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Gdańsk University of Technology

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  • Adaptable management for cooling cyclic air in ship power plants by heat conversion – Part 1: Downsizing strategy for cogeneration plants
    • Roman Radchenko
    • Andrii Radchenko
    • Dariusz Mikielewicz
    • Mykola Radchenko
    • Anatoliy Pavlenko
    • Andrii Andreev
    2024 ENERGY

    The ship power plants (SPP) are generally based on Diesel engines. Their fuel efficiency is gradually sensible to cyclic air temperatures and drops with their rise. A sustainable performance of ship engines with high fuel efficiency is possible by cooling intake and charge air as two objects in waste heat conversion chillers. The peculiarities of marine engine application are associated with constrained space of machine room. Whereas, the chiller’s downsizing leads to inevitable lack of their cooling capacity and incomplete cooling air which results in reduction of fuel saving. The research objective is to develop the heat conversion management adaptable to balanced downsizing and fuel saving strategies due to flexible heat distribution between the chillers of different efficiencies (COP) in response to current thermal loads on engine cyclic air cooling system along the ship routes which enables to foresee a sustainable thermally stabilized and fuel saving operation of SPP. For the first time, such conflicting challenges are satisfied by flexible heat distribution between different chillers in response to intake and charge air cooling needs. The unique of such approach lies in unloading the high efficient but cumbersome chiller (absorption with COP about 0.7 as example) to boost the less efficient but easy to place in machine room ejector chiller (COP of about 0.2). The method of flexible heat distribution to minimize chiller sizes as constraints is realized in methodology based on step-by-step comparing the gap between heat demand and production to minimize shortage in fuel reduction simultaneously. It has been proved that cooling engine air by compact but less efficient ejector chiller (ECh) and high effective but cumbersome lithium bromide absorption chiller (ACh) of reduced capacity and sizes by about 30% accordingly provides a specific fuel consumption reduced by about 3%. The loss of route fuel saving by about 10% is considered as the "cost" for downsizing. These findings have been verified by the calculation results on current and summarized values of the cooling capacities lack, caused by the chiller’s downsizing, and fuel saving along the ship route. The novel strategy of heat conversion by combined chillers is especially useful for upgrading the existing engine air cooling system to implement it into the ship machine room.

  • 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
    • Maciej Zakrzewski
    • Dominik Kwiatkowski
    • Jan Cychnerski
    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.

  • Additive manufacturing of Proton-Conducting Ceramics by robocasting with integrated laser postprocessing
    • Joanna Pośpiech
    • Małgorzata Nadolska-Dawidowska
    • Mateusz Cieślik
    • Tomasz Sobczyk
    • Marek Chmielewski
    • Aleksandra Mielewczyk-Gryń
    • Ragnar Strandbakke
    • José M Serra
    • Sebastian Wachowski
    2024 Full text Applied Materials Today

    A hybrid system combining robocasting and NIR laser postprocessing has been designed to fabricate layers of mixed proton-electron conducting Ba0.5La0.5Co1-xFexO3-δ ceramic. The proposed manufacturing technique allows for the control of the geometry and microstructure and shortens the fabrication time to a range of a few minutes. Using 5 W laser power and a scanning speed of 500 mm⋅s− 1, sintering of a round-shaped layer with an 8 mm radius was performed in less than 2 s. The single phase of the final product was confirmed by X-ray diffraction. Various ceramic-to-polymer weight ratios were tested, showing that various porosities of microstructures of ~30 - 35 % and ~19 % can be obtained with 2:1 and 4:1 loading respectively.

  • 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.

  • Adsorption behavior of Methylene Blue and Rhodamine B on microplastics before and after ultraviolet irradiation
    • Jiang Li
    • Kefu Wang
    • Kangkang Wang
    • Siqi Liang
    • Changyan Guo
    • Afaq Hassan
    • Jide Wang
    2024 COLLOIDS AND SURFACES A-PHYSICOCHEMICAL AND ENGINEERING ASPECTS

    The accumulation of microplastics (MPs) and dyes has attracted extensive attention because of their environ mental effects, which will be exacerbated especially after the aging of MPs. This study aimed at investigating the significance of Methylene Blue (MB) and Rhodamine B (RhB) adsorption behavior by PLA (polylactic acid) and PA66 (Polyamide 66) MPs after UV aging. After aging, there was an observed increase in the hydrophilicity, specific surface area, and oxygen content of MPs. The results indicate that aging enhances the adsorption ca pacity of both PLA and PA66. Furthermore, it is noteworthy that PLA undergoes more significant changes in its physicochemical properties compared to PA66 following aging. The adsorption process conformed the pseudosecond order (PSO) kinetic model and Langmuir isotherm well, and the adsorption capacity followed the sequence of aged-PLA > aged-PA66 > pristine-PA66 > pristine-PLA. Besides, the adsorption of dyes onto the MPs was studied across four variables (pH, salinity, surfactants, and dissolved organic matter). The aforementioned findings collectively demonstrate that the aged MPs still exhibit a higher adsorption capacity than the pristine MPs. In desorption experiments, the desorption efficiency of MB (PLA) was reduced from 35.29 % (P-PLA) to 32.76 % (A-PLA), and the similar trend was observed on other aged MPs. These findings suggest that aged MPs

  • 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 N. 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.

  • Advanced ultra super critical power plants: role of buttering layer
    • Saurabh Rathore
    • Amit Kumar
    • Sachin Sirohi
    • Shailesh M. Pandey
    • Ankur Gupta
    • Dariusz Fydrych
    • Chandan Pandey
    2024 INTERNATIONAL JOURNAL OF ADVANCED MANUFACTURING TECHNOLOGY

    Dissimilar metal welded (DMW) joint plays a crucial role in constructing and maintaining ultra-supercritical (USC) nuclear power plants while presenting noteworthy environmental implications. This research examines different welding techniques utilized in DMWJ, specifically emphasizing materials such as P91. The study investigates the mechanical properties of these materials, the impact of alloying elements, the notable difficulties encountered with industrial materials, and the concept of buttering. The USC nuclear power plants necessitate welding procedures appropriate for the fusion of diverse metal alloys. Frequently employed methodologies encompass shielded metal arc welding (SMAW), gas tungsten arc welding (GTAW), gas metal arc welding (GMAW), and flux-cored arc welding (FCAW). Every individual process possesses distinct advantages and limitations, and the choice of process is contingent upon various factors, including joint configuration, material properties, and the desired weld quality. The steel alloy known as P91, which possesses high strength and resistance to creep, is extensively employed in advanced ultra-supercritical (AUSC) power plants. P91 demonstrates exceptional mechanical characteristics, encompassing elevated-temperature strength, commendable thermal conductivity, and notable resistance against corrosion and oxidation. The presence of alloying elements, namely chromium, molybdenum, and vanadium, in P91, is responsible for its improved characteristics and appropriateness for utilization in (AUSC) power plant applications. Nevertheless, the utilization of industrial materials in DMW joint is accompanied by many noteworthy concerns, such as the propensity for stress corrosion cracking (SCC), hydrogen embrittlement, and creep deformation under high temperatures. The challenges mentioned above require meticulous material selection, process optimization, and rigorous quality control measures to guarantee the dependability and sustained effectiveness of DMW joint. To tackle these concerns, a commonly utilized approach referred to as buttering is frequently employed. When forming DMW joint in nuclear facilities, it is customary to place a buttering coating on ferritic steel. This facilitates the connection between pressure vessel components of ferritic steel and pipes of austenitic stainless steel.

  • Advances and Trends in Non-Conventional, Abrasive and Precision Machining 2021
    • Mariusz Deja
    • Angelos P. Markopoulos
    2024 Machines

    In the modern, rapidly evolving industrial landscape, the quest for machining and production processes consistently delivering superior quality and precision is more pronounced than ever. This necessity and imperative are driven by the increasing complexity in the design and manufacturing of mechanical components, an evolution in lockstep with the swift advancements in material science. The real challenge of this evolution lies in the strategic integration and continuous development of novel machining methods and processes within the manufacturing sphere. Non-conventional machining processes, standing in contrast to their conventional counterparts, exploit alternative forms of energy, including thermal, electrical, and chemical, to form and/or remove material. These innovative processes are distinguished and characterized by their utilization of high-power density energy sources, high accuracy, and the capability to machine complex and design-demanding geometries. Among these techniques are Electrical Discharge Machining (EDM), Electrochemical Machining (ECM), laser processing, and laser-assisted machining, each heralding a new era of precision and capability in manufacturing. Simultaneously, abrasive processes such as grinding, lapping, polishing, and superfinishing are undergoing relentless advancement, continuously pushing the boundaries of efficiency and surface finish quality. These methods are pivotal in achieving the highest surface finishes and are instrumental in the pursuit of advancement in manufacturing.

  • Advancing electrochemical impedance analysis through innovations in the distribution of relaxation times method
    • Adeleke Maradesa
    • Baptiste Py
    • Jake Huang
    • Yang Luo
    • Pietro Iurilli
    • Aleksander Mroziński
    • Ho Mei Law
    • Yuhao Wang
    • Zilong Wang
    • Jingwei Li
    • Shengjun Xu
    • Quentin Meyer
    • Jiapeng Liu
    • Claudio Brivio
    • Alexander Gavrilyuk
    • Kiyoshi Kobayashi
    • Antonio Bertei
    • Nicholas J. Williams
    • Chuan Zhao
    • Michael Danzer
    • Mark Zic
    • Phillip Wu
    • Ville Yrjänä
    • Sergei Pereverzyev
    • Yuhui Chen
    • André Weber
    • Sergei V. Kalinin
    • Jan Philipp Schmidt
    • Yoed Tsur
    • Bernard A. Boukamp
    • Qiang Zhang
    • Miran Gaberšček
    • Ryan O’Hayre
    • Francesco Ciucci
    2024 Joule

    Electrochemical impedance spectroscopy (EIS) is widely used in electrochemistry, energy sciences, biology, and beyond. Analyzing EIS data is crucial, but it often poses challenges because of the numerous possible equivalent circuit models, the need for accurate analytical models, the difficulties of nonlinear regression, and the necessity of managing large datasets within a unified framework. To overcome these challenges, non-parametric models, such as the distribution of relaxation times (DRT, also known as the distribution function of relaxation times, DFRT), have emerged as promising tools for EIS analysis. For example, the DRT can be used to generate equivalent circuit models, initialize regression parameters, provide a time-domain representation of EIS spectra, and identify electrochemical processes. However, mastering the DRT method poses challenges as it requires mathematical and programming proficiency, which may extend beyond experimentalists’ usual expertise. Post-inversion analysis of DRT data can be difficult, especially in accurately identifying electrochemical processes, leading to results that may not always meet expectations. This article examines nonparametric EIS analysis methods, outlining their strengths and limitations from theoretical, computational, and end-user perspectives, and provides guidelines for their future development. Moreover, insights from survey data emphasize the need to develop a large impedance database, akin to an impedance genome. In turn, software development should target one-click, fully automated DRT analysis for multidimensional EIS spectra interpretation, software validation, and reliability. Particularly, creating a collaborative ecosystem hinged on free software could promote innovation and catalyze the adoption of the DRT method throughout all fields that use impedance data.

  • 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.

  • Advancing Urban Transit: Gepard and CAR projects - Innovations in Trolleybus Technology
    • Mikołaj Bartłomiejczyk
    • Leszek Jarzębowicz
    • Slobodan Mirchevski
    • Marcin Połom
    2024

    The Gepard project in Gdynia, Poland, revolutionized the city's trolleybus network with the introduction of “Trolleybus 2.0” vehicles and an innovative charging system. “Trolleybus 2.0” vehicles combine features of traditional trolleybuses and electric buses boasting traction batteries for autonomous driving and dual legal approval. Statistical analysis of energy consumption informed the development of a hybrid charging concept, balancing overhead contact line (OHL) coverage with additional fast charging stations. This hybrid In Motion Charhing (IMC) system reduces costs while ensuring reliable operation, even in adverse weather conditions. Moreover, as part of the CAR project, a fast charging station for trolleybuses was constructed, allowing for the additional extension of trolleybus routes.

  • A Flexible Way of Coarse Coordinates Estimation for Sodars
    • Kamil Stawiarski
    2024 International Journal of Electronics and Telecommunications

    The publication presents a flexible approach to implementing coarse coordinate estimation of an object observed with a sodar. This flexibility permits any arrangement of sound sources as well as microphones. Only minimal requirements are imposed on the probing signal, which can particularly be broadband. The algorithms have been tested on both synthetic data and data recorded with an actual device.

  • 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.

  • AI-Powered Cleaning Robot: A Sustainable Approach to Waste Management
    • Johan Carcamo Pineda
    • Ahmad M.A. Shehada
    • Arda Candas
    • Nirav Vaghasiya
    • Murad Abdullayev
    • Jacek Rumiński
    2024

    The world is producing a massive amount of single use waste, especially plastic waste made from polymers. Such waste is usually distributed in large areas within cities, near roads, parks, forests, etc. It is a challenge to collect them efficiently. In this work, we propose a Cleaning Robot as an autonomous vehicle for waste collection, utilizing the Nvidia Jetson Nano platform for precise arm movements guided by computer vision capabilities. Integrated with the Raspberry Pi platform for mobility control, the robot employs the YOLO (You Only Look Once) framework for efficient waste detection and classification. The model was trained, implemented in the robot prototype, and tested on preprocessed waste images, resulting in mean average precision (mAP) above 80 percent. Our design emphasizes singular-object focus, enabling real-time detection of waste with accurate distance (83.7-95.6%) and direction (84.7 97.3%) information. The robot autonomously navigates towards detected waste, halting at a predefined distance for collection and disposal into a designated bin. This work contributes to advancements in waste management systems using small robots.

  • AI-powered Customer Relationship Management – GenerativeAI-based CRM – Einstein GPT, Sugar CRM, and MS Dynamics 365
    • Edyta Gołąb-Andrzejak
    2024 Full text Procedia Computer Science

    Generative artificial intelligence (GenAI) and its implementation in successive business management support systems is a rapidly growing area of theoretical consideration, ongoing research, discourse and application in practice. Recently, the implementation of of GenAI in customer relationship management (CRM) systems has been observed. Accordingly, the aim of this article is to identify areas where GenAI can enhance CRM systems, using Einstain GPT, Sugar CRM or Microsoft Dynamics 365 as examples. To this end, a research question was formulated: how can GenAI improve the effective use of CRM systems? Accordingly, a preliminary study based on secondary data analysis as well as software analysis was conducted to identify areas of GenAI use in CRM systems where we see an increase in the effective application of CRM. The results of the analysis showed that GenAI-powered CRM systems support the effectiveness and efficiency of marketing, sales, commerce, service and system user success. This is because they provide numerous advantages in terms of developing, expanding and strengthening customer relationships through highly advanced personalisation, closely linked to customer segmentation, which allows unique experiences to be provided to individual segments. As a result, this translates into building a company's competitive advantage and increasing the profitability of its CRM efforts.

  • Airport Runoff Water: State-of-the-Art and Future Perspectives
    • Anna Maria Sulej-Suchomska
    • Danuta Szumińska
    • Miguel de la Guardia
    • Piotr Przybyłowski
    • Żaneta Polkowska
    2024 Sustainability

    The increase in the quantity and variety of contaminants generated during routine airport infrastructure maintenance operations leads to a wider range of pollutants entering soil and surface waters through runoff, causing soil erosion and groundwater pollution. A significant developmental challenge is ensuring that airport infrastructure meets high-quality environmental management standards. It is crucial to have effective tools for monitoring and managing the volume and quality of stormwater produced within airports and nearby coastal areas. It is necessary to develop methodologies for determining a wide range of contaminants in airport stormwater samples and assessing their toxicity to improve the accuracy of environmental status assessments. This manuscript aims to showcase the latest advancements (2010–2024 update) in developing methodologies, including green analytical techniques, for detecting a wide range of pollutants in airport runoff waters and directly assessing the toxicity levels of airport stormwater effluent. An integrated chemical and ecotoxicological approach to assessing environmental pollution in airport areas can lead to precise environmental risk assessments and well-informed management decisions for sustainable airport operations. Furthermore, this critical review highlights the latest innovations in remediation techniques and various strategies to minimize airport waste. It shifts the paradigm of soil and water pollution management towards nature-based solutions, aligning with the sustainable development goals of the 2030 Agenda

  • 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.

  • Alkyl polyglycoside-assisted separation followed by smartphone-based digital image colorimetry for on-site determination of total phenolic content in plant-based milk alternatives
    • Lutfi Yahya
    • Marek Tobiszewski
    • Khrystyna Vakh
    2024 MICROCHEMICAL JOURNAL

    A low-cost lab-on-a-smartphone platform for rapid and on-site assessment of total phenolic content in plant-based milk alternatives has been proposed for the first time. The three main components, including a smartphone, a disposable Eppendorf vial and a specially designed holder with an integrated polychromatic light source, were compactly assembled to enable color-forming reaction, separation and measurement. The method involves the application of the classically used Folin-Ciocalteu reagent to perform a color-forming reaction and the green surfactant alkyl polyglycoside C8-C10 to separate derivatives and impurities of the sample matrix. The coacervation phenomenon proceeding due to the addition of heptanoic acid to a micellar solution facilitates efficient phase separation and overcomes the challenges posed by matrix components such as ash, protein, carbohydrates and crude fibre. It also eliminates the need to use centrifugation and filtration as the matrix components were isolated into the obtained supramolecular solvent. The digital images were captured inside a lab-on-a-smartphone platform with controlled light conditions and analyzed using image processing algorithms. Various types of plant-based milk alternatives, including extracts from cereals, legumes, nuts, seeds and pseudocereals, were analyzed and the total phenolic content was found in the range 34.6 to 2748.7 mg GAE L−1. The results obtained, compared with the reference method, demonstrate significant findings, with a slope of the Passing-Bablok regression equal to 1.0049, indicating results are in good agreement. The linear range for total phenolic content was established as 25–110 mg GAE L−1, with a coefficient of determination of 0.9957. The limit of quantification and limit of detection were determined to be 25 mg GAE L−1 and 15 mg GAE L−1, respectively. Inter-day and intra-day precision, expressed as coefficient of variation (% CV), were 7.28 % and 2.29 %, respectively, while recovery rates ranged from 73 % to 154 %. Additionally, a greenness assessment using the AGREE tool showed an overall score of 0.84, indicating that the proposed smartphone-based method has a low environmental footprint. The total analysis time did not exceed 10 min, which is satisfactory for on-site analysis.

  • All but one expanding Lorenz maps with slope greater than or equal to $\sqrt 2$ are leo
    • Piotr Bartłomiejczyk
    • Piotr Nowak-Przygodzki
    2024 Colloquium Mathematicum

    We prove that with only one exception, all expanding Lorenz maps $f\colon[0,1]\to[0,1]$ with the derivative $f'(x)\ge\sqrt{2}$ (apart from a finite set of points) are locally eventually onto. Namely, for each such $f$ and each nonempty open interval $J\subset(0,1)$ there is $n\in\N$ such that $[0,1)\subset f^n(J)$. The mentioned exception is the map $f_0(x)=\sqrt{2}x+(2-\sqrt{2})/2 \pmod 1$. Recall that $f$ is an expanding Lorenz map if it is strictly increasing on $[0,c)$ and $[c,1]$ for some $c$ and satisfies the condition $\inf{f'}>1$.

  • 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 Adversarial Machine Learning Approach on Securing Large Language Model with Vigil, an Open-Source Initiative
    • Kushal Pokhrel
    • Cesar Sanín
    • Md Rafiqul Islam
    • Edward Szczerbicki
    2024 Full text Procedia Computer Science

    Several security concerns and efforts to breach system security and prompt safety concerns have been brought to light as a result of the expanding use of LLMs. These vulnerabilities are evident and LLM models have been showing many signs of hallucination, repetitive content generation, and biases, which makes them vulnerable to malicious prompts that raise substantial concerns in regard to the dependability and efficiency of such models. It is vital to have a complete grasp of the complex behaviours of malicious attackers in order to build effective strategies for protecting modern artificial intelligence (AI) systems through the development of effective tactics. The purpose of this study is to look into some of these aspects and propose a method for preventing devastating possibilities and protecting LLMs from potential threats that attackers may pose. Vigil is an open-source LLM prompt security scanner, that is accessible as a Python library and REST API, specifically to solve these problems by employing a sophisticated adversarial machine-learning algorithm. The entire objective of this study is to make use of Vigil as a security scanner. and asses its efficiency. In this case study, we shed some light on Vigil, which effectively recognises and helps LLM prompts by identifying two varieties of threats: malicious and benign.

  • 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 Analysis of the Performance of Lightweight CNNs in the Context of Object Detection on Mobile Phones
    • Jakub Łęcki
    • Marek Hering
    • Maciej Jabłoński
    • Aleksandra Karpus
    2024

    Convolutional Neural Networks (CNNs) are widely used in computer vision, which is now increasingly used in mobile phones. The problem is that smartphones do not have much processing power. Initially, CNNs focused solely on increasing accuracy. High-end computing devices are most often used in this type of research. The most popular application of lightweight CNN object detection is real-time image processing, which can be found in devices such as cameras and autonomous vehicles. Therefore, there is a need to optimize CNNs for use on mobile devices. This paper presents the comparision of latency and mAP of 22 lightweight CNN models from the MobileNet and EfficientDet families measured on 7 mobile phones.

  • 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 ANN-Based Method for On-Load Tap Changer Control in LV Networks with a Large Share of Photovoltaics—Comparative Analysis
    • Klara Janiga
    • Piotr Miller
    • Robert Małkowski
    • Michał Izdebski
    2024 ENERGIES

    The paper proposes a new local method of controlling the on-load tap changer (OLTC) of a transformer to mitigate negative voltage phenomena in low-voltage (LV) networks with a high penetration of photovoltaic (PV) installations. The essence of the method is the use of the load compensation (LC) function with settings determined via artificial neural network (ANN) algorithms. The proposed method was compared with other selected local methods recommended in European regulations, in particular with those currently required by Polish distribution system operators (DSOs). Comparative studies were performed using the model of the 116-bus IEEE test network, taking into account the unbalance in the network and the voltage variation on the medium voltage (MV) side.

  • 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.

  • An automated learning model for twitter sentiment analysis using Ranger AdaBelief optimizer based Bidirectional Long Short Term Memory
    • Sasirekha Natarajan
    • Smitha Kurian
    • Parameshachari Bidare Divakarachari
    • Przemysław Falkowski-Gilski
    2024 EXPERT SYSTEMS

    Sentiment analysis is an automated approach which is utilized in process of analysing textual data to describe public opinion. The sentiment analysis has major role in creating impact in the day-to-day life of individuals. However, a precise interpretation of text still relies as a major concern in classifying sentiment. So, this research introduced Bidirectional Long Short Term Memory with Ranger AdaBelief Optimizer (Bi-LSTM RAO) to classify sentiment of tweets. Initially, data is obtained from Twitter API, Sentiment 140 and Stanford Sentiment Treebank-2 (SST-2). The raw data is pre-processed and it is subjected to feature extraction which is performed using Bag of Words (BoW) and Term Frequency-Inverse Document Frequency (TF-IDF). The feature selection is performed using Gazelle Optimization Algorithm (GOA) which removes the irrelevant or redundant features that maximized model performance and classification is performed using Bi LSTM–RAO. The RAO optimizes the loss function of Bi-LSTM model that maximized accuracy. The classification accuracy of proposed method for Twitter API, Sentiment 140 and SST 2 dataset is obtained as 909.44%, 99.71% and 99.86%, respectively. These obtained results are comparably higher than ensemble framework, Robustly Optimized BERT and Gated Recurrent Unit (RoBERTa-GRU), Logistic Regression-Long Short Term Memory (LR-LSTM), Convolutional Bi-LSTM, Sentiment and Context Aware Attention-based Hybrid Deep Neural Network (SCA-HDNN) and Stochastic Gradient Descent optimization based Stochastic Gate Neural Network (SGD-SGNN).

  • An Efficient PEEC-Based Method for Full-Wave Analysis of Microstrip Structures
    • Jinyan Ma
    • Da Li
    • Hanzhi Ma
    • Ruifeng Li
    • Ling Zhang
    • Michał Mrozowski
    • Erping Li
    2024 IEEE TRANSACTIONS ON ELECTROMAGNETIC COMPATIBILITY

    This article introduces an efficient method for the equivalent circuit characterization and full-wave analysis of microstrip structures, leveraging the full-wave partial element equivalent circuit (PEEC). In particular, the multilayered Green's function is evaluated using the discrete complex-image method (DCIM) and employed to establish the mixed potential integral equations. The proposed strategy considers time delays for the retarded electric and magnetic couplings, offering a new efficient full-wave approach to extract equivalent circuit components, which encapsulate the contributions of the quasi-static, surface-wave, and complex images. It is noted that the proposed full-wave PEEC strategy allows each component contribution derived from DCIM to be efficiently represented as frequency-independent lumped circuit elements and corresponding frequency factors, thereby simplifying the extraction process of the entire frequency-dependent lumped elements in the traditional PEEC method. Moreover, the proposed PEEC model, equipped with full-wave equivalent circuits, offers clear physical insight into electromagnetic behaviors, thereby facilitating design and optimization. Finally, the accuracy and efficiency of the proposed PEEC model are fully demonstrated through various examples and experiments.

  • An Empirical Study of a Dynamic Stop Loss Strategy with Deep Reinforcement Learning on the NASDAQ Stock Market
    • Mateusz Anders
    • Jozef Zurada
    • Paweł Weichbroth
    2024

    The objective of this paper is to empirically investigate the efficacy of using Deep Reinforcement Learning (DRL) to maximize investment returns by incorporating expected optimal closing prices of long positions into a daily strategy. This paper extends existing research on the impact of stop-loss orders on investment strategy results and brings contribution of these orders to trading strategies into a completely new perspective. We propose a novel approach using DRL, in contrast to fixed-price stop-loss strategies, trailing-stop strategies, or other machine learning approaches. In the backtesting experiment, daily OHLCV data for stocks from the NASDAQ-100 index (as of May 2024) were used for the period spanning from January 2014 to January 2024. The strategy is compared with buy-and-hold, stop-loss, and trailing stop-loss strategies. Significant effort was made to accurately reflect market conditions in the simulation. We found a positive impact of using DRL compared to other tested strategies when encountering entirely new data, suggesting positive serial market correlations. The results suggest that appropriate closing rules and active management of stop levels can increase investment returns without necessarily reducing portfolio return volatility.

  • An Example of Using Low-Cost LiDAR Technology for 3D Modeling and Assessment of Degradation of Heritage Structures and Buildings
    • Piotr Kędziorski
    • Marcin Jagoda
    • Paweł Tysiąc
    • Jacek Katzer
    2024 Materials

    This article examines the potential of low-cost LiDAR technology for 3D modeling and assessment of the degradation of historic buildings, using a section of the Koszalin city walls in Poland as a case study. Traditional terrestrial laser scanning (TLS) offers high accuracy but is expensive. The study assessed whether more accessible LiDAR options, such as those integrated with mobile devices such as the Apple iPad Pro, can serve as viable alternatives. This study was conducted in two phases—first assessing measurement accuracy and then assessing degradation detection—using tools such as the FreeScan Combo scanner and the Z+F 5016 IMAGER TLS. The results show that, while low-cost LiDAR is suitable for small-scale documentation, its accuracy decreases for larger, complex structures compared to TLS. Despite these limitations, this study suggests that low-cost LiDAR can reduce costs and improve access to heritage conservation, although further development of mobile applications is recommended.

  • An Extremely Compact Frequency Reconfigurable Antenna Diplexer Employing Dielectric Liquids
    • Rusan Kumar Barik
    • Xiaohu Wu
    • Xiaoguang Liu
    • Sławomir Kozieł
    2024 IEEE Antennas and Wireless Propagation Letters

    The letter presents an extremely compact frequency reconfigurable antenna diplexer based on fluidic channels for sub6 GHz applications. The proposed antenna diplexer is modelled by employing half-mode (HM) and quarter-mode (QM) substrateintegrated rectangular cavities, two slots, orthogonal feed lines, and fluidic vias. To comprehend the radiation mechanism, the equivalent circuit, electric field distributions, and frequency responses are analyzed. Utilization of HM and QM cavities that are loaded with slots results in an extremely compact antenna diplexer. Three fluidic vias are bored from the bottom plane of each cavity and filled with different dielectric liquids to enable frequency reconfigurability. For validation of the concept, an antenna diplexer is built and demonstrated. The constructed antenna prototype has a small footprint 0.078lg2 with 15% and 16% of reconfigurability in lower and upper frequency bands, respectively. The proposed antenna offers high-isolation exceeding 28 dB, realized gain better than > 3.8 dBi, front-to-back-ratio of > −18 dB, and cross-polarization level of > −18 dB. A good consistency is obtained in between full-wave simulations and measurement.

  • An image processing approach for fatigue crack identification in cellulose acetate replicas
    • Krzysztof Pałczyński
    • Jan Seyda
    • Dariusz Skibicki
    • Łukasz Pejkowski
    • Wojciech Macek
    2024 ENGINEERING FAILURE ANALYSIS

    The cellulose acetate replication technique is an important method for studying material fatigue. However, extracting accurate information from pictures of cellulose replicas poses challenges because of distortions and numerous artifacts. This paper presents an image processing procedure for effective fatigue crack identification in plastic replicas. The approach employs thresholding, adaptive Gaussian thresholding, and Otsu binarization to convert gray-scale images into binary ones, enhancing crack visibility. Morphological operations refine object shapes, and Connected Components Analysis facilitates crack identification. Despite limited data, the handcrafted feature extraction algorithm proves robust, addressing challenges. The algorithm shows efficacy in detecting cracks as small as 30 μm, even in the presence of cellulose replication artifacts. The results highlight ability to capture significant cracks’ orientation, length, and growth stages, essential for understanding fatigue mechanisms. Analysis of results, especially evaluation metrics encompassing false positives and false negatives, provides a comprehensive understanding of the algorithm’s strengths and limitations. The proposed tool is available on GitHub.