latach eksploatacji, stworzono model, którego parametry, w tym wytrzymałość betonu na rozciąganie oraz pozioma sztywność podpór, zostały zaktualizowane na podstawie testów odbiorczych. Przeprowadzono analizę parametryczną, uwzględniając nieliniowe obliczenia z możliwością zarysowania żelbetu. Zarejestrowano symulowane wartości ugięć całkowitych, sprężystych i trwałych. Na ich podstawie wybrano parametry modelu. Określone parametry zastosowano do projektowania obciążenia próbnego w 2023 roku, które następnie zostało przeprowadzone. Uzyskano dobrą zgodność wyników teoretycznych z eksperymentalnymi na średnim poziomie 90,4%.

  • Krajobraz wiejski i pamięć zbiorowa
    • Anna Górka
    2024 Zawód: Architekt

    Źródła trwających od lat barbarzyńskich praktyk, jakim poddawany jest wiejski krajobraz, należałoby szukać m.in. w zbiorowej „utracie pamięci”. Szansą na przywrócenie ładu przestrzennego na obszarach wiejskich byłby wówczas mit, nadający społeczną wartość zasobom ich krajobrazów i na tej bazie rozwijający tożsamość modernizowanej wsi. Esej podejmuje wybrane wątki tekstu pt. Krajobraz kulturowy wsi jako nośnik mitu, który ukazał się w monografii Niematerialne wartości krajobrazów kulturowych, w czasopiśmie „Prace Komisji Krajobrazu Kulturowego”, nr 15 z 2011 r.

  • Krajowe laboratorium sieci i usług PL, 5G: kierunki badań i perspektywy rozwoju techniki 5G/6G
    • Andrzej Bęben
    • Maciej Sosnowski
    • Wotold Jóźwiak
    • Józef Woźniak
    • Krzysztof Gierłowski
    • Michał Hoeft
    • Marek Natkaniec
    • Piotr Boryło
    • Bartosz Belter
    • Maksymilian Furmann
    • Schauer Patryk
    • Łukasz Falas
    • Arkadiusz Warzyński
    • Igor Michalski
    • Dariusz Więcek
    2024 Full text Przegląd Telekomunikacyjny + Wiadomości Telekomunikacyjne

    Artykuł przedstawia unikatową infrastrukturę badawczą PL 5G opracowaną w ramach projektu „ krajowe laboratorium sieci i usług 5G wraz z otoczeniem” oraz kierunki badań dotyczących techniki 5G oraz przyszłej sieci 6g. Laboratorium umożliwia prowadzenie praktycznych eksperymentów, w środowisku zbliżonym do warunków sieci operatorskiej, dotyczących rozwoju techniki 5G/6G, a także szerokiego spektrum jej zastosowań w środowiskach terenowych, tj. morskim, lotniczym, przemysłowym czy miejskim. przedstawiono również przykładowe eksperymenty wykorzystujące rozważaną infrastrukturę.

  • Kriging-assisted hybrid reliability design and optimization of offshore wind turbine support structure based on a portfolio allocation strategy
    • Debiao Meng
    • Hengfei Yang
    • Shiyuan Yang
    • Yuting Zhang
    • Abílio M.P. De Jesus
    • José A.F.O. Correia
    • Tiago Fazeres-Ferradosa
    • Wojciech Macek
    • Ricardo Branco
    • Shun-Peng Zhu
    2024 OCEAN ENGINEERING

    In recent years, offshore wind power generation technology has developed rapidly around the world, making important contributions to the further development of renewable energy. When designing an Offshore Wind Turbine (OWT) system, the uncertainties in parameters and different types of constraints need to be considered to find the optimal design of these systems. Therefore, the Reliability-Based Design Optimization (RBDO) method is usually adopted to ensure the stability and reliability of the design scheme. However, the calculation cost is huge in the RBDO problem considering mixed uncertainties. The Kriging model is a widely used approximation technique to reduce the computational cost in RBDO. However, establishing a sufficiently accurate Kriging model for a complex engineering system often requires the collection of more sample data and more time-consuming performance evaluation. In order to solve this problem, this study proposes a hybrid RBDO method based on a Portfolio allocation strategy. Based on ensuring the accuracy of the Kriging model, this method requires fewer iterations than the previous method of iteratively establishing the Kriging model using the same learning function. Furthermore, the optimal design of the system can be completed in a shorter time. This has great application potential to reduce the time labor and material costs spent in the design process of OWT. Two mathematical examples and two engineering examples are used to verify the accuracy of the method. Then, the proposed method is used in the design and optimization of a typical OWT support structure, showing the method's feasibility and superiority.

  • Lab-in-syringe as a practical technique for automatic microextraction: Evaluation by Blue Applicability Grade Index
    • Natalia Manousi
    • Justyna Płotka-Wasylka
    • Erwin Rosenberg
    • Aristidis Anthemidis
    2024 TRAC-TRENDS IN ANALYTICAL CHEMISTRY

    Lab-in-syringe (LIS) is a powerful automatic technique that is derived from sequential injection analysis. In LIS, a computer-controlled syringe pump is employed, and its syringe barrel serves as a mixing, reaction, and/or extraction chamber. Until now, the LIS concept has been efficiently employed for the automation of solid-phase microextraction and liquid-phase microextraction protocols as a front-end to a plethora of chromatographic and spectrometric techniques. In this work, the applicability of LIS systems was examined using the Blue Applicability Grade Index (BAGI), a recently introduced metric tool that is used to examine the applicability of an analytical method. For this purpose, the sample preparation procedure and the instrumental method are thoroughly evaluated. The attained BAGI scores ranged between 60.0 and 77.5 for all the examined methods, while the average score was 71.3. As derived from the BAGI evaluation, good practicality can be attributed to the reviewed systems and protocols.

  • Landscape, EIA and decision-making. A case study of the Vistula Spit Canal, Poland
    • Aleksandra Sas-Bojarska
    • Iwona Orzechowska-Szajda
    • Krystian Puzdrakiewicz
    • Magdalena Kiejzik-Głowińska
    2024 Full text Impact Assessment and Project Appraisal

    Although landscapes are often considered public goods, they frequently receive inadequate attention in Environmental Impact Assessments (EIAs), particularly in Poland. This neglect often leads to visible degradation during investment processes. This article examines the case of the Vistula Spit Canal, currently the largest engineering project under construction in Poland. We analysed whether the conclusions drawn in the EIA report, particularly those concerning landscape changes, influenced the decision to proceed with the construction. Although the EIA report described potential landscape changes as both significant and irreversible, the authorities nonetheless approved the project, citing moderate environmental impacts in other areas. This case underscores the tendency to overlook landscape considerations when greenlighting large-scale investments.

  • Large magnetoresistance and first-order phase transition in antiferromagnetic single-crystalline EuAg4Sb2
    • Sudip Malick
    • Hanna Świątek
    • Joanna Blawat
    • John Singleton
    • Tomasz Klimczuk
    2024 Full text PHYSICAL REVIEW B

    present the results of a thorough investigation of the physical properties of single crystals using magnetization, heat capacity, and electrical resistivity measurements. High-quality single crystals, which crystallize in a trigonal structure with space group , were grown using a conventional flux method. Temperature-dependent magnetization measurements along different crystallographic orientations confirm two antiferromagnetic phase transitions around and . Isothermal magnetization data exhibit several metamagnetic transitions below these transition temperatures. Antiferromagnetic phase transitions in are further confirmed by two sharp peaks in the temperature-dependent heat capacity data at 1 and 2, which shift to lower temperature in the presence of an external magnetic field. Our systematic heat capacity measurements utilizing a long-pulse and single-slope analysis technique allow us to detect a first-order phase transition in EuAg4Sb2 at 7.5 K. The temperature-dependent electrical resistivity data also manifest two features associated with magnetic order. The magnetoresistance exhibits a broad hump due to a field-induced metamagnetic transition. Remarkably, the magnetoresistance keeps increasing without showing any tendency to saturate as the applied magnetic field increases, and it reaches ∼20 000% at 1.6 K and 60 T. At high magnetic fields, several magnetic quantum oscillations are observed, indicating a complex Fermi surface. A large negative magnetoresistance of about −55% is also observed near 1. Moreover, the − phase diagram constructed using magnetization, heat capacity, and magnetotransport data indicates complex magnetic behavior in EuAg4Sb2.

  • Large‐Scale Traveling Ionospheric Disturbances Over the European Sector During the Geomagnetic Storm on March 23–24, 2023: Energy Deposition in the Source Regions and the Propagation Characteristics
    • Grzegorz Nykiel
    • Arthur Amaral Ferreira
    • Florian Günzkofer
    • Pelin Iochem
    • Samira Tasnim
    • Hiroatsu Sato
    2024 Full text JOURNAL OF GEOPHYSICAL RESEARCH-SPACE PHYSICS

    Multiple Large-Scale Traveling Ionospheric Disturbances (LSTIDs) are observed in the European sector in both day-time and night-time during the magnetic storm on March 23–24, 2023. The Total Electron Content (TEC) observation from a network of GNSS receivers shows the propagation of LSTIDs with amplitudes between around 0.5 and 1 TECU originating from auroral and polar cusp regions down to southern Europe (35°N) with velocities between around 500 and 1,600 [m/s]. We study the energy deposition to the LSTIDs in the source regions and the resulting horizontal propagation over storm-time background density by using continuous measurements of EISCAT incoherent scatter radars in northern Norway and Svalbard that allow for estimating the source energy to the thermosphere-ionosphere system via Joule heating and particle precipitation. Both EISCAT and GNSS TEC data show that the electron density decreased to 50% in the auroral zone after the storm onset. The ionospheric heating caused a nearly 250% increase in the electron temperature above 200 km altitude and the ion temperature above 100 km altitude. We find that Joule Heating acts as a primary energy source for the night-time LSTIDs triggered in the auroral region, while the day-time LSTIDs can be also driven by precipitating particles in the polar cusp. We also find that a significant background density decrease over the whole European sector is caused by this storm for the following day, during which almost no clear LSTIDs are observed.

  • Laser-textured cross-hatched surface topography analysis with evaluation of high-frequency measurement noise
    • Przemysław Podulka
    • Wojciech Macek
    • Ricardo Branco
    • Andrzej Kubit
    2024 Full text MEASUREMENT

    The precision of surface roughness determination using ISO 25178 parameters relies on various factors that directly impact the measurement process. In industry applications, the contactless roughness measurement reduces data collection time. However, it introduces several potential errors, including those stemming from the environment. One of the main types of errors encountered during topography analysis is measurement noise, which arises from different external disturbances. High-frequency noise is particularly studied as a result of vibration. In the present study, the laser-texture cross-hatched surface topographies were analysed using the results obtained from white light interference measurements. Measurement noise was defined based on noisy data, also called noise surface, which is the result of filter decomposition methods. This data separation technique was supported with power spectral analysis, autocorrelation function applications and texture direction characterisation. It was suggested to conduct a comprehensive study of the noisy data to enhance the understanding of texturing direction. Various data filtration techniques were studied, namely robust Gaussian, spline, fast Fourier transform and morphological closing-opening filters. The results of the proposed procedure were validated against variations in the values of ISO 25178 surface texture parameters. Validating the proposed approach, the variations of noise-sensitive surface texture parameters were compared to the variations of the same parameters but received by averaging three repeated measurements, as proposed by international standards. The main advantage of the proposed method against standards procedure was reducing the time of data collection when the measurement must be repeated and averaged. In conclusion, a method for reducing high-frequency measurement noise was introduced through the application of the proposed procedure.

  • Lattice-commensurate skyrmion texture in a centrosymmetric breathing kagome magnet
    • Max Hirschberger
    • Bertalan G. Szigeti
    • Mamoun Hemmida
    • Moritz M. Hirschmann
    • Sebastian Esser
    • Hiroyuki Ohsumi
    • Yoshikazu Tanaka
    • Leonie Spitz
    • Shang Gao
    • Kamil Kolincio
    • Hajime Sagayama
    • Hironori Nakao
    • Yuichi Yamasaki
    • László Forró
    • Hans-Albrecht Krug von Nidda
    • Istvan Kezsmarki
    • Taka-hisa Arima
    • Yoshinori Tokura
    2024 Full text npj Quantum Materials

    Skyrmion lattices (SkL) in centrosymmetric materials typically have a magnetic period on the nanometer-scale, so that the coupling between magnetic superstructures and the underlying crystal lattice cannot be neglected. We reveal the commensurate locking of a SkL to the atomic lattice in Gd3Ru4Al12 via high-resolution resonant elastic x-ray scattering (REXS). Weak easy-plane magnetic anisotropy, demonstrated here by a combination of ferromagnetic resonance and REXS, penalizes placing a skyrmion core on a site of the atomic lattice. Under these conditions, a commensurate SkL, locked to the crystal lattice, is stable at finite temperatures – but gives way to a competing incommensurate ground state upon cooling.Wediscuss the role of Umklapp-terms in theHamiltonian for the formation of this lattice-locked state, its magnetic space group, and the role of slight discommensurations, or (line) defects in the magnetic texture. We also contrast our findings with the case of SkLs in noncentrosymmetric material platforms.

  • LCF behavior of 2024AA under uni- and biaxial loading taking into account creep pre-deformation
    • Adam Tomczyk
    • Andrzej Seweryn
    2024 ENGINEERING FRACTURE MECHANICS

    This study presents the results of experimental low-cycle fatigue (LCF) tests of aluminum 2024 alloy T3511 temper in uni- and biaxial loading states. Tests were carried out on both the as-received material (hardened extruded rods) and material with different pre-deformation histories. These deformations were carried out in the creep process at 200 °C and 300 °C for two different levels of at each temperature. The pre-deformed material’s basic fatigue characteristics were determined and compared with the appropriate characteristics of the as-received material. In-depth macro- and microscopic analysis (SEM) of fracture surfaces was done. The effect of preliminary creep on LCF behavior of investigated alloy was characterized for both uni- and biaxial loading. An increase of fatigue life occurs for large plastic strains in the case of cyclic tension/compression. For in-phase biaxial loading, improvement of life is observed only for material with pre-deformation at 300 °C. Crack initiates in the plane of maximum shear strains for both biaxial loading and pure torsion. For tension/compression – in the plane of maximum principal stress (strain).

  • LDNet: A Robust Hybrid Approach for Lie Detection Using Deep Learning Techniques
    • Shanjita Akter Prome
    • Md Rafiqul Islam
    • David Asirvatham
    • Neethiahnanthan Ari Ragavan
    • Cesar Sanín
    • Edward Szczerbicki
    2024 CMC-Computers Materials & Continua

    Deception detection is regarded as a concern for everyone in their daily lives and affects social interactions. The human face is a rich source of data that offers trustworthy markers of deception. The deception or lie detection systems are non-intrusive, cost-effective, and mobile by identifying facial expressions. Over the last decade, numerous studies have been conducted on deception detection using several advanced techniques. Researchers have focused their attention on inventing more effective and efficient solutions for the detection of deception. So, it could be challenging to spot trends, practical approaches, gaps, and chances for contribution. However, there are still a lot of opportunities for innovative deception detection methods. Therefore, we used a variety of machine learning (ML) and deep learning (DL) approaches to experiment with this work. This research aims to do the following: (i) review and analyze the current lie detection (LD) systems; (ii) create a dataset; (iii) use several ML and DL techniques to identify lying; and (iv) create a hybrid model known as LDNet. By combining layers from Vgg16 and DeneseNet121, LDNet was developed and offered the best accuracy (99.50%) of all the models. Our developed hybrid model is a great addition that significantly advances the study of LD. The findings from this research endeavor are expected to advance our understanding of the effectiveness of ML and DL techniques in LD. Furthermore, it has significant practical applications in diverse domains such as security, law enforcement, border control, organizations, and investigation cases where accurate lie detection is paramount.

  • Leading with Understanding: Cultivating Positive Relationships between Neurotypical Leaders and Neurodivergent Employees
    • Joanna Szulc
    2024 Full text Employee Relations

    Neurodivergent employees have atypical needs that require distinctive leadership approaches. In this study, the specific nature of a relationship between neurodivergent employees and their neurotypical leaders is explored through the lens of the Leader-Member-Exchange (LMX) theory. This two-phased qualitative study builds on 12 semi-structured interviews with neurodivergent employees and an unstructured focus group with 15 individuals with professional and/or personal interest in neurodiversity. The researcher spent almost 13 hours listening to the lived experiences of research participants concerning neurodiversity and leadership. Leaders who exhibit empathy and understanding were noted to provide greater support. The findings also highlight the complexity of neuro-inclusion in the workplace. Specifically, the delicate balance between accommodation and avoiding stigmatization is emphasized, addressing the concerns raised regarding the legal risks associated with neurodivergent inclusion. Additionally, the findings underscore the necessity for leaders to avoid patronizing behaviours while catering to the diverse needs of neurodivergent employees. This underscores the importance of supporting both neurodivergent employees but also leaders navigating such challenges. The findings help to establish inclusive and accommodating employee relations practices that conscientiously address the requirements of neurodivergent employees while providing support for those in leadership roles. This study constitutes a direct answer to recent calls to develop more nuanced understanding of workplace neurodiversity with a specific focus on neuro-inclusive leadership. Acknowledging that we still use inappropriate, old tools in new situations that require novel approaches to leadership, it helps to set the agenda for future research in this area.

  • Learning sperm cells part segmentation with class-specific data augmentation
    • Marcin Jankowski
    • Emilia Lewandowska
    • Hugues Talbot
    • Daniel Węsierski
    • Anna Węsierska
    2024

    Infertility affects around 15% of couples worldwide. Male fertility problems include poor sperm quality and low sperm count. The advanced fertility treatment methods like ICSI are nowadays supported by vision systems to assist embryologists in selecting good quality sperm. Computer-Assisted Semen Analysis (CASA) provides quantitative and qualitative sperm analysis concerning concentration, motility, morphology, vitality, and fragmentation. However, fertility assessment algorithms often neglect individual spermatozoon tail and its beating patterns because recognizing the tails in blurry microscopic images reliably is challenging. In this article, we propose that models trained with head and tail part classes can better localize parts and segment the whole spermatozoon objects. Usually, the training of segmentation sperm models is supported by image-level augmentation. We argue that models guided by class-specific data augmentation attend to less discriminative sperm parts. To demonstrate this, we decouple the augmentation into object-level and background augmentation for the sperm part segmentation problem. Our proposed method outperforms state-of-the-art methods on the SegSperm dataset. Moreover, our ablation studies confirm the effectiveness of the proposed part-based object representation and augmentation.

  • Leather Waste Hydrolysation, Carbonization, and Microbial Treatment for Nitrogen Recovery by Ryegrass Cultivation
    • Ksawery Kuligowski
    • Dawid Skrzypczak
    • Katarzyna Mikula
    • Katarzyna Chojnacka
    • Paulina Bandrów
    • Robert Tylingo
    • Szymon Mania
    • Adrian Woźniak
    2024 Materials

    Leather waste contains up to 10% nitrogen (N); thus, combustion or gasification only for the energy recovery would not be rational, if safety standards are met. On the other hand, the chromium (Cr) content exceeding 5% in half of the waste stream (w/w) is too significant to be applied in agriculture. In this work, four acid hydrolysates from leather waste shavings, both wet-white free of Cr and wet-blue with Cr, were used: two with a mixture of acids and supplemented with Cu, Mn, and Zn, and the other two as semi-products from collagen extraction using hydrochloric acid. Additionally wet-green leather waste shavings, e.g., impregnated with olive extract, were used followed by the two treatments: amendment with a biochar from “wet white” leather waste shavings and amendment with this biochar incubated with the commercial phosphorus stimulating microbial consortia BactoFos. They were applied as organic nitrogen-based fertilizers in a glasshouse experiment, consisting of 4–5 subsequent harvests every 30 days, under spring–autumn conditions in northern Poland. Biochar-amended wet-greens provided the highest nitrogen use efficiencies, exceeding 100% after 4 months of growth (for 20 kg N/ha) and varying from 17% to 37% in particular months. This is backed up by another parameter (relative agronomic effectiveness) that for these materials exceeded 150% for a single month and in total was around 33%. Biochar amendments significantly increased agronomic parameters for wet-greens, and their microbial treatment enhanced them even further. Recycling this type of waste can replace inorganic fertilizers, reducing greenhouse gas emissions and carbon footprint.

  • LemPhos – New P-Chiral Phospholene Core Based Ligand
    • Adam Włodarczy
    • Łukasz Ponikiewski
    2024 SYNTHESIS-STUTTGART

    Synthesis and modifications of new chiral phospholene-based scaffolds are described. The construction of base molecules was accomplished via McCormack synthesis. Separation of single P-epimers was accomplished with column chromatography on silica gel.

  • Lepidium peruvianum as a Source of Compounds with Anticancer and Cosmetic Applications
    • Dorota Kasprzak
    • Katarzyna Gaweł-Bęben
    • Wirginia Kukuła-Koch
    • Marcelina Strzępek-Gomółka
    • Anna Wawruszak
    • Sylwia Woźniak
    • Marcelina Chrzanowska
    • Karolina Czech
    • Julia Borzyszkowska-Bukowska
    • Kazimierz Głowniak
    • Dariusz Matosiuk
    • Rita Cristina Orihuela-Campos
    • Barbara Jodłowska-Jędrych
    • Tomasz Laskowski
    • Henry O. Meissner
    2024 INTERNATIONAL JOURNAL OF MOLECULAR SCIENCES

    Lepidium peruvianum—an edible herbaceous biennial plant distributed in the Andes—has been used for centuries as food and as a natural medicine in treating hormonal disorders, as an antidepressant, and as an anti-osteoporotic agent. The presented study aims to prove its beneficial cosmetic and chemopreventive properties by testing the antiradical, whitening, cytotoxic, and anticancer properties of differently colored phenotypes that were extracted using three solvents: methanol, water, and chloroform, with the help of the chemometric approach to provide evidence on the impact of single glucosinolanes (seven identified compounds in the HPLC-ESI-QTOF-MS/MS analysis) on the biological activity of the total extracts. The tested extracts exhibited moderate antiradical activity, with the methanolic extract from yellow and grey maca phenotypes scavenging 49.9 ± 8.96% and 48.8% ± 0.44% of DPPH radical solution at a concentration of 1 mg/mL, respectively. Grey maca was the most active tyrosinase inhibitor, with 72.86 ± 3.42% of the enzyme activity calculated for the water extract and 75.66 ± 6.21% for the chloroform extract. The studies in cells showed no cytotoxicity towards the human keratinocyte line HaCaT in all studied extracts and a marked inhibition of cell viability towards the G361 melanoma cell line, which the presence of pent-4-enylglucosinolate, glucotropaeolin, and glucoalyssin in the samples could have caused. Given all biological activity tests combined, the three mentioned compounds were shown to be the most significant positive contributors to the results obtained, and the grey maca water extract was found to be the best source of the former compound among the tested samples.

  • Lessons learned in a decade: Medical‐toxicological view of tattooing
    • Michael Giulbudagian
    • Beatrice Battisini
    • Wolfgang Bäumler
    • Ana M Rico Blass
    • Beatrice Bocca
    • Corinna Brungs
    • Marco Famele
    • Milena Foerster
    • Birgit Gutsche
    • Veit Houben
    • Urs Hauri
    • Katarzyna Karpienko
    • Uwe Karst
    • Linda M. Katz
    • Nicolas Kluger
    • Jørgen Serup
    • Steffen Schubert
    • Ines Schreiver
    • Sebastiaan A. S. van der Bent
    • Carina Wolf
    • Andreas Luch
    • Peter Laux
    2024 Full text JOURNAL OF THE EUROPEAN ACADEMY OF DERMATOLOGY AND VENEREOLOGY

    Tattooing has been part of the human culture for thousands of years, yet only in the past decades has it entered the mainstream of the society. With the rise in popularity, tattoos also gained attention among researchers, with the aim to better understand the health risks posed by their application. 'A medical-toxicological view of tattooing'-a work published in The Lancet almost a decade ago, resulted from the international collaboration of various experts in the field. Since then, much understanding has been achieved regarding adverse effects, treatment of complications, as well as their regulation for improving public health. Yet major knowledge gaps remain. This review article results from the Second International Conference on Tattoo Safety hosted by the German Federal Institute for Risk Assessment (BfR) and provides a glimpse from the medical-toxicological perspective, regulatory strategies and advances in the analysis of tattoo inks.

  • Leveraging Activation Maps for Improved Acoustic Events Detection and Classification
    • Maciej Szczodrak
    • Józef Kotus
    • Piotr Szczuko
    • Grzegorz Szwoch
    2024

    This paper presents a novel approach to enhance the accuracy of deep learning models for acoustic event detection and classification in real-world environments. We introduce a method that leverages activation maps to identify and address model overfitting, combined with an expert-knowledge-based event detection algorithm for data pre-processing. Our approach significantly improved classification performance, increasing the F1 score from 0.65 in the baseline model to 0.96 in the optimized model. The method was evaluated on a diverse validation dataset of 100 samples per each of 8 classes of urban acoustic events, including gunshots, explosions, and screams. Gradient-weighted Class Activation Mapping (Grad-CAM) visualizations confirmed that our enhanced model focuses on relevant signal components, reducing reliance on irrelevant background information. Additionally, our expert detection module enables efficient online processing by bypassing the classifier for non-event signals. This research demonstrates the effectiveness of combining explainable AI techniques with domain expertise to improve the robustness and efficiency of acoustic event classification systems.

  • Leveraging food waste for electricity: A low-carbon approach in energy sector for mitigating climate change and achieving net zero emission in Hong Kong (China)
    • Tonni Agustiono Kurniawan
    • Xue Liang
    • Hui Hwang Goh
    • Mohd Hafiz Dzarfan Othman
    • Abdelkader Anouzla
    • Hussein Al-Hazmi
    • Kit Wayne Chew
    • Faissal Aziz
    • Imran Ali
    2024 JOURNAL OF ENVIRONMENTAL MANAGEMENT

    In recent years, food waste has been a global concern that contributes to climate change. To deal with the rising impacts of climate change, in Hong Kong, food waste is converted into electricity in the framework of low-carbon approach. This work provides an overview of the conversion of food waste into electricity to achieve carbon neutrality. The production of methane and electricity from waste-to-energy (WTE) conversion are determined. Potential income from its sale and environmental benefits are also assessed quantitatively and qualitatively. It was found that the electricity generation from the food waste could reach 4.33 109 kWh annually, avoiding equivalent electricity charge worth USD 3.46 109 annually (based on US' 8/kWh). An equivalent CO2 mitigation of 9.9 108 kg annually was attained. The revenue from its electricity sale in market was 1.44 in the 1st year and USD 4.24 in the 15th year, respectively, according to the projected CH4 and electricity generation. The modelling study indicated that the electricity production is 0.8 kWh/kg of landfilled waste. The food waste could produce electricity as low as US' 8 per kW h. In spite of its promising results, there are techno-economic bottlenecks in commercial scale production and its application at comparable costs to conventional fossil fuels. Issues such as high GHG emissions and high production costs have been determined to be resolved later. Overall, this work not only leads to GHG avoidance, but also diversifies energy supply in providing power for homes in the future.

  • Leveraging Generative AI Tools for UX Design in Lean and Agile Projects
    • Marcin Sikorski
    2024

    Recent advancements in Generative AI (GenAI) open new opportunities to improve User Experience (UX) practitioners’ efficiency in their projects. Due to intensive teamwork caused by time pressure and readiness for rapid changes, Lean and Agile project management seems particularly predestined for easy adoption of GenAI-supported UX design methods. However, precipitate and spontaneous leveraging of GenAI tools to UX design bears the risk that results may differ from what is expected and cause delays that harm a speedy IT project management. This paper identifies issues relevant to UX practitioners' dilemmas when considering GenAI tools for user interface projects, and proposes a fast-and-fugal decision-making framework for IT project managers and UX professionals on whether to use (or not) GenAI tools in Agile and Lean IT projects.

  • Light formed through urban morphology and different organism groups: First findings from a systematic review
    • Seren Dincel
    • Ute Besenecker
    • Daniel Koch
    • Karolina Zielińska-Dąbkowska
    2024 Full text IOP Conference Series: Earth and Environmental Science

    The prevailing implementation and usage of contemporary lighting technologies and design practices in cities have created over-illuminated built environments. Recent studies indicate that exposure to electric lighting effects formed through spatial characteristics has visual, physiological, and behavioural effects on both humans and non-humans, such as wildlife. In order to gain a better understanding of the impact that electric lighting has on space and different organism groups, a comprehensive literature review was conducted applying PRISMA 2020 systematic review guidelines. Results of the searches from various databases, such as Web of Science, PubMed and Scopus, identified 5260 related studies. A total of 55 papers connected to four themes: (1) urban morphology; (2) human visual impressions; (3) ecological impacts; and (4) design approaches and methods were analysed with a focus on urban morphology. The review provided the following general findings: lighting properties alone are inadequate to depict visual impressions of pedestrians, patterns formed through light interacting with spatial characteristics can contribute to understanding how spaces are visually perceived and help characterising the exposure of wildlife organisms to potential disturbances.

  • Lignocellulosic waste biosorbents infused with deep eutectic solvents for biogas desulfurization
    • Patrycja Makoś-Chełstowska
    • Dominika Sikorska
    • Patrycja Janicka
    • Edyta Słupek
    • Aleksandra Mielewczyk-Gryń
    • Jacek Gębicki
    2024 CHEMICAL ENGINEERING JOURNAL

    This paper introduces an innovative method for treating biogas streams, employing lignocellulosic biosorbents infused with environmentally friendly solvents known as deep eutectic solvents (DES). The primary focus of this study was the elimination of volatile organosulfur compounds (VSCs) from model biogas. Biosorbents, including energetic poplar wood, antipka tree, corncobs, and beech wood, were used, each with varying levels of lignin and hemicellulose content. The selection of the DES with the greatest potential for VSC removal was carried out using COnductor-like Screening MOdel for Realistic Solvents (COSMO-RS) modeling. The chosen DES consisted of quaternary ammonium salts and glycols, specifically, tetrapropylammonium bromide and 1,2-hexanediol (1:3). The physicochemical properties of the new DES, such as the viscosity, density, and melting point, were evaluated. The biosorbents were treated with the selected DES after shredding, purifying, and sieving. Comprehensive analysis techniques, including thermogravimetric analysis, scanning electron microscopy, and X-ray diffraction, were employed on the modified biosorbents both before and after modification. The subsequent step involved the adsorption of VSCs from biogas. The results of this study demonstrated the superior performance of a novel sorbent based on corn cob modified by DES compared to commercially available alternatives. The sorption capacity ranged from 103.8 to 112.1 mg/g for various VSCs. The adsorption process using the new biosorbent can be described by the pseudo second order kinetic model, as well as the Yoon-Nelson and Adams-Bohart models. The high efficacy of the VSCs removal was attributed to the concurrent operation of the absorption and adsorption processes. The resulting sorbent was also characterized by its ability to regenerate repeatedly without significant loss of sorption capacity of the new sorbents.

  • Limitation of Floating-Point Precision for Resource Constrained Neural Network Training
    • Mariusz Pietrołaj
    2024 Full text

    Insufficient availability of computational power and runtime memory is a major concern when it comes to experiments in the field of artificial intelligence. One of the promising solutions for this problem is an optimization of internal neural network’s calculations and its parameters’ representation. This work focuses on the mentioned issue by the application of neural network training with limited precision. Based on this research, the author proposes a new method of precision limitation for neural network training leveraging a custom, constrained floating-point representation with additional rounding mechanism. Its application allows to limit the resources required during neural network training thanks to the reduction of computational complexity and memory usage. The work shows that the proposed procedure allows to train commonly used benchmark networks such as LeNet, AlexNet and ResNet without significant accuracy degradation while using only 8-bit custom floating-point variables. It has also been proven that the proposed method of precision limitation does not negatively affect the network’s convergence, therefore, it is not required to extend the training by increasing the number of costly training epochs.

  • Limited dissolution of transition metals in the nanocrystalline cerium (IV) oxide
    • Agata Ducka
    • Patryk Błaszczak
    • Marcin Zajac
    • Adrian Mizera
    • Francesco d'Acapito
    • Beata Bochentyn
    2024 Full text CERAMICS INTERNATIONAL

    Nanocrystalline cerium (IV) oxides doped with transition metals have gained significant interest recently, mostly in the field of catalysis. Herein, we present the comprehensive studies on ceria doped with 10 mol.% of transition metals (Mn, Fe, Co, Ni or Cu) synthesized by the reverse microemulsion method. The aim of this work is to study the properties of those materials with the use of different complementary methods like XRD, SEM, TPR, and XPS and to determine the possibility of fabrication of single-phase materials with that doping level. Studies presented here prove that despite showing single-phase XRD patterns with high nanocrystallinity, in all obtained materials, the dopants are not fully incorporated in the ceria lattice. Spectroscopy studies show that additional transition metal oxides are present on the surface of all materials. Herein, we also present the analyses of L3,2-edges of transition metals in ceria, as well as high energy Ce K-edge to prove that 10 mol.% of any of those transition metals cannot be incorporated in the ceria host without the formation of additional phases. Using techniques presented here, it was found that the highest share of Mn can be dissolved in the lattice, while Cu is mostly present as a surficial Cu2O. Studies presented are an important contribution to the discussion about the solubility limits in nanocrystalline ceria and its properties which may be utilized for e.g various catalysts or as electrolytes.

  • Linear Time-Varying Dynamic-Algebraic Equations of Index One on Time Scales
    • Svetlin Georgiev
    • Sergey Kryzhevich
    2024

    In this paper, we introduce a class of linear time-varying dynamic-algebraic equations (LTVDAE) of tractability index one on ar- bitrary time scales. We propose a procedure for the decoupling of the considered class LTVDAE. Explicit formulae are written down both for transfer operator and the obtained decoupled system. A projector ap- proach is used to prove the main statement of the paper and sufficient conditions of decoupling are also written down explicitly.

  • Local basis function method for identification of nonstationary systems
    • Artur Gańcza
    2024 Full text

    This thesis is focused on the basis function method for the identification of nonstationary processes. The first chapter describes a group of models that can be identified using the basis function method. The next chapter describes the basic version of the basis function method, including its algebraic and statistical properties. The following section introduces the local basis function (LBF) method: its properties are described and similarities and differences between LBF and the basic basis function method are highlighted. The main difference lies in the approach to estimation. The primary version of the basis function method provides estimates for the entire analysis interval. The analysis window is then shifted so that estimates can be found for the next set of observations. In the case of the LBF method, the data from the analysis window are used to find parameter estimates for only one time instant within the analysis interval. The window is then moved to the subsequent observation and the estimation process is repeated. As a result, one obtains more accurate estimates at the expense of the increased computational burden.

  • Lokalne zarysowania w żelbetowych mostach sprężonych. Analiza wybranych problemów
    • Krzysztof Żółtowski
    • Przemysław Kalitowski
    2024 Materiały Budowlane

    Betonowe konstrukcje sprężone to struktury, w których wprowadza się w materiał naprężenia eliminujące lub w znacznym stopniu ograniczające w nim rozciąganie [1].W idealnie sprężonej konstrukcji nie występuje praca elementów żelbetowych w II fazie. Niestety tylko niektóre obecnie wznoszone konstrukcje mostowe spełniają ten warunek. Powszechnie stosuje się sprężenie podłużne, natomiast płytę jezdni i inne detale konstruuje jako żelbetowe. W ten sposób została zaprojektowana i wykonana większość najbardziej spektakularnych polskich konstrukcji mostowych. Doświadczenie zdobyte przy budowie i eksploatacji mostów sprężonych pokazuje, że praca w II fazie drugorzędnych elementów konstrukcyjnych nie stanowi problemu utrzymaniowego i nie wpływa na trwałość, ale każde lokalne zarysowanie dźwigara sprężonego, a przede wszystkim rysy i pęknięcia betonu w strefach zakotwień kabli sprężających, budzą niepokój i emocje.

  • Long-range, water-mediated interaction between a moderately active antifreeze protein molecule and the surface of ice
    • Joanna Grabowska
    • Anna Kuffel
    • Jan Zielkiewicz
    2024 JOURNAL OF CHEMICAL PHYSICS

    Using molecular dynamics simulations, we show that a molecule of moderately active antifreeze protein (type III AFP, QAE HPLC-12 isoform) is able to interact with ice in an indirect manner. This interaction occurs between the ice binding site (IBS) of the AFP III molecule and the surface of ice, and it is mediated by liquid water which separates these surfaces. As a result, the AFP III molecule positions itself at a specific orientation and distance relative to the surface of ice, which enables the effective binding (via hydrogen bonds) of the molecule with the nascent ice surface. Our results show that the final adsorption of the AFP III molecule on the surface of ice is not achieved by chaotic diffusion movements, but it is preceded by a remote, water-mediated interaction between the IBS and the surface of ice. The key factor that determines the existence of this interaction is the ability of water molecules to spontaneously form large, high-volume aggregates which can be anchored to both the IBS of the AFP molecule and the surface of ice. The results presented in this work for AFP III are in full agreement with the ones obtained by us previously for hyperactive CfAFP, which indicates that the mechanism of the remote interaction of these molecules with ice remains unchanged despite significant differences in the molecular structure of their ice binding sites. For that reason we can expect that also other types of AFPs interact with the ice surface according to an analogous mechanism.

  • Looking For Motivation. How to Keep Students’ Software Projects from Ending up on the Shelf?
    • Teresa Zawadzka
    • Michał Zawadzki
    • Agnieszka Landowska
    2024

    IT specialists in the business environment work in teams according to the established methodology and using the established toolkit. From the university’s point of view, preparing IT students to work in such an environment is a challenging task, as it requires either cooperation with business or the simulation of similar conditions in the university environment. Participation of students in real projects can provide them with the necessary practical skills. The aim of this paper is to present the experience gained in running real-life, long-term projects in academia, and to provide guidelines on how to involve students in running these projects to the benefit of students.

  • Looking through the past: better knowledge retention for generative replay in continual learning
    • Valeriya Khan
    • Sebastian Cygert
    • Kamil Deja
    • Tomasz Trzciński
    • Bartłomiej Twardowski
    2024 Full text IEEE Access

    In this work, we improve the generative replay in a continual learning setting to perform well on challenging scenarios. Because of the growing complexity of continual learning tasks, it is becoming more popular, to apply the generative replay technique in the feature space instead of image space. Nevertheless, such an approach does not come without limitations. In particular, we notice the degradation of the continually trained model’s performance could be attributed to the fact that the generated features are far from the original ones when mapped to the latent space. Therefore, we propose three modifications that mitigate these issues. More specifically, we incorporate the distillation in latent space between the current and previous models to reduce feature drift. Additionally, a latent matching for the reconstruction and original data is proposed to improve generated features alignment. Further, based on the observation that the reconstructions are better for preserving knowledge, we add the cycling of generations through the previously trained model to make them closer to the original data. Our method outperforms other generative replay methods in various scenarios. Code available at https://github.com/valeriya-khan/looking-through-the-past.

  • Losing influence: the Changing Role of the Merchant Community of Danzig in the Timber Value Chain (1919-1939)
    • Luciano Segreto
    2024 Full text Jahrbuch f. Wirtschaftsgeschichte/Economic History Yearbook

    The article highllghts the changing role of the merchant community of Danzig after the establishment of the Second Polish Republic and the Fee City of Danzig

  • Loss Minimization-Based Sensorless Control of High-Speed Induction Motor Considering Core Loss
    • Tadele Ayana
    • Piotr Kołodziejek
    • Marcin Morawiec
    • Lelisa Wogi
    2024 Full text IEEE Access

    This paper presents loss-minimizing sensorless control (LMC) strategies utilized to optimize the energy of high-speed induction motor (HSIM) drives. A machine’s ability to operate effectively depends on the estimation of its electrical losses. Although copper losses account for the majority of electrical losses in electrical machines, core loss also contributes a major part, particularly in high-speed induction motors. A review of design solutions of power electronic converters to feed HSIMs and the effect of their parameters on iron losses were analyzed. In the gathered literature, HSIM loss analysis was generally performed using software analytical techniques such as finite element methods. There were few real-time loss analysis and loss minimization sensorless control approaches for HSIM in the literature. Finally, the study of sensorless control of 500Hz frequency with synchronous speed of 15000 rpm HSIM with optimal flux and reference reactive torque based optimization for loss minimization through nonlinear control system design was presented as a solution to the evaluated gaps found in the literature and the simulation findings were experimentally verified.

  • Low temperature rotary Stirling engine: conceptual design and theoretical analysis
    • Jacek Kropiwnicki
    2024 APPLIED THERMAL ENGINEERING

    The use of low-temperature energy sources for electricity generation demands a dual focus: a substantial enhancement in the efficiency of energy conversion devices and a reduction in system production costs. Particularly in scenarios where low-temperature energy sources are scarce, this factor can be pivotal in facilitating widespread adoption of such technologies. The Stirling engine emerges as a promising solution capable of meeting these articulated expectations, owing to its straightforward design and utilization of non-toxic, non-flammable, and cost-effective working mediums. This paper introduces a novel concept of a rotary Stirling engine, exhibiting significant potential for operation with low-temperature energy sources. Additionally, an analytical model of the engine is presented, enabling simulations of its operation under varying supply temperatures and geometric configurations. To analyse the impact of internal leaks on the net efficiency and net power of the engine, a modified adiabatic model was introduced. It was observed that utilizing identical heat exchangers for heat supply at 250°C and 100°C could lead to a decline in net efficiency from 8% to 3% for the worst case. Furthermore, an analysis was performed to assess the impact of the heater's overall heat transfer coefficient and engine rotational speed on both net efficiency and net mechanical power for a heat supply temperature of 200°C.

  • Low-Barrier Hydrogen Bond Determines Target-Binding Affinity and Specificity of the Antitubercular Drug Bedaquiline
    • Joanna Słabońska
    • Subrahmanyam Sappati
    • Antoni Marciniak
    • Jacek Czub
    2024 ACS Medicinal Chemistry Letters

    The role of short strong hydrogen bonds (SSHB) in ligand-target binding remains largely unexplored, thereby hin- dering a potentially important avenue in the rational drug de- sign. Here, we investigate the interaction between bedaquiline (Bq), a potent anti-tuberculosis drug, and the mycobacterial ATP synthase, to unravel the role of a specific hydrogen bond to a conserved acidic residue in the target affinity and specificity. Our ab initio molecular dynamics simulations reveal that this bond belongs to the SSHB category and accounts for a substan- tial fraction of the target binding energy. We also demonstrate that the presence of an extra acidic residue (D32), found exclu- sively in mycobacteria, cooperatively enhances the HB strength ensuring the specificity for the mycobacterial target. Consis- tently, we show that the removal of D32 markedly weakens the affinity, leading to Bq resistance associated with mutations of D32 to non-acidic residues. By designing simple Bq analogs, we then explore the possibility to overcome the resistance and po- tentially broaden the Bq antimicrobial spectrum by making the SSHB independent on the presence of the extra acidic residue.

  • Low-Cost 3-D Printed Lens Antenna for Ka-Band Connectivity Applications
    • Kamil Trzebiatowski
    • Weronika Kalista
    • Mateusz Rzymowski
    • Łukasz Kulas
    • Krzysztof Nyka
    2024

    This paper discusses the use of low-cost 3-D printing technology to fabricate dielectric lenses for Ka-band wireless networks. A low-cost FDM alternative to previously presented 3-D printed lens in SLA technology with high performance resin is presented. The presented approach has been demonstrated for a 39 GHz MU-MIMO antenna array modified to realize multibeam or switched-beam antenna that can support demanding energy-efficient applications in millimeter waves. The impact of different 3-D printing settings on the lens performance is also investigated. The results demonstrate that with proper printing settings, low-cost 3-D printed lenses created using FDM process are a viable alternative for high-frequency applications.

  • Low-Cost and Precise Automated Re-Design of Antenna Structures Using Interleaved Geometry Scaling and Gradient-Based Optimization
    • Anna Pietrenko-Dąbrowska
    • Sławomir Kozieł
    2024 KNOWLEDGE-BASED SYSTEMS

    Design of contemporary antennas is an intricate endeavor involving multiple stages, among others, tuning of geometry parameters. In particular, re-designing antennas to different operating frequencies, makes parametric optimization imperative to ensure the best achievable system performance. If the center frequency at the current design is distant from the target one, local tuning methods generally fail, whereas global algorithms (e.g., nature-inspired procedures) incur prohibitive computational expenses, especially when antenna evaluation is performed using full-wave electromagnetic (EM) analysis. In this paper, a novel technique involving automated decision-making has been developed, whose main objective is low-cost and precise re-design of antenna structures over wide ranges of operating frequencies. The employed methodology involves knowledge-based simultaneous scaling of antenna dimensions and gradient-based performance improvements. The two stages are automatically interleaved, and embedded into an iterative optimization procedure. The problem-specific knowledge allows for carrying out the scaling phase, in which fast relocation of the center frequency of the antenna is performed, based on a single EM analysis of the structure. The gradient-based tuning phase enhances the design quality with regard to the assumed objectives. The process defaults to local optimization after the antenna center frequency becomes sufficiently close to the target. The main novelty of the proposed algorithm consists in development of an automated knowledge-based framework of quasi-global search capabilities linking brute-force scaling and design refinement. Our technique has been demonstrated with the use of three microstrip antennas, optimized for best matching and maximum in-band gain. The main findings are that for all structures, satisfactory designs have been identified despite poor starting points, with operating frequencies being away from the assumed targets. At the same time, the computational cost is comparable to conventional local search. The proposed approach is versatile, simple to implement and easy to handle, in particular, its control parameters do not require tailoring to a specific antenna structure at hand.

  • Low-Cost Method for Internal Surface Roughness Reduction of Additively Manufactured All-Metal Waveguide Components
    • Jakub Sorocki
    • Ilona Piekarz
    • Michał Baranowski
    • Adam Lamęcki
    • Alberto Cattenone
    • Stefania Marconi
    • Gianluca Alaimo
    • Nicolo Delmonte
    • Lorenzo Silvestri
    • Bozzi Maurizio
    2024 Full text IEEE TRANSACTIONS ON MICROWAVE THEORY AND TECHNIQUES

    In this study, a novel low-cost polishing method for internal surface roughness reduction of additively manufactured components, developed for waveguide (WG) circuits operating in the millimeter frequency range is proposed. WG components fabricated using powder bed fusion (PBF) generally feature roughness of ten to fifty microns, which influences the increase of roughness-related conductor power losses having a major effect on the electrical performance of additively manufactured allmetal WGs. To improve and decrease the surface roughness of circuits fabricated using PBF, glass microbeads as an abrasive medium are proposed to be used in combination with a rotary tumbler. This technique allows the abrasive medium to efficiently penetrate internal long channels and cavities, having cross section dimensions in the range of sub- to a few millimeters. An experimental study was carried out on an example of WG sections and bandpass filters fabricated using PBF through selective laser melting (SLM), operating within the 8.2 to 40 GHz range. Polishing impact on both mechanical and electrical properties was studied showing surface roughness reduction by 18% and sixth order filter’s insertion loss reduction at 23 GHz by 40% after 24 h of tumbling with 300–400 µm large glass microbeads.

  • Low-cost multiband four-port phased array antenna for sub-6 GHz 5G applications with enhanced gain methodology in Radio-over-fiber systems using modulation instability
    • Hassan Zakeri
    • Rasul Azizpour
    • Parsa Khoddami
    • Gholamreza Moradi
    • Mohammad Alibakhshikenari
    • Chan Hwang See
    • Tayeb Dendini
    • Francisco Falcone
    • Sławomir Kozieł
    • Ernesto Limiti
    2024 Full text IEEE Access

    Phased array antenna (PAA) technology is essential for applications requiring high gain and wide bandwidth, such as sensors, medical, and 5G. Achieving such a design, however, is a challenging and intricate process that calls for precise calculations and a combination of findings to alter the phase and amplitude of each unit. Furthermore, coupling effects between these PAA structure elements can only be completed with the use of full-wave electromagnetic simulation tools. Due to recent advances, radio-over-fiber (RoF) technology has been positioned as a possible alternative for high-capacity wireless communications. This paper presents a low-cost, multiband Sub-6 GHz 5G PAA with enhanced gain achieved through integration with a new specialized RoF system design to improve PAA performance by using the phenomenon of modulation instability (MI). Optimizing the antenna’s Defected Ground Structure (DGS) leads to even more improvement. To enable operation across three distinct frequency bands (Sub6 GHz n78 band (3-3.8 GHz), n79 band (3.8-5 GHz), and n46 band (5-5.5 GHz)), the proposed antenna design features four elliptical patches strategically positioned at the four sides of the ground plane, providing comprehensive 360° coverage in the azimuth plane. Additionally, integrating elliptical slots and upper gaps contributes to improvement. The proposed PAA’s experimentally validated gain values are 5.2 dB, 7.4 dB, and 7.8 dB in the n78, n79, and n46 bands, respectively. For improving the performance of the proposed PAA in RoF systems, anomalous fibers (n2 ̸= 0 and β2 < 0) are employed to consider the modulation instability (MI) phenomenon, which can lead to the generation of the MI gain on the carrier sideband. The true time delay (TTD) technique controls the beam pattern by adjusting the time delay between adjacent radiation elements. Furthermore, the TTD technique utilizes frequency combs for the proposed 4-element array antenna to apply MI gain to all antenna elements.

  • Low-Loss 3D-Printed Waveguide Filters Based on Deformed Dual-Mode Cavity Resonators
    • Michał Baranowski
    • Łukasz Balewski
    • Adam Lamęcki
    • Michał Mrozowski
    2024 Full text IEEE Access

    This paper introduces a new type of waveguide filter with smooth profile, based on specially designed dual-mode (DM) cavity resonators. The DM cavity design is achieved by applying a shape deformation scheme. The coupling between the two orthogonal cavity modes is implemented by breaking the symmetry of the structure, thus eliminating the need for additional coupling elements. The modes operating in the cavity are carefully analyzed and a scheme for managing the spurious modes is discussed. Two filter prototypes employing the designed DM cavities are developed and described in detail. The first design is a fourth-order bandpass filter (BPF) with a 90◦ rotated output and a transmission zero (TZ), whereas the second design is an eighth-order filter with four TZs. Both designs are developed, taking into account the limitations of 3D printing technology to enable their single-piece fabrication without internal supports. The structures benefit from additive manufacturing (AM) by having a smooth surface profile and reduced weight, which is often highly desirable for high-power and low-loss applications. Filter prototypes were manufactured using selective laser melting (SLM) from aluminum alloy and tested to validate the designs. Measurement results are consistent with the simulation and prove the validity of the proposed solutions. Both measured BPF prototypes demonstrate low insertion loss, i.e., 0.11 dB and 0.25 dB for the fourth-order and eighthorder designs, respectively. The estimated Q-factors reach 3500 and 4500, which is a very good result for 3D-printed parts.

  • LSA Is not Dead: Improving Results of Domain-Specific Information Retrieval System Using Stack Overflow Questions Tags
    • Szymon Olewniczak
    • Julian Szymański
    • Piotr Malak
    • Robert Komar
    • Agnieszka Letowska
    2024 Full text

    The paper presents the approach to using tags from Stack Overflow questions as a data source in the process of building domain-specific unsupervised term embeddings. Using a huge dataset of Stack Overflow posts, our solution employs the LSA algorithm to learn latent representations of information technology terms. The paper also presents the Teamy.ai system, currently developed by Scalac company, which serves as a platform that helps match IT project inquiries with potential candidates. The heart of the system is the information retrieval module that searches for the best-matching candidates according to the project requirements. In the paper, we used our pre-trained embeddings to enhance the search queries using the query expansion algorithm from the neural information retrieval domain. The proposed solution improves the precision of the retrieval compared to the basic variant without query expansion.

  • Łukowy wiadukt Pomorskiej Kolei Metropolitalnej w Gdańsku. Założenia projektowe i stan techniczny po 10 latach eksploatacji
    • Krzysztof Żółtowski
    • Przemysław Kalitowski
    • Mikołaj Binczyk
    • Tomasz Romaszkiewicz
    2024 Mosty

    Artykuł przedstawia historię, projektowanie i ocenę techniczną po 10 latach eksploatacji wiaduktu WK11 w Gdańsku, będącego częścią Pomorskiej Kolei Metropolitalnej. Opisuje proces budowy i wyzwania związane z rekonstrukcją historycznego mostu z 1914 roku, który został zniszczony podczas II wojny światowej. Autorzy szczegółowo analizują koncepcje projektowe, w tym rozważania nad schematem statycznym i zastosowanym materiałem. W artykule zawarto wyniki zaawansowanych analiz numerycznych, które doprowadziły do ostatecznego kształtu wiaduktu. Po 10 latach użytkowania wiadukt WK11 nadal jest w dobrym stanie technicznym, a jedyne zauważalne ślady zużycia to drobne rysy skurczowe. Konstrukcja działa zgodnie z założeniami projektowymi.

  • Machine learning approach to packaging compatibility testing in the new product development process
    • Norbert Piotrowski
    2024 Full text JOURNAL OF INTELLIGENT MANUFACTURING

    The paper compares the effectiveness of selected machine learning methods as modelling tools supporting the selection of a packaging type in new product development process. The main goal of the developed model is to reduce the risk of failure in compatibility tests which are preformed to ensure safety, durability, and efficacy of the finished product for the entire period of its shelf life and consumer use. This kind of testing is mandatory inter alia for all aerosol packaging as any mechanical alterations of the packaging can cause the pressurized product to unseal and stop working properly. Moreover, aerosol products are classified as dangerous goods and any leaking of the product or propellent can be a serious hazard to the storage place, environment, and final consumer. Thus, basic compatibility observations of metal aerosol packaging (i.e. general corrosion, pitting corrosion, coating blistering or detinning) and different compatibility factors (e.g. formula ingredients, water contamination, pH, package material and coatings) were discussed. Artificial intelligence methods applied in the design process can reduce the lengthy testing time as well as developing costs and help benefit from the knowledge and experience of technologists stored in historical data in databases.

  • Machine learning for the management of biochar yield and properties of biomass sources for sustainable energy
    • Giao Van Nguyen
    • Prabhakar Sharma
    • Ümit Ağbulut
    • Huu Son Le
    • Thanh Hai Truong
    • Marek Dzida
    • Minh Ho Tran
    • Huu Cuong Le
    • Viet Dung Tran
    2024 Biofuels Bioproducts & Biorefining-Biofpr

    Biochar is emerging as a potential solution for biomass conversion to meet the ever increasing demand for sustainable energy. Efficient management systems are needed in order to exploit fully the potential of biochar. Modern machine learning (ML) techniques, and in particular ensemble approaches and explainable AI methods, are valuable for forecasting the properties and efficiency of biochar properly. Machine-learning-based forecasts, optimization, and feature selection are critical for improving biomass management techniques. In this research, we explore the influences of these techniques on the accurate forecasting of biochar yield and properties for a range of biomass sources. We emphasize the importance of the interpretability of a model, as this improves human comprehension and trust in ML predictions. Sensitivity analysis is shown to be an effective technique for finding crucial biomass characteristics that influence the synthesis of biochar. Precision prognostics have far-reaching ramifications, influencing industries such as biomass logistics, conversion technologies, and the successful use of biomass as renewable energy. These advances can make a substantial contribution to a greener future and can encourage the development of a circular biobased economy. This work emphasizes the importance of using sophisticated data-driven methodologies such as ML in biochar synthesis, to usher in ecologically friendly energy solutions. These breakthroughs hold the key to a more sustainable and environmentally friendly future.

  • Machine Learning-Based Wetland Vulnerability Assessment in the Sindh Province Ramsar Site Using Remote Sensing Data
    • Rana Waqar Aslam
    • Hong Shu
    • Iram Naz
    • Abdul Quddoos
    • Andaleeb Yaseen
    • Khansa Gulshad
    • Saad Saud Alarifi
    2024 Full text Remote Sensing

    Wetlands provide vital ecological and socioeconomic services but face escalating pressures worldwide. This study undertakes an integrated spatiotemporal assessment of the multifaceted vulnerabilities shaping Khinjhir Lake, an ecologically significant wetland ecosystem in Pakistan, using advanced geospatial and machine learning techniques. Multi-temporal optical remote sensing data from 2000 to 2020 was analyzed through spectral water indices, land cover classification, change detection and risk mapping to examine moisture variability, land cover modifications, area changes and proximity-based threats over two decades. The random forest algorithm attained the highest accuracy (89.5%) for land cover classification based on rigorous k-fold cross-validation, with a training accuracy of 91.2% and a testing accuracy of 87.3%. This demonstrates the model’s effectiveness and robustness for wetland vulnerability modeling in the study area, showing 11% shrinkage in open water bodies since 2000. Inventory risk zoning revealed 30% of present-day wetland areas under moderate to high vulnerability. The cellular automata–Markov (CA–Markov) model predicted continued long-term declines driven by swelling anthropogenic pressures like the 29 million population growth surrounding Khinjhir Lake. The research demonstrates the effectiveness of integrating satellite data analytics, machine learning algorithms and spatial modeling to generate actionable insights into wetland vulnerability to guide conservation planning. The findings provide a robust baseline to inform policies aimed at ensuring the health and sustainable management and conservation of Khinjhir Lake wetlands in the face of escalating human and climatic pressures that threaten the ecological health and functioning of these vital ecosystems.

  • Machine-learning methods for estimating compressive strength of high-performance alkali-activated concrete
    • Torkan Shafighfard
    • Farzin Kazemi
    • Neda Asgarkhani
    • Doo-Yeol Yoo
    2024 ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE

    High-performance alkali-activated concrete (HP-AAC) is acknowledged as a cementless and environmentally friendly material. It has recently received a substantial amount of interest not only due to the potential it has for being used instead of ordinary concrete but also owing to the concerns associated with climate change, sustainability, reduction of CO2 emissions, and energy consumption. The characteristics and amounts of the ingredients used to produce HP-AAC influence its compressive strength. This study performs a comparative analysis based on machine learning (ML) algorithms to present an ensemble model capable of predicting the compressive strength of HP-AAC. This is in response to the development of sophisticated prediction approaches that seek to lower the cost of experimental tools and labor. An extensive framework including 538 experimental datasets with 18 input parameters are extracted. In addition, stacked ML (SM) models are developed to provide their best base estimator combination with the highest capability. The results show that stacked model (SM-5) with score of 14, and prediction accuracy of 98% following by the largest experiment-to-predicted ratio, provide the best estimations of compressive strength of HP-AAC, which has the lowest error values compare to other 18 ML models. Thereafter, a graphical user interface (GUI) is provided and validated by extra experimental tests for estimating the compressive strength, cost, and carbon emission of HP-AAC. Overall, the significance of the current study highlight the outstanding performance of developed stacked ML and GUI for predicting the compressive strength of HP-ACC, which contribute for the on-going research in this area.

  • Machine-Learning Methods for Estimating Performance of Structural Concrete Members Reinforced with Fiber-Reinforced Polymers
    • Farzin Kazemi
    • Neda Asgarkhani
    • Torkan Shafighfard
    • Robert Jankowski
    • Doo-Yeol Yoo
    2024 ARCHIVES OF COMPUTATIONAL METHODS IN ENGINEERING

    In recent years, fiber-reinforced polymers (FRP) in reinforced concrete (RC) members have gained significant attention due to their exceptional properties, including lightweight construction, high specific strength, and stiffness. These attributes have found application in structures, infrastructures, wind power equipment, and various advanced civil products. However, the production process and the extensive testing required for assessing their suitability incur significant time and cost. The emergence of Industry 4.0 has presented opportunities to address these drawbacks by leveraging machine learning (ML) methods. ML techniques have recently been used to forecast the properties and assess the importance of process parameters for efficient structural design and their broad applications. Given their wide range of applications, this work aims to perform a comprehensive analysis of ML algorithms used for predicting the mechanical properties of FRPs. The performance evaluation of various models was discussed, and a detailed analysis of their pros and cons was provided. Finally, the limitations that currently exist in these techniques were pinpointed, and suggestions were given to improve their prediction precision suitable for evaluating the mechanical properties of FRP components.

  • Machine-Learning-Based Global Optimization of Microwave Passives with Variable-Fidelity EM Models and Response Features
    • Sławomir Kozieł
    • Anna Pietrenko-Dąbrowska
    2024 Full text Scientific Reports

    Maximizing microwave passive component performance demands precise parameter tuning, particularly as modern circuits grow increasingly intricate. Yet, achieving this often requires a comprehensive approach due to their complex geometries and miniaturized structures. However, the computational burden of optimizing these components via full-wave electromagnetic (EM) simulations is substantial. EM analysis remains crucial for circuit reliability, but the expense of conducting rudimentary EM-driven global optimization by means of popular bio-inspired algorithms is impractical. Similarly, nonlinear system characteristics pose challenges for surrogate-assisted methods. This paper introduces an innovative technique leveraging variable-fidelity EM simulations and response feature technology within a kriging-based machine-learning framework for cost-effective global parameter tuning of microwave passives. The efficiency of this approach stems from performing most operations at the low-fidelity simulation level and regularizing the objective function landscape through the response feature method. The primary prediction tool is a co-kriging surrogate, while a particle swarm optimizer, guided by predicted objective function improvements, handles the search process. Rigorous validation demonstrates the proposed framework's competitive efficacy in design quality and computational cost, typically requiring only sixty high-fidelity EM analyses, juxtaposed with various state-of-the-art benchmark methods. These benchmarks encompass nature-inspired algorithms, gradient search, and machine learning techniques directly interacting with the circuit's frequency characteristics.

  • Machine-learning-based precise cost-efficient NO2 sensor calibration by means of time series matching and global data pre-processing
    • Sławomir Kozieł
    • Anna Pietrenko-Dąbrowska
    • Marek Wójcikowski
    • Bogdan Pankiewicz
    2024 Full text Engineering Science and Technology-An International Journal-JESTECH

    Air pollution remains a considerable contemporary challenge affecting life quality, the environment, and economic well-being. It encompasses an array of pollutants—gases, particulate matter, biological molecules—emanating from sources such as vehicle emissions, industrial activities, agriculture, and natural occurrences. Nitrogen dioxide (NO2), a harmful gas, is particularly abundant in densely populated urban areas. Given its detrimental impact on health and the environment, precise monitoring of NO2 levels is crucial for devising effective strategies to mitigate risks. However, precise measurement of NO2 presents challenges as it traditionally relies on expensive and heavy (therefore, stationary) equipment. This has led to the pursuit of more affordable alternatives, though their dependability is frequently questionable. This study introduces an innovative technique for precise calibration of low-cost NO2 sensors. Our methodology involves statistical preprocessing of sensor measurements to align their distributions with reference data. The core of the calibration model is an artificial neural network (ANN), trained to synchronize sensor and reference time series measurements. It incorporates environmental variables such as temperature, humidity, and atmospheric pressure, along with readings from redundant NO2 sensors for cross-referencing, and short time series of primary sensor NO2 measurements. This enables efficient learning of typical sensor changes over time in relation to these factors. Additionally, an interpolative kriging model serves as an auxiliary surrogate to enhance the correction process's reliability. Validation using an autonomous monitoring platform from Gdansk University of Technology, Poland, and public reference station data gathered over five months shows remarkable calibration accuracy, with a correlation coefficient close to 0.95 and RMSE of 2.4 µg/m3. These results position the corrected sensor as an attractive and cost-effective alternative to conventional NO2 measurement methods.

  • Macrocyclic derivatives of imidazole as chromoionophores for bismuth(III)/lead(II) pair
    • Błażej Galiński
    • Ewa Wagner-Wysiecka
    2024 Full text SENSORS AND ACTUATORS B-CHEMICAL

    18-membered diazomacrocycles with imidazole or 4-methylimidazole residue as a part of macrocycle were used as chromoionophores in bismuth(III) and lead(II) dual selective optodes for the first time. Cellulose triacetate membranes doped with macrocyclic chromoionophores are bismuth(III) and lead(II) selective with color change from orange/red to different shades of blue and violet, respectively. Results obtained for model and real samples of bismuth(III) and lead(II) showed that easily accessible and regenerable sensor materials can be used for spectrophotometric and colorimetric detection and determination of bismuth(III) and lead(II). The obtained LOD values for bismuth(III) are 1.63×10-7 M and 3.03×10-7 M with spectrophotometric and colorimetric detection, respectively, when using optode with imidazole residue. For sensing material with 4-methylimidazole in macroring the lowest detection limits were obtained for lead(II): 2.14×10-7 M and 3.99×10-7 M with spectrophotometric and digital color analysis detection mode, respectively.