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Gdańsk University of Technology

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  • A review on homogeneous and heterogeneous catalytic microalgal lipid extraction and transesterification for biofuel production
    • Vinoth Kumar Ponnumsamy
    • Hussein Al-Hazmi
    • Sutha Shobana
    • Jeyaprakash Dharmaraja
    • Dipak Ashok Jadhav
    • Rajesh Banu J
    • Grzegorz Piechota
    • Bartłomiej Igliński
    • Vinod Kumar
    • Amit Bhatnagar
    • Kyu-Jung Chae
    • Gopalakrishnan Kumar
    2024 Full text CHINESE JOURNAL OF CATALYSIS

    Extracting lipids from microalgal biomass presents significant potential as a cost-effective approach for clean energy generation. This can be achieved through the chemical conversion of lipids to produce fatty acid methyl esters via transesterification. The extraction mainly involves free fatty acids, phospholipids, and triglycerides, and requires less energy, making it an attractive option for satisfying the growing demand for fossil-derived energies. Several approaches have been explored for sustainable bioenergy production from microalgal species via catalytic, non-catalytic, and enzymatic transesterification. This review discusses recent insights into microalgal lipid extraction via solvent, Soxhlet, Bligh and Dyer’s, supercritical CO2, and ionic liquids solvent methods and lipid conversion by transesterification and homo/heterogeneous acid/base catalyzed, enzymatic, non-catalytic, and mechanically/chemically catalyzed in-situ techniques towards algal bioenergy production. Technical advances in both extraction and conversion are necessary for the commercialization of renewable energy sources.

  • A Review on Metal–Organic Framework as a Promising Catalyst for Biodiesel Production ENERGY & FUELS
    • Giao Van Nguyen
    • Prabhakar Sharma
    • Marek Dzida
    • Van Hung Bui
    • Huu Son Le
    • Ahmed Shabana El-Shafay
    • Huu Cuong Le
    • Duc Trong Nguyen Le
    • Viet Dung Tran
    2024 ENERGY & FUELS

    The rapid depletion of fossil-derived fuels along with rising environmental pollution have motivated academics and manufacturers to pursue more environmentally friendly and sustainable energy options in today’s globe. Biodiesel has developed as an ecologically favorable alternative. However, the mass manufacturing of biodiesel on an industrial scale confronts substantial cost and pricing challenges. To address this issue, high-efficiency catalysts with a large number of active sites are needed, resulting in increased biodiesel output and quality. Metal–organic frameworks (MOFs) have received a lot of interest as a catalyst for converting oils/fats or fatty acids into biodiesel. MOFs are polyporous materials that can alter pore size as well as topological structure. They serve as a versatile foundation for designing active sites to satisfy the unique needs of catalytic reactions and conversion pathways. The purpose of this current work is to shed light on the underlying mechanisms and essential properties of MOF-based catalysts used in biodiesel synthesis. In addition, several methods for connecting active sites inside MOFs are scrutinized, while the properties and usability of MOF-based catalysts for the biodiesel production process are completely compared to other catalysts. More importantly, limits and future research directions about the utilization of MOFs in the biodiesel synthesis route are also critically presented. In general, this review contributes to improved awareness about the potential of MOFs in the biodiesel production sector by investigating the primary mechanism and characteristics of MOF-based catalysts.

  • A Review: Structural Shape and Stress Control Techniques and their Applications
    • Ahmed Manguri
    • Najmadeen Saeed
    • Robert Jankowski
    2024 Full text ARCHIVES OF COMPUTATIONAL METHODS IN ENGINEERING

    This review article presents prior studies on controlling shape and stress in flexible structures. The study offers a comprehensive survey of literature concerning the adjustment and regulation of shape, stress, or both in structures and emphasizes such control’s importance. The control of systems is classified into three primary classes: nodal movement control, axial force control, and controlling the two classes concurrently. Each class is thoroughly assessed, showcasing diverse methods anticipated by various scholars. Furthermore, the paper discusses methods to reduce the number of devices (actuators) to adjust and optimize actuators’ placement to achieve optimal structural control, considering the cost implications of numerous actuators. Additionally, various actuators are presented in detail, their advantages and disadvantages are also discussed. Moreover, the applications of the presented techniques are reviewed in detail, the essential recommendations for future work are also suggested.

  • A Robust Random Forest Model for Classifying the Severity of Partial Discharges in Dielectrics
    • Sayed Mohammad Kameli
    • Abdelaziz Abuelrub
    • Mohammad AlShaikh Saleh
    • Shady S. Refaat
    • Marek Olesz
    • Jarosław Guziński
    2024

    Partial Discharges (PDs) are a common source of degradation in electrical assets. It is essential that the extent of the deterioration level of insulating medium is correctly identified, to optimize maintenance schedules and prevent abrupt power outages. Temporal PD signals received from damaged insulation, collected through the IEC-60270 method is the gold standard for PD detection. Temporal signals may be transformed to the frequency domain, introducing new spectral features that may be beneficial in certain circumstances. Consequently, time delays are introduced, due to the high utilization of computational resources within the signal processing pipeline. Moreover, some microprocessors struggle with the excess computational burden demanded by resource-heavy mathematical transformations. To rectify these issues, an alternative approach is utilized, where Machine learning (ML) algorithms are directly used for the classification of PD severity. Cylindrically-shaped air cavities with lengths ranging from 1mm–6mm are introduced to a resin-based polyethylene terephthalate (PET) insulation material. The cavities are partitioned based on size, to obtain different classes of PD severity. A comparative analysis is performed on various ML algorithms, to determine which algorithm correctly determined the severity of PDs, with highest efficacy. Random Forest was determined to be the most performant, with an accuracy of 98.33%. The high performance illustrates the model’s potential success in accurately determining the hazard level of PDs in real-time, based on merely time-domain signals.

  • A Selection of Starting Points for Iterative Position Estimation Algorithms Using Feedforward Neural Networks
    • Jarosław Sadowski
    • Jacek Stefański
    2024 Full text SENSORS

    This article proposes the use of a feedforward neural network (FNN) to select the starting point for the first iteration in well-known iterative location estimation algorithms, with the research objective of finding the minimum size of a neural network that allows iterative position estimation algorithms to converge in an example positioning network. The selected algorithms for iterative position estimation, the structure of the neural network and how the FNN is used in 2D and 3D position estimation process are presented. The most important results of the work are the parameters of various FNN network structures that resulted in a 100% probability of convergence of iterative position estimation algorithms in the exemplary TDoA positioning network, as well as the average and maximum number of iterations, which can give a general idea about the effectiveness of using neural networks to support the position estimation process. In all simulated scenarios, simple networks with a single hidden layer containing a dozen non-linear neurons turned out to be sufficient to solve the convergence problem.

  • A simple and efficient hybrid discretization approach to alleviate membrane locking in isogeometric thin shells
    • Roger Sauer
    • Zhihui Zou
    • T.j.r. Hughes
    2024 COMPUTER METHODS IN APPLIED MECHANICS AND ENGINEERING

    This work presents a new hybrid discretization approach to alleviate membrane locking in isogeometric finite element formulations for Kirchhoff–Love shells. The approach is simple, and requires no additional dofs and no static condensation. It does not increase the bandwidth of the tangent matrix and is effective for both linear and nonlinear problems. It combines isogeometric surface discretizations with classical Lagrange-based surface discretizations, and can thus be run with existing isogeometric finite element codes. Also, the stresses can be recovered straightforwardly. The effectiveness of the proposed approach in alleviating, if not eliminating, membrane locking is demonstrated through the rigorous study of the convergence behavior of several classical benchmark problems. Accuracy gains are particularly large in the membrane stresses. The approach is formulated here for quadratic NURBS, but an extension to other discretization types can be anticipated. The same applies to other constraints and associated locking phenomena.

  • A simplified channel estimation procedure for NB-IoT downlink
    • Jarosław Magiera
    2024 Full text

    This paper presents a low-complexity channel estimation procedure which is suitable for use in energy-efficient NB-IoT user equipment devices. The procedure is based on the well-established least squares scheme, followed by linear interpolation in the time domain and averaging in the frequency domain. The quality of channel estimation vs. signal-to-noise ratio is evaluated for two channel models and compared with the performance of channel estimation function implemented in the Matlab LTE Toolbox. The computational complexities of both implementations are assessed by measuring the average processing times required to obtain channel estimates for a given number of consecutive downlink frames. The results indicate that the proposed method provides a similar quality of channel estimation with considerably shorter processing time compared to its counterpart.

  • A Simplified SPWM Scheme for a Compact 3-Level Dual-Output Inverter
    • Charles Odeh
    • Arkadiusz Lewicki
    • Marcin Morawiec
    2024 IEEE TRANSACTIONS ON CIRCUITS AND SYSTEMS II-EXPRESS BRIEFS

    Sinusoidal Pulse-width modulation, SPWM, is inverter-leg-based, logical-operation-based, and lesscomputational-intensive. In this brief, these simplifying features of SPWM are extended to the control of the 10-switch, 3-level inverter. The control strategy is based on the single triangular carrier SPWM perspective. Procedures and details of the sharing of four-common power switches by the 3 inverter legs are given. Classically, a 2-wing, 20-switch dual-output inverter, DOI, is configured; which topologically provides flexibility of independent operation of the constituting inverter wings at reduced component-count. This independent operation entails each of the inverter retaining full modulation index operational range (0 to unity) for the same or different frequencies of operation. Experimental demonstrations of the proposed SPWM scheme in the control of the 2-wing, 20-switch DOI are adequately presented.

  • A stochastic approach for the solution of single and multi – objective optimisation problems of biological processes in sequencing batch reactor
    • Tomasz Ujazdowski
    • Robert Piotrowski
    • Michał Banach
    2024 JOURNAL OF PROCESS CONTROL

    This paper investigates the impact of implementing single and multi-optimisation solutions on the biological treatment process in a sequencing batch reactor (SBR). The research is based on a case study of the water resource recovery facility (WRRF) in Swarzewo, Northern Poland. The paper introduces the adaptive extremum seeking control (ESC) method for dissolved oxygen (DO) concentration control and places it in a layered control structure. Further, it presents the introduction of an optimisation layer for the structure and parameters of the SBR cycle, through the synthesis of stochastic methods: single-objective optimisation (SOO) using a genetic algorithm (GA) and multi-objective optimisation (MOO) using the NSGA-II algorithm. The results were compared to a classical approach with fixed cycle parameters. The paper shows the advantages of optimising cycle parameters, including the number of phases as well as the DO value, on the process flow. These control structures underwent simulation tests in the MATLAB environment with the Simba package. The biochemical processes occurring in the reactor are based on the Activated Sludge Model No. 2d (ASM2d). The optimising control system demonstrates tangible improvements in operational efficiency and significant reductions in electrical energy consumption, highlighting the effectiveness of the proposed methodologies. © 2017 Elsevier Inc. All rights reserved.

  • A study of cavitation erosion resistance of an ion nitrided titanium alloy by means of the vibration and rotating disk methods
    • V.a. Safonov
    • Anna Zykova
    • Janusz Steller
    • Marek Szkodo
    • Grzegorz Gajowiec
    • Jarosław Chmiel
    • A. Varhoshkov
    2024 Full text Journal of Physics : Conference Series

    In this work, a comparative analysis of the cavitation erosion resistance of the Ti– 6.7Al–2.5Mo–1.8Cr–0.5Fe–0.25Si alloy (known as brand VT3-1) before and after lowtemperature ion nitriding is carried out. Two test methods were applied, using vibrative (ASTM G-32) and rotating disk rigs, respectively. The kinetic dependences of the erosive destruction of samples of titanium alloy VT3-1 in the initial state, after ion nitriding and stainless steel AISI 321 were obtained. A study of the microstructure, hardness and surface morphology was carried out. The samples were examined using optical and scanning electron microscopy. The relationship between mass loss and cavitation erosion duration was experimentally determined and analyzed. The erosion rate on a rotating disk stand is much higher than during vibration tests. A large cavitation load gradient provides additional opportunities for analyzing the durability of materials and coatings using this method.

  • A systematic review on cellular responses of Escherichia coli to nonthermal electromagnetic irradiation
    • Khadijeh Askaripour
    • Arkadiusz Żak
    2024 BIOELECTROMAGNETICS

    Investigation of Escherichia coli under electromagnetic fields is of significance in human studies owing to its short doubling time and human‐like DNA mechanisms. The present review aims to systematically evaluate the literature to conclude causality between 0 and 300 GHz electromagnetic fields and biological effects in E. coli. To that end, the OHAT methodology and risk of bias tool were employed. Exponentially growing cells exposed for over 30 min at temperatures up to 37C with fluctuations below 1C were included from the Web‐of‐Knowledge, PubMed, or EMF‐Portal databases. Out of 904 records identified, 25 articles satisfied the selection criteria, with four excluded during internal validation. These articles examined cell growth (11 studies), morphology (three studies), and gene regulation (11 studies). Most experiments (85%) in the included studies focused on the extremely low‐frequency (ELF) range, with 60% specifically at 50 Hz. Changes in growth rate were observed in 74% of ELF experiments and 71% of radio frequency (RF) experiments. Additionally, 80% of ELF experiments showed morphology changes, while gene expression changes were seen in 33% (ELF) and 50% (RF) experiments. Due to the limited number of studies, especially in the intermediate frequency and RF ranges, establishing correlations between EMF exposure and biological effects on E. coli is not possible.

  • A tool for designing water tanks for measuring hydroacoustic transducers
    • Roman Salamon
    • Jacek Marszal
    • Iwona Kochańska
    2024 Full text Vibrations in Physical Systems

    Special water tanks are commonly used to measure the parameters of underwater acoustic systems. They must meet specific requirements, the fulfilment of which ensures very small but acceptable measurement errors. These requirements define the size of the tank and its shape as well as the strong attenuation of reflected waves. At the design stage, it is necessary to determine the impact of the tank structure on the measurement errors and to adapt it to the expected measurement methodology. The article presents a mathematical tool for designing such water tanks using the impulse response method. Contrary to the use of this method in architectural design, the presented method is here used to determine the measurement signals emitted by ultrasonic transmitting transducers and received by receiving transducers. The relationships are given between the parameters of the impulse response and the design parameters of the tank and the measurement system, as well as its transfer functions and sample measurement signals.

  • A total scoring system and software for complex modified GAPI (ComplexMoGAPI) application in the assessment of method greenness
    • Fotouh R. Mansour
    • Khalid M. Omer
    • Justyna Płotka-Wasylka
    2024 Full text Green Analytical Chemistry

    Evaluating analytical methods with innovative metrics is essential to ensure the effectiveness of analytical procedures. Various approaches have been proposed to assess the performance of an analytical method and its environmental consequences, as sustainable environment and green chemistry ideology are of high importance nowadays. Considering greenness evaluation of developed analytical procedures, Green Analytical Procedure Index (GAPI), one of these metrics, utilizes five distinct colored pentagons to evaluate the environmental foot- print of the analytical process at different stages. An additional tool named Complementary Green Analytical Procedure Index (ComplexGAPI) was introduced to expand on GAPI by adding additional fields pertaining to the processes performed prior to the analytical procedure itself. Nevertheless, the existing ComplexGAPI lacks a comprehensive scoring system for individual methods, which would allow for even easier comparison of pro- cedures using this tool. In response to queries from ComplexGAPI users, this study introduces a refined tool named ComplexMoGAPI, merging the visual appeal of ComplexGAPI with precise total scores. The accompa- nying software streamlines the application, facilitating quicker and simpler evaluations. This software is avail- able as an open source on bit.ly/ComplexMoGAPI. We believe that, following ComplexGAPI success, this ComplexMoGAPI tool will also gain attention and eventually trust and acceptance from the chemical community.

  • Absorbing Boundary Conditions Derived Based on Pauli Matrices Algebra
    • Tomasz Stefański
    • Jacek Gulgowski
    • Kosmas L. Tsakmakidis
    2024 IEEE Antennas and Wireless Propagation Letters

    In this letter, we demonstrate that a set of absorbing boundary conditions (ABCs) for numerical simulations of waves, proposed originally by Engquist and Majda and later generalized by Trefethen and Halpern, can alternatively be derived with the use of Pauli matrices algebra. Hence a novel approach to the derivation of one-way wave equations in electromagnetics is proposed. That is, the classical wave equation can be factorized into two two-dimensional wave equations with first-order time derivatives. Then, using suitable approximations, not only Engquist and Majda ABCs can be obtained, but also generalized ABCs proposed by Trefethen and Halpern, which are applicable to simulations of radiation problems.

  • Accessibility to urban green spaces: A critical review of WHO recommendations in the light of tree-covered areas assessment
    • Patrycja Przewoźna
    • Adam Inglot
    • Marcin Mielewczyk
    • Krzysztof Mączka
    • Piotr Matczak
    2024 ECOLOGICAL INDICATORS

    Easy accessibility of Urban Green Spaces (UGSs) is essential to the quality of life in urban areas. The World Health Organization (WHO) recommendations focus on spatial access to UGSs, define as accessible those larger than 0.5 ha situated up to 300 m of residential areas, and disregard the social significance of smaller green spaces. This paper assesses the extent to which the WHO recommendations permit the identification of locations for tree-covered UGSs that serve urban residents. The study uses geo-questionnaire to collect data on residents’ perception of the ecosystem services (ESs) provided by trees in both small-sized (<0.5 ha) and larger (≥0.5 ha) UGSs in two Polish cities, Poznań and Gdańsk. Three factors impacting the social perception of UGS accessibility were controlled: (a) distance to trees (influencing reaching it by walk), (b) age, (c) the ESs provided by trees in both sizes of UGSs. The minority, i.e. 26 % of respondents valued trees in the larger UGSs. Regulating ecosystem services appeared significant there (mainly impact on health and well-being) and cultural ecosystem services, such as recreation. Most of the treecovered areas residents’ identified as significant were small-sized UGSs within the median distance of 150 m and were related to trees in residential areas and along the roadsides. Cultural ecosystem service − a sense of intimacy, separating from the neighbors, was specifically valued there. Age does not appear to be a major determinant of social perspectives on trees in UGSs. The results suggest that smallsized tree-covered UGSs are essential to residents and should be included in policies on urban greenery. In addition, the research findings indicate that identifying UGSs that are valuable from residents’ perspectives requires a comprehensive methodological approach. Limiting the assessment of the accessibility of such sites only to analyses based on the criterion of area and distance can offer narrow policy guidelines.

  • Accuracy of marine gravimetric measurements in terms of geodetic coordinates of land reference benchmark
    • Krzysztof Pyrchla
    • Kamil Łapiński
    • Jakub Szulwic
    • Wojciech Jurczak
    • Marek Przyborski
    • Jerzy Pyrchla
    2024 Full text Eksploatacja i Niezawodność - Maintenance and Reliability

    The article presents how the values of (3D) coordinates of land reference points affect the results of gravimetric measurements made from the ship in sea areas. These measurements are the basis for 3D maritime inertial navigation, improving ships' operational safety. The campaign verifying the network absolute point coordinates used as a reference point for relative marine gravity measurements was described. The obtained values were compared with catalogue values. In verification of network points 3D position the satellite data Global Satellite Navigation System (GNSS) and ground supporting systems (GBAS) was used. In this example, the height difference of the land reference point was 0.32 m. As a consequence, the offset budget of the marine campaign was affected in the range of up to 0.35 mGal. The influence on gravity free-air anomaly was not constant over the entire area covered by the campaign.

  • Accurate Post-processing of Spatially-Separated Antenna Measurements Realized in Non-Anechoic Environments
    • Adrian Bekasiewicz
    • Vorya Waladi
    • Tom Dhaene
    • Bartosz Czaplewski
    2024

    Antenna far-field performance is normally evaluated in expensive laboratories that maintain strict control over the propagation environment. Alternatively, the responses can be measured in non-anechoic conditions and then refined to extract the information on the structure field-related behavior. Here, a framework for correction of antenna measurements performed in non-anechoic test site has been proposed. The method involves automatic synchronization (in time-domain) of spatially separated measurements followed by their combination so as to augment the fraction of the signal that represents the antenna performance while suppressing the interferences. The method has been demonstrated based on six experiments performed in an office room. The performance improvement due to proposed post-processing amounts to 9.4 dB, which is represents up to over 5 dB improvement compared to the state-of-the-art methods.

  • Active Kriging-based conjugate first-order reliability method for highly efficient structural reliability analysis using resample strategy
    • Changqi Luo
    • Shun-Peng Zhu
    • Behrooz Keshtegar
    • Wojciech Macek
    • Ricardo Branco
    • Debiao Meng
    2024 COMPUTER METHODS IN APPLIED MECHANICS AND ENGINEERING

    Efficient structural reliability analysis method is crucial to solving reliability analysis of complex structural problems. High-computational cost and low-failure probability problems greatly limit the efficiency in structural reliability analysis problems, causing the safety and reliability of the structure to be questioned. In this work, a highly efficient structural reliability analysis method coupling active Kriging algorithm with conjugate first order reliability method (AK-CFORM) is proposed. Specifically, the resample strategy is considered to reduce the number of samples evaluated in each active learning process; the uniform sampling is used to better balance global and local optimal problems; the conjugate map is used to improve the robustness of analytical first order reliability method; and the approximate numerical differential formula is proposed to solve the problems of non-convergence when solving the gradient of the Kriging surrogate model. Finally, three numerical cases and four engineering cases are used to illustrate the effectiveness and robustness of the proposed method. The results show that the proposed AK-CFORM has greater advantages in the number of calling system response and surrogate model with robust and accurate performance.

  • Active Learning on Ensemble Machine-Learning Model to Retrofit Buildings Under Seismic Mainshock-Aftershock Sequence
    • Neda Asgarkhani
    • Farzin Kazemi
    • Robert Jankowski
    2024

    This research presents an efficient computational method for retrofitting of buildings by employing an active learning-based ensemble machine learning (AL-Ensemble ML) approach developed in OpenSees, Python and MATLAB. The results of the study shows that the AL-Ensemble ML model provides the most accurate estimations of interstory drift (ID) and residual interstory drift (RID) for steel structures using a dataset of 2-, to 9-story steel structures considering four soil type effects. To prepare the dataset, 3584 incremental dynamic analysis (IDA) were performed on 64 structures. The research employs 6-, and 8-story structures to validate the AL-Ensemble ML model's effectiveness, showing it achieves the highest accuracy among conventional ML models, with an R2 of 98.4%. Specifically, it accurately predicts the RID of floor levels in a 6-story structure with an accuracy exceeding 96.6%. Additionally, the programming code identifies the specific damaged floor level in a building, facilitating targeted local retrofitting instead of retrofitting the entire structure promising a reduction in retrofitting costs while enhancing prediction accuracy.

  • Activity-based payments: alternative (anonymous) online payment model
    • Rafał Leszczyna
    2024 International Journal of Information Security

    Electronic payments are the cornerstone of web-based commerce. A steady decrease in cash usage has been observed, while various digital payment technologies are taking over. They process sensitive personal information raising concerns about its potentially illicit usage. Several payment models that confront this challenge have been proposed. They offer varying levels of anonymity and readiness for adoption. The aim of this study was to broaden the portfolio with a solution that assures the highest level of anonymity and is well applicable. An empirical design research study with prototyping and conceptual research with a proposed construct were employed for this purpose. As a result, the Activity-Based Payment (ABP) model was proposed. It introduces a different mode of completing a payment transaction based on performing specific activities on a web location indicated by the payee. The anonymity properties of the solution, as well as its performance and applicability have been evaluated showing its particular suitability to micropayment and small payment scenarios.

  • Actual and reference evapotranspiration for a natural, temperate zone fen wetland – Upper Biebrza case study
    • Malgorzata Kleniewska
    • Tomasz Berezowski
    • Dorota Mitrowska
    • Sylwia Szporak-Wasilewska
    • Wojciech Ciezkowski
    2024 Full text Journal of Water and Land Development

    Evapotranspiration is the key and predominant component of the water balance in wetlands. Direct evapotranspiration measurements are challenging in wetlands due to their remoteness and high surface water level. This article describes the actual (ETa and reference evapotranspiration (ET0) from a cultivated wet meadow located in the Biebrza National Park – the largest national park in north-east Poland, Central Europe. The data were sourced from a micrometeorological station equipped with an eddy covariance system to measure heat and vapour fluxes and such meteorological elements as radiation balance components, air temperature and humidity. The values of directly measured ETa were presented daily in the context of available energy and ET0. Daily sums of ETa ranged from below 0.2 mm in winter to 6.5 mm in summer. The share of daily sums of ETa in the ET0 usually ranged from 50 to 60%, with extreme values from 10 to 170%. Aside from giving more insight into Biebrza wetlands’ functioning, the actual data produced in this study may be used instead of indirect methods, which were used the most in modelling wetlands areas.

  • Adapt Your Teacher: Improving Knowledge Distillation for Exemplar-free Continual Learning
    • Filip Szatkowski
    • Mateusz Pyła
    • Marcin Przewięźlikowski
    • Sebastian Cygert
    • Bartłomiej Twardowski
    • Tomasz Trzciński
    2024

    In this work, we investigate exemplar-free class incremental learning (CIL) with knowledge distillation (KD) as a regularization strategy, aiming to prevent forgetting. KDbased methods are successfully used in CIL, but they often struggle to regularize the model without access to exemplars of the training data from previous tasks. Our analysis reveals that this issue originates from substantial representation shifts in the teacher network when dealing with outof-distribution data. This causes large errors in the KD loss component, leading to performance degradation in CIL models. Inspired by recent test-time adaptation methods, we introduce Teacher Adaptation (TA), a method that concurrently updates the teacher and the main models during incremental training. Our method seamlessly integrates with KD-based CIL approaches and allows for consistent enhancement of their performance across multiple exemplar-free CIL benchmarks. The source code for our method is available at https://github.com/fszatkowski/cl-teacher-adaptation.

  • Adaptacyjny system oświetlania dróg oraz inteligentnych miast
    • Tomasz Śmiałkowski
    2024 Full text

    Przedmiotem rozprawy jest zbadanie praktycznej możliwości wykrywania w czasie rzeczywistym anomalii w systemie oświetlenia drogowego w oparciu o analizę danych ze inteligentnych liczników energii. Zastosowanie inteligentnych liczników energii elektrycznej (Smart Meter) w systemach oświetlenia drogowego stwarza nowe możliwości w zakresie automatycznej diagnostyki takich niepożądanych zjawisk jak awarie lamp, odstępstwa od harmonogramu czy tez kradzieże energii z sieci zasilającej. Rozwiązanie takie wpisuje się w koncepcję inteligentnych miast (Smart City) gdzie zastosowanie adaptacyjnego systemu oświetlenia stwarza nowe wyzwania dla funkcji monitorowania. Zbadano metody uczenia maszynowego oparte o modele regresyjne oraz rekurencyjne sieci neuronowe. Zaproponowano praktyczne podejście oparte na zastosowaniu algorytmów czasu rzeczywistego, nienadzorowanych oraz używających ograniczonych zasobów obliczeniowych możliwych do implementacji w urządzeniach przemysłowych. Algorytmy przetestowano na rzeczywistych danych pochodzących z instalacji systemu oświetlenia i wykazano, że obie metody umożliwiają stworzenie samouczących algorytmów detekcji anomalii, działających w czasie rzeczywistym i że jest możliwa ich implementacja na urządzeniach warstwy Edge Computing. W rozprawie przedstawiono również architekturę uniwersalnej platformy sterowania elementami infrastruktury oświetleniowej opracowanej przy udziale autora, jako głównego konstruktora, w ramach projektu rozwojowego „INFOLIGHT - Chmurowa platforma oświetleniowa dla inteligentnych miast”.

  • Adaptacyjny system sterowania ruchem drogowym
    • Andrzej Sroczyński
    2024 Full text

    Adaptacyjny system sterowania ruchem drogowym to rodzaj systemu sterowania, który dynamicznie, w czasie rzeczywistym, dostosowuje swoje parametry w oparciu o bieżące warunki ruchu drogowego. Celem niniejszej rozprawy jest sprawdzenie wpływu wybranych cech systemu, zbudowanego w oparciu o zaprojektowane i zbudowane z udziałem autora inteligentne znaki drogowe, na wybrane parametry mające wpływ na bezpieczeństwo i płynność ruchu. W pierwszej kolejności zbadany został, na podstawie eksperymentu symulacyjnego, wpływ metody stopniowania redukcji prędkości na płynność ruchu. Drugim przedmiotem badań był wpływ odległości pomiędzy kolejnymi znakami ograniczenia prędkości na wariancję prędkości pojazdów. Ostatnim badanym aspektem była weryfikacja możliwości testowania modeli uczenia maszynowego, wytrenowanych na danych rzeczywistych, za pomocą danych syntetycznych, uzyskanych w drodze symulacji. Wyniki badań posłużyły do udowodnienia trzech tez badawczych, sformułowanych w niniejszej pracy. Praca zawiera ponadto rozdział w całości poświęcony opisowi praktycznej realizacji demonstratora adaptacyjnego systemu sterowania ruchem drogowym. Przedstawione zostały instalacje eksperymentalne oraz niektóre rezultaty badań terenowych, zaś dodatkiem do rozprawy jest przegląd konstrukcji opracowanych demonstratorów inteligentnych znaków drogowych.

  • Adaptive Hounsfield Scale Windowing in Computed Tomography Liver Segmentation
    • Jan Cychnerski
    • Maciej Zakrzewski
    • Dominik Kwiatkowski
    2024

    In computed tomography (CT) imaging, the Hounsfield Unit (HU) scale quantifies radiodensity, but its nonlinear nature across organs and lesions complicates machine learning analysis. This paper introduces an automated method for adaptive HU scale windowing in deep learning-based CT liver segmentation. We propose a new neural network layer that optimizes HU scale window parameters during training. Experiments on the Liver Tumor Segmentation Benchmark show that the learned window parameters often converge to a range encompassing clinically used windows but wider, suggesting that adjacent data may contain useful information for machine learning. This layer may enhance model efficiency with just 2 additional parameters.

  • Adaptive Hyperparameter Tuning within Neural Network-based Efficient Global Optimization
    • Taeho Jeong
    • Leifur Leifsson
    • Sławomir Kozieł
    • Anna Pietrenko-Dąbrowska
    2024

    In this paper, adaptive hyperparameter optimization (HPO) strategies within the efficient global optimization (EGO) with neural network (NN)-based prediction and uncertainty (EGONN) algorithm are proposed. These strategies utilize Bayesian optimization and multiarmed bandit optimization to tune HPs during the sequential sampling process either every iteration (HPO-1itr) or every five iterations (HPO-5itr). Through experiments using the three-dimensional Hartmann function and evaluating both full and partial sets of HPs, adaptive HPOs are compared to traditional static HPO (HPO-static) that keep HPs constant. The results reveal that adaptive HPO strategies outperform HPOstatic, and the frequency of tuning and number of tuning HPs impact both the optimization accuracy and computational efficiency. Specifically, adaptive HPOs demonstrate rapid convergence rates (HPO-1itr at 28 iterations, HPO-5itr at 26 for full HPs; HPO-1itr at 13, HPO-5itr at 28 iterations for selected HPs), while HPO-static fails to approximate the minimum within the allocated 45 iterations for both scenarios. Mainly, HPO-5itr is the most balanced approach, found to require 21% of the time taken by HPO-1itr for tuning full HPs and 29% for tuning a subset of HPs. This work demonstrates the importance of adaptive HPO and sets the stage for future research.

  • Adaptive Optimal Discrete-Time Output-Feedback Using an Internal Model Principle and Adaptive Dynamic Programming
    • Zhongyang Wang
    • Youqing Wang
    • Zdzisław Kowalczuk
    2024 IEEE-CAA Journal of Automatica Sinica

    In order to address the output feedback issue for linear discrete-time systems, this work suggests a brand-new adaptive dynamic programming (ADP) technique based on the internal model principle (IMP). The proposed method, termed as IMP-ADP, does not require complete state feedback, merely the measurement of input and output data. More specifically, based on the IMP, the output control problem can first be converted into a stabilization problem. We then design an observer to reproduce the full state of the system by measuring the inputs and outputs. Moreover, includes both a policy iteration algorithm and a value iteration algorithm to determine the optimal feedback gain without using a dynamic system model. It is important that in this concept you do not need to solve the regulator equation. Finally, this control method was tested on an inverter system of grid-connected LCLs to demonstrate that the proposed method provides the desired performance in terms of both tracking and disturbance rejection.

  • Adaptive Sampling for Non-intrusive Reduced Order Models Using Multi-Task Variance
    • Abhijnan Dikshit
    • Leifur Leifsson
    • Sławomir Kozieł
    • Anna Pietrenko-Dąbrowska
    2024

    Non-intrusive reduced order modeling methods (ROMs) have become increasingly popular for science and engineering applications such as predicting the field-based solutions for aerodynamic flows. A large sample size is, however, required to train the models for global accuracy. In this paper, a novel adaptive sampling strategy is introduced for these models that uses field-based uncertainty as a sampling metric. The strategy uses Monte Carlo simulations to propagate the uncertainty in the prediction of the latent space of the ROM obtained using a multitask Gaussian process to the high-dimensional solution of the ROM. The high-dimensional uncertainty is used to discover new sampling locations to improve the global accuracy of the ROM with fewer samples. The performance of the proposed method is demonstrated on the environment model function and compared to one-shot sampling strategies. The results indicate that the proposed adaptive sampling strategies can reduce the mean relative error of the ROM to the order of 8 × 10−4 which is a 20% and 27% improvement over the Latin hypercube and Halton sequence sampling strategies, respectively at the same number of samples.

  • Addressing challenges of BiVO4 light-harvesting ability through vanadium precursor engineering and sub-nanoclusters deposition for peroxymonosulfate-assisted photocatalytic pharmaceuticals removal
    • Marta Kowalkińska
    • Alexey Maximenko
    • Aleksandra Szkudlarek
    • Karol Sikora
    • Anna Zielińska-Jurek
    2024 Full text SEPARATION AND PURIFICATION TECHNOLOGY

    In this study, we present a complex approach for increasing light utilisation and peroxymonosulfate (PMS) activation in BiVO4-based photocatalyst. This involves two key considerations: the design of the precursor for BiVO4 synthesis and interface engineering through CuOx sub-nanoclusters deposition. The designed precursor of ammonium methavanadate (NH4VO3, NHV) leads to reduction in particle size, better dispersion and improved light harvesting ability, confirmed by the calculations of the local volume rate of photon absorption (LVRPA) using the Six-Flux Radiation Absorption-Scattering model. The morphological changes result in a significant improvement in photocatalytic activity under visible light for the degradation of pharmaceuticals (naproxen and ofloxacin) compared to the commercial NH4VO3. Additionally, CuOx sub-nanoclusters were deposited on designed BiVO4 and characterised using X-ray absorption near edge structure (XANES). The presence of subnanoclusters enhanced charge carriers separation, resulting in an increase in the apparent rate constants of 1.60 and 3.32-times for photocatalytic NPX and OFL removal, respectively. The application of obtained Vis light active photocatalysts in the presence of 0.1 mM PMS resulted in remarkably more efficient degradation of NPX (100 % within 60 min) and OFL (98.2 % within 120 min). PMS/Vis420/CuOx/BiVO4 system exhibited high stability and reusability in the subsequent cycles of photodegradation. However, high PMS dosage induced Bi leaching which may cause the instability of the photocatalyst. Finally, to address the environmental implications of pharmaceutical removal and adhere to the Guidelines for drinking-water quality, toxicity assessments using Vibrio fischeri bacteria were performed and compared to a quantitative structure–activity relationship (QSAR) model.

  • Addressing the Weaknesses of Multi-Criteria Decision-Making Methods using Python
    • Semra Erpolat Tasabat
    • Tugba KIRAL Ozkan
    • Olgun Aydin
    2024

    The book aims to draw attention to the weaknesses in Multi-Criteria Decision-Making (MCDM) methods and provide insights to improve the decision-making process. By addressing these weaknesses, it seeks to enhance the accuracy and effectiveness of MCDM methods in selecting the best alternatives in various fields. The book covers popular MCDM methods such as TOPSIS, ELECTRE, VIKOR, and PROMETHEE. It compares traditional methods with the proposed modified Human Development Index (HDI) data using Python code examples. The target audience for the book includes computer scientists, engineers, business, and financial management professionals, as well as anyone interested in MCDM and its applications.

  • Adjusted SpikeProp algorithm for recurrent spiking neural networks with LIF neurons
    • Krzysztof Laddach
    • Rafał Łangowski
    2024 APPLIED SOFT COMPUTING

    A problem related to the development of a supervised learning method for recurrent spiking neural networks is addressed in the paper. The widely used Leaky-Integrate-and-Fire model has been adopted as a spike neuron model. The proposed method is based on a known SpikeProp algorithm. In detail, the developed method enables gradient descent learning of recurrent or multi-layer feedforward spiking neural networks. The research included an extended verification study for the classical XOR classification problem. In addition, the developed learning method has been used to provide a spiking neural black-box model of fast processes occurring in a pressurised water nuclear reactor. The obtained simulation results demonstrate satisfactory effectiveness of the proposed approach.

  • Adoption of the F-statistic of Fisher-Snedecor distribution to analyze importance of impact of modifications of injector opening pressure of a compression ignition engine on specific enthalpy value of exhaust gas flow
    • Patrycja Puzdrowska
    2024 Full text Combustion Engines

    This article analyzes the effect of modifications of injector opening pressure on the operating values of a compression ignition engine, including the temperature of the fumes. A program of experimental investigation is described, considering the available test stand and measurement capabilities. The structure of the test stand on which the experimental measurements were conducted is presented. The method of introducing real modifications of injector opening pressure to the existing test engine was characterized. It was proposed to use F statistic of Fisher-Snedecor (F-S) distribution to evaluate the importance of the impact of modifications of injector opening pressure on the specific enthalpy of the flue gas flow. Qualitative and statistical studies of the results achieved from the measurements were carried out. The specific enthalpy of the fumes for a single cycle of the compression ignition engine, determined from the course of rapidly variable flue gas temperature, was analyzed. The results of these studies are presented and the usable adoption of this type of assessment in parametric diagnosing of compression ignition engines is discussed.

  • Advanced Bayesian study on inland navigational risk of remotely controlled autonomous ship
    • Cunlong Fan
    • Victor Bolbot
    • Jakub Montewka
    • Di Zhang
    2024 ACCIDENT ANALYSIS AND PREVENTION

    The arise of autonomous ships has necessitated the development of new risk assessment techniques and methods. This study proposes a new framework for navigational risk assessment of remotely controlled Maritime Autonomous Surface Ships (MASS). This framework establishes a set of risk influencing factors affecting safety of navigation of a remotely-controlled MASS. Next, model parameters are defined based on the risk factors, and the model structure is developed using Bayesian Networks. To this end, an extensive literature survey is conducted, enhanced with the domain knowledge elicited from the experts and improved by the experimental data obtained during representative MASS model trials carried out in an inland river. Conditional Probability Tables are generated using a new function employing expert feedback regarding Interval Type 2 Fuzzy Sets. The developed Bayesian model yields the expected utilities results representing an accident’s probability and consequence, with the results visualized on a dedicated diagram. Finally, the developed risk assessment model is validated by conducting three axiom tests, extreme scenarios analysis, and sensitivity analysis. Navigational environment, natural environment, traffic complexity, and shore-ship collaboration performance are critical from the probability and consequence perspective for inland navigational accidents to a remotely controlled MASS. Lastly, important nodes to Shore-Ship collaboration performance include autonomy of target ships, cyber risk, and transition from other remote control centers.

  • Advanced nanomaterials and metal-organic frameworks for catalytic bio-diesel production from microalgal lipids – A review
    • Zohaib Saddique
    • Muhammad Imran
    • Shoomaila Latif
    • Ayesha Javaid
    • Shahid Nawaz
    • Nemira Zilinskaite
    • Marcelo Franco
    • Ausra Baradoke
    • Ewa Wojciechowska
    • Grzegorz Boczkaj
    2024 JOURNAL OF ENVIRONMENTAL MANAGEMENT

    Increasing energy demands require exploring renewable, eco-friendly (green), and cost-effective energy resources. Among various sources of biodiesel, microalgal lipids are an excellent resource, owing to their high abundance in microalgal biomass. Transesterification catalyzed by advanced materials, especially nanomaterials and metal-organic frameworks (MOFs), is a revolutionary process for overcoming the energy crisis. This review elaborates on the conversion of microalgal lipids (including genetically modified algae) into biodiesel while primarily focusing on the transesterification of lipids into biodiesel by employing catalysts based on above mentioned advanced materials. Furthermore, current challenges faced by this process for industrial scale upgradation are presented with future perspectives and concluding remarks. These materials offer higher conversion (>90%) of microalgae into biodiesel. Nanocatalytic processes, lack the need for higher pressure and temperature, which simplifies the overall process for industrial-scale application. Green biodiesel production from microalgae offers better fuel than fossil fuels in terms of performance, quality, and less environmental harm. The chemical and thermal stability of advanced materials (particularly MOFs) is the main benefit of the blue recycling of catalysts. Advanced materials-based catalysts are reported to reduce the risk of biodiesel contamination. While purity of glycerin as side product makes it useful skin-related product. However, these aspects should still be controlled in future studies. Further studies should relate to additional aspects of green production, including waste management strategies and quality control of obtained products. Finally, catalysts stability and recycling aspects should be explored.

  • Advanced seismic control strategies for smart base isolation buildings utilizing active tendon and MR dampers
    • Morteza Akbari
    • Javad Palizvan Zand
    • Tomasz Falborski
    • Robert Jankowski
    2024 ENGINEERING STRUCTURES

    This paper investigates the seismic behaviour of a five-storey shear building that incorporates a base isolation system. Initially, the study considers passive base isolation and employs a multi-objective archived-based whale optimization algorithm called MAWOA to optimize the parameters of base isolation. Subsequently, a novel model is proposed, which incorporates an interval type-2 Takagi-Sugeno fuzzy logic controller (IT2TSFLC) utilizing clustering techniques. The building includes Magneto-rheological (MR) dampers installed at the base isolation level and two active tendons positioned on the first and second storeys of the structure. The semi-active control force of the base isolation with MR dampers is determined by the fuzzy system, while the active control force for the active tendons is computed using the linear quadratic regulator (LQR) algorithm, enabling control force provision during seismic events. The primary objective of this model is to enhance the seismic control of the building. Therefore, it is classified as a proposed model. The structural system is subjected to seismic analyses, considering three different structural configurations: uncontrolled, equipped with passive base isolation, and equipped with semi-active base isolation combining MR dampers and active tendons. The findings of the research demonstrate that by considering the optimization of parameters of the passive base isolation based on the white noise scenario and using these parameters as design parameters, during seismic analysis of the structure in some earthquakes, increased structural responses were observed when compared to uncontrolled structure, highlighting a potential risk. Nevertheless, the proposed system effectively addresses this drawback of passive control systems by markedly reducing structural responses, as compared to both passive base isolation and uncontrolled structure. These results suggest that the proposed system is an effective solution for mitigating seismic risks in structural seismic control.

  • Advanced Sensor for Non-Invasive Breast Cancer and Brain Cancer Diagnosis Using Antenna Array with Metamaterial-Based AMC
    • Musa Hamza
    • Mohammad Tariqul Islam
    • Sławomir Kozieł
    2024 Full text Engineering Science and Technology-An International Journal-JESTECH

    Microwave imaging techniques can identify abnormal cells in early development stages. This study introduces a microstrip patch antenna coupled with artificial magnetic conductor (AMC) to realize improved sensor for non-invasive (early-stage) breast cancer and brain cancer diagnosis. The frequency selectivity of the proposed antenna has been increased by the presence of AMC by creating an additional resonance at 2.276 GHz associated with peak gain of 8.15 dBi and 10.02 dBi, with and without AMC, respectively. High precision and high-quality imaging in the field of medical diagnostics are ensured by the directive radiation pattern of the sensor, emitted from the center of the sensor's front surface. The antenna has been manufactured and experimentally validated with measurement results being in good agreement with the full-wave simulations. In particular, the measured broadside gain at the operating frequency is 11.7 dBi. The presented structure has been incorporated in the microwave imaging system for breast and brain cancer identification. Extensive simulation studies corroborate its suitability for the task based on the analysis of multiple scenarios of tumor detection. Furthermore, our antenna has been favorably compared to state-of-the-art designs reported in the literature showing its competitive performance, especially in terms of size, impedance matching bandwidth, and gain trade-offs.

  • Advancing Solar Energy: Machine Learning Approaches for Predicting Photovoltaic Power Output
    • Kawsar Nassereddine
    • Marek Turzyński
    • Mykola Lukianov
    • Ryszard Strzelecki
    2024

    This research is primarily concentrated on predicting the output of photovoitaic power, an essential field in the study of renewable energy. The paper comprehensively reviews various forecasting methodologies, transitioning from conventional physical and statistical methods to advanced machine learning (ML) techniques. A significant shift has been observed from traditional point forecasting to machine learning-based forecasting in solar power. This transition offers a broader and more detailed perspective for power system operators. The core of this research lies in applying and comparing three distinct Machine Learning algorithms for forecasting photovoltaic power output. The primary aim is to evaluate each method's accuracy and to identify the algorithm with the lowest prediction error. This comparative analysis is crucial for determining the most effective machine learning forecasting method, significantly contributing to the more reliable and efficient integration of renewable energy into power systems.

  • Advancing sustainable hybrid bitumen systems: A compatibilization solution by functionalized polyolefins for enhanced crumb rubber content in bitumen
    • Mateusz Malus
    • Joanna Bojda
    • Maciej Sienkiewicz
    • Wojciech Szot
    • Miloud Bouyahyi
    • Lanti Yang
    • Francisco Javier Navarro
    • Maha AlSayegh
    • Rasha Daadoush
    • Maria Soliman
    • Rob Duchateau
    • Lidia Jasinska-Walc
    2024 JOURNAL OF CLEANER PRODUCTION

    Polymer waste pollution has a profound effect on the environment and, consequently, on the lifestyle of hu- mankind. The massive production and disposal of cross-linked polymers clearly exemplify the challenges of recycling. Increasing efforts are being undertaken to introduce recycled polymers, especially crumb rubber (CR), into asphalt formulations. Due to the rather poor processability and phase separation associated with CR- modified bitumen (CRMB) compositions, a broader implementation of such concept is challenging unless an efficient compatibilizer is applied. Results from the study on usage of In-Reactor-Functionalized Polyolefins viz. poly(propylene-co-hex-1-ene-co-hex-5-en-1-ol) (FPP), demonstrated excellent compatibilizing ability in CRMB, allowing incorporation of up to 10 wt% of CR. This represents a significant improvement when compared to the best-in-class solutions. The FPP-containing products exhibit superior bulk, nanomechanical and rheological properties, as well as stability during binder annealing. Furthermore, the bitumen surface morphology is significantly improved. The polar groups present in the FPP create a thermo-reversible interpenetrating cross- linked network that provides mechanical integrity and contributes to the adhesion to different components of the modified bitumen at service temperatures, enhancing its processability. The exceptional compatibility of FPP in CRMB resulted in a significant increase in the Performance Grade of the hybrid system by 5 classes (88) compared to neat bitumen (58). Moreover, the best-performing composition fulfilled the low-temperature ductility specifications, withstanding deformation without fracturing or breaking up to a 400 mm elongation.

  • Advancing sustainable wastewater management: A comprehensive review of nutrient recovery products and their applications
    • Bogna Śniatała
    • Hussein Al-Hazmi
    • Dominika Sobotka
    • Jun Zhai
    • Jacek Mąkinia
    2024 SCIENCE OF THE TOTAL ENVIRONMENT

    Wastewater serves as a vital resource for sustainable fertilizer production, particularly in the recovery of nitrogen (N) and phosphorus (P). This comprehensive study explores the recovery chain, from technology to final product reuse. Biomass growth is the most cost-effective method, valorizing up to 95 % of nutrients, although facing safety concerns. Various techniques enable the recovery of 100 % P and up to 99 % N, but challenges arise during the final product crystallization due to the high solubility of ammonium salts. Among these techniques, chemical precipitation and ammonia stripping/ absorption have achieved full commercialization, with estimated recovery costs of 6.0–10.0 EUR kgP-1 and 4.4-4.8 £ kgN-1, respectively. Multiple technologies integrating biomass thermo-chemical processing and P and/or N have also reached technology readiness level TRL = 9. However, due to maturing regulatory of waste-derived products, not all of their products are commercially available. The non-homogenous nature of wastewater introduces impurities into nutrient recovery products. While calcium and iron impurities may impact product bioavailability, some full-scale P recovery technologies deliver products containing this admixture. Recovered mineral nutrient forms have shown up to 60 % higher yield biomass growth compared to synthetic fertilizers. Life cycle assessment studies confirm the positive environmental outcomes of nutrient recycling from wastewater to agricultural applications. Integration of novel technologies may increase wastewater treatment costs by a few percent, but this can be offset through renewable energy utilization and the sale of recovered products. Moreover, simultaneous nutrient recovery and energy production via bio-electrochemical processes contributes to carbon neutrality achieving. Interdisciplinary cooperation is essential to offset both energy and chemicals inputs, increase their cos-efficiency and optimize technologies and understand the nutrient release patterns of wastewater-derived products on various crops. Addressing non-technological factors, such as legal and financial support, infrastructure redesign, and market-readiness, is crucial for successfully implementation and securing the global food production.

  • AGREEMIP: The Analytical Greenness Assessment Tool for Molecularly Imprinted Polymers Synthesis
    • Mariusz Marć
    • Wojciech Wojnowski
    • Francisco Pena-Pereira
    • Marek Tobiszewski
    • Antonio Martín-Esteban
    2024 ACS Sustainable Chemistry & Engineering

    Molecular imprinting technology is well established in areas where a high selectivity is required, such as catalysis, sensing, and separations/sample preparation. However, according to the Principles of Green Chemistry, it is evident that the various steps required to obtain molecularly imprinted polymers (MIPs) are far from ideal. In this regard, greener alternatives to the synthesis of MIPs have been proposed in recent years. However, although it is intuitively possible to design new green MIPs, it would be desirable to have a quantitative measure of the environmental impact of the changes introduced for their synthesis. In this regard, this work proposes, for the first time, a metric tool and software (termed AGREEMIP) to assess and compare the greenness of MIP synthesis procedures. AGREEMIP is based on 12 assessment criteria that correspond to the greenness of different reaction mixture constituents, energy requirements, and the details of MIP synthesis procedures. The input data of the 12 criteria are transformed into individual scores on a 0−1 scale that in turn produce an overall score through the calculation of the weighted average. The assessment can be performed using user-friendly open-source software, freely downloadable from mostwiedzy.pl/agreemip. The assessment result is an easily interpretable pictogram and visually appealing, showing the performance in each of the criteria, the criteria weights, and overall performance in terms of greenness. The application of AGREEMIP is presented with selected case studies that show good discrimination power in the greenness assessment of MIP synthesis pathways.

  • Agri-food waste biosorbents for volatile organic compounds removal from air and industrial gases – A review
    • Patrycja Makoś-Chełstowska
    • Edyta Słupek
    • Jacek Gębicki
    2024 Full text SCIENCE OF THE TOTAL ENVIRONMENT

    Approximately 1.3 billion metric tons of agricultural and food waste is produced annually, highlighting the need for appropriate processing and management strategies. This paper provides an exhaustive overview of the utilization of agri-food waste as a biosorbents for the elimination of volatile organic compounds (VOCs) from gaseous streams. The review paper underscores the critical role of waste management in the context of a circular economy, wherein waste is not viewed as a final product, but rather as a valuable resource for innovative processes. This perspective is consistent with the principles of resource efficiency and sustainability. Various types of waste have been described as effective biosorbents, and methods for biosorbents preparation have been discussed, including thermal treatment, surface activation, and doping with nitrogen, phosphorus, and sulfur atoms. This review further investigates the applications of these biosorbents in adsorbing VOCs from gaseous streams and elucidates the primary mechanisms governing the adsorption process. Additionally, this study sheds light on methods of biosorbents regeneration, which is a key aspect of practical applications. The paper concludes with a critical commentary and discussion of future perspectives in this field, emphasizing the need for more research and innovation in waste management to fully realize the potential of a circular economy. This review serves as a valuable resource for researchers and practitioners interested in the potential use of agri-food waste biosorbents for VOCs removal, marking a significant first step toward considering these aspects together.

  • Algebraic periods and minimal number of periodic points for smooth self-maps of 1-connected 4-manifolds with definite intersection forms
    • Haibao Duan
    • Grzegorz Graff
    • Jerzy Jezierski
    • Adrian Myszkowski
    2024 Full text Journal of Fixed Point Theory and Applications

    Let M be a closed 1-connected smooth 4-manifolds, and let r be a non-negative integer. We study the problem of finding minimal number of r-periodic points in the smooth homotopy class of a given map f: M-->M. This task is related to determining a topological invariant D^4_r[f], defined in Graff and Jezierski (Forum Math 21(3):491–509, 2009), expressed in terms of Lefschetz numbers of iterations and local fixed point indices of iterations. Previously, the invariant was computed for self-maps of some 3-manifolds. In this paper, we compute the invariants D^4_r[f] for the self-maps of closed 1-connected smooth 4-manifolds with definite intersection forms (i.e., connected sums of complex projective planes). We also present some efficient algorithmic approach to investigate that problem.

  • Alginate-based sorbents in miniaturized solid phase extraction techniques - Step towards greenness sample preparation
    • Natalia Jatkowska
    2024 Full text TRAC-TRENDS IN ANALYTICAL CHEMISTRY

    In response to growing concerns about environmental degradation, one of the main areas of research activity in recent years has been to make sample preparation methods more sustainable and eco-friendly. The increasing greenness of this step can be achieved by minimizing the usage of reagents, automating individual stages, saving energy and time, and using non-toxic, biodegradable substances. Therefore, the use of natural materials as sorbents in miniaturized extraction techniques is becoming a main trend. One of the natural material that is increasingly being used, not only due to eco-friendly nature but also because of their easy applicability to various sample preparation techniques, is alginate hydrogel. Following this trend, this review discusses the recent application of alginate-based sorbents in various microextraction techniques, focusing on functionalization approaches that enhance extraction performance. Additionally, the green profile of alginate-based sorbent microextraction approaches, along with the sorbent synthesis, were investigated.

  • Algorithmic Human Resources Management
    • Łukasz Sienkiewicz
    2024

    The rapid evolution of Digital Human Resources Management has introduced a transformative era where algorithms play a pivotal role in reshaping the landscape of workforce management. This transformation is encapsulated in the concepts of algorithmic management and algorithmic Human Resource Management (HRM). The integration of advanced analytics, predictive and prescriptive analytics and the power of Artificial Intelligence (AI) has given rise to algorithmic approaches that go beyond traditional human-centric decision-making. This paradigm shift is marked by comprehensive data collection, real-time responses to data influencing management decisions and automated decision-making processes. The culmination of algorithmic management is illustrated in a scheme where human-configured management processes are automated, leading to autonomous decision-making by an information system. The ongoing development of AI-driven algorithms, adapting and becoming self-learning, raises the prospect of increased automation, potentially leading to the displacement of human managers. This chapter provides a comprehensive overview of the evolution of algorithmic management and algorithmic HRM, setting the stage for a deeper exploration of their implications on organisational decision-making, employee management and the future of algorithm-supported management. While algorithmic HRM brings benefits to organisations it also poses risks, such as hidden errors and ethical concerns, emphasising the need for transparency and responsible application. Unconscious system errors, especially in fully automated systems, can lead to detrimental personnel decisions. Organisations must actively counteract potential negative consequences, striking a balance between technology and human decision-making, fostering organisational culture that embraces digital transformation while prioritising the human element. Enterprises should view technology as a tool to enhance work processes, focusing on connecting people and technology to create an innovative and inclusive work environment.

  • Alphitobius diaperinus larvae (lesser mealworm) as human food – An approval of the European Commission – A critical review
    • Shahida Anusha Siddiqui
    • Y.s. Wu
    • K. Vijeepallam
    • K. Batumalaie
    • M.h.m. Hatta
    • H. Lutuf
    • Roberto Castro Munoz
    • I. Fernando
    • S.a. Ibrahim
    2024 Full text Journal of Insects as Food and Feed

    Due to the increasing threat of climate change and the need for sustainable food sources, human consumption of edible insects or entomophagy has gained considerable attention globally. The larvae of Alphitobius diaperinus Panzer (Coleoptera: Tenebrionidae), also known as the lesser mealworm, have been identified as a promising candidate for mass-rearing as a food source based the on evaluation on several aspects such as the production process, the microbiological and chemical composition, and the potential allergenicity to humans. As a consequence, the European Commission has recently approved the utilization of lesser mealworms as human foods. Lesser mealworms are considered a good source of protein, with a protein content ranging from 50-65% of their dry weight and containing various essential amino acids. Lesser mealworms are also rich in other essential nutrients such as iron, calcium, and vitamins B12 and B6. Furthermore, the hydrolysates of lesser mealworms are known to contain antioxidants, suggesting the therapeutic properties of the insects. To enable and ensure a continuous supply of lesser mealworms, various rearing procedures of the insects and information on optimal environmental rearing conditions have been reported. However, like other edible insects, lesser mealworms are still not commonly consumed in Western countries because of various consumer- and product-related factors. Ultimately, the European Commission’s approval of lesser mealworms as a novel food is a key milestone in the development of the insect food industry. Embracing the consumption of edible insects can help address the challenges of feeding a growing population, mitigate the environmental impact of food production, and promote a more sustainable and resilient food system for the future.

  • An absorbing set for the Chialvo map
    • Paweł Pilarczyk
    • Grzegorz Graff
    2024 Communications in Nonlinear Science and Numerical Simulation

    The classical Chialvo model, introduced in 1995, is one of the most important models that describe single neuron dynamics. In order to conduct effective numerical analysis of this model, it is necessary to obtain a rigorous estimate for the maximal bounded invariant set. We discuss this problem, and we correct and improve the results obtained by Courbage and Nekorkin (2010). In particular, we provide an explicit formula for an absorbing set for the Chialvo neuron model. We also introduce the notion of a weakly absorbing set, outline the methodology for its construction, and show its advantage over an absorbing set by means of numerical computations.

  • An Adaptive Network Model for a Double Bias Perspective on Learning from Mistakes within Organizations
    • Mojgan Hosseini
    • Jan Treur
    • Wioleta Kucharska
    2024

    Although making mistakes is a crucial part of learning, it is still often being avoided in companies as it is considered as a shameful incident. This goes hand in hand with a mindset of a boss who dominantly believes that mistakes usually have negative consequences and therefore avoids them by only accepting simple tasks. Thus, there is no mechanism to learn from mistakes. Employees working for and being influenced by such a boss also strongly believe that mistakes usually have negative consequences but in addition they believe that the boss never makes mistakes, it is often believed that only those who never make mistakes can be bosses and hold power. That’s the problem, such kinds of bosses do not learn. So, on the one hand, we have bosses who select simple tasks to be always seen as perfect. Therefore, also they believe they should avoid mistakes. On the other hand, there exists a mindset of a boss who is not limited to simple tasks, he/she accepts more complex tasks and therefore in the end has better general performance by learning from mistakes. This then also affects the mindset and actions of employees in the same direction. This paper investigates the consequences of both attitudes for the organizations. It does so by computational analysis based on an adaptive dynamical systems modeling approach represented in a network format using the self-modeling network modeling principle.

  • An Analysis of Airline GRI and SDG Reporting
    • Eljas Johansson
    2024 Full text

    This study aims to increase our understanding of the Global Reporting Initiative’s (GRI) topic-specific disclosures and the sustainable development goals (SDGs) addressed in the global passenger airline industry’s sustainability reporting (SR). Based on a quantitative content analysis of the industry’s sustainability reports from the financial year 2019 (FY19), this study reveals that airlines focused more on reporting environmental issues, especially emissions, than economic or social dimensions, demonstrating this emission-intensive industry’s responsiveness to stakeholders’ information needs. However, a closer look at the reported impacts shows that many topic-specific disclosures and SDGs, which industry associations have not identified as relevant to the industry, were also mentioned across the reports. Moreover, the results indicated a broader use of SDGs in Asia-Pacific reports than in European. The results are expected to interest practitioners and academics in assessing and developing the industry’s SR.

  • An analytical approach to determine the health benefits and health risks of consuming berry juices
    • Magdalena Fabjanowicz
    • Anna Rożańska
    • Nada S. Abdelwahab
    • Marina Pereira-Coelho
    • Isabel Cristina da Silva Haas
    • Luiz Augusto dos Santos Madureira
    • Justyna Płotka-Wasylka
    2024 Full text FOOD CHEMISTRY

    Food products composition analysis is a prerequisite for verification of product quality, fulfillment of regulatory enforcements, checking compliance with national and international food standards, contracting specifications, and nutrient labeling requirements and providing quality assurance for use of the product for the supplemen- tation of other foods. These aspects also apply to the berry fruit and berry juice. It also must be noted that even though fruit juices are generally considered healthy, there are many risks associated with mishandling both fruits and juices themselves. The review gathers information related with the health benefits and risk associated with the consumption of berry fruit juices. Moreover, the focus was paid to the quality assurance of berry fruit juice. Thus, the analytical methods used for determination of compounds influencing the sensory and nutritional characteristics of fruit juice as well as potential contaminants or adulterations.

  • An annotated timeline of sensitivity analysis
    • Marta Kuc-Czarnecka
    • Stefano Tarantolo
    • Federico Ferretti
    • Samuele Lo Piano
    • Mariia Kozlova
    • Alesio Lachi
    • Rosana Rosati
    • Arnald Puy,
    • Pamphile Roy
    • Giulia Vannucci
    • Andrea Saltelli,
    2024 Full text ENVIRONMENTAL MODELLING & SOFTWARE

    The last half a century has seen spectacular progresses in computing and modelling in a variety of fields, applications, and methodologies. Over the same period, a cross-disciplinary field known as sensitivity analysis has been making its first steps, evolving from the design of experiments for laboratory or field studies, also called ‘in-vivo’, to the so-called experiments ‘in-silico’. Some disciplines were quick to realize the importance of sensitivity analysis, whereas others are still lagging behind. Major tensions within the evolution of this discipline arise from the interplay between local vs global perspectives in the analysis as well as the juxtaposition of the mathematical complexification and the desire for practical applicability. In this work, we retrace these main steps with some attention to the methods and through a bibliometric survey to assess the accomplishments of sensitivity analysis and to identify the potential for its future advancement with a focus on relevant disciplines, such as the environmental field.