2024 - Repozytorium publikacji - Politechnika Gdańska

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On the use of black tea waste as a functional filler for manufacturing self-stabilizing polyethylene composites: In-depth thermal analysis
- Joanna Aniśko-Michalak
- Paulina Kosmela
- Mateusz Barczewski
2024 INDUSTRIAL CROPS AND PRODUCTS
Thermal and oxidative stabilization are critical aspects in the processing and exploitation of polyolefins. Black tea contains many natural antioxidants, the largest group of compounds in its composition. When used as a filler for composite manufacturing, the thermo-oxidation process of polyethylene can be slowed down. Black tea waste (BTW) generated during the process of packing tea into sachets was introduced into a bio-based low-density polyethylene (LDPE) as a filler. The composites containing 1, 2, 5, and 10 wt% were produced by melt extrusion. In order to assess the antioxidant capacity of the used filler, an analysis was carried out using the DPPH solution on both the filler extracts and the produced composites. The total phenolic content and total flavonoid content analysis were also carried out on black tea waste extracts. Thermogravimetric analysis of composites was held in an inert and oxidative atmosphere, and data from the test in a nitrogen atmosphere was used to calculate degradation kinetics. The composites were also evaluated in terms of their thermal behavior by differential scanning calorimetry (DSC) to describe their crystallization process and oxidation induction time (OIT) to confirm stabilization effects caused by functional waste fillers on polyethylene. Adding 10 wt% of black tea waste elongates OIT nearly 36 times compared to LDPE. The thermo-oxidation process was also conducted at 90 °C for 1, 2, 4, 7, 11 and 15 days. The composites aged this way were subjected to the FTIR test. Thanks to this study, the carbonyl index (CI) was determined, which showed that adding tea waste limits the oxidation of polyethylene.
On the use of uniaxial one-parameter damage laws for estimating fatigue life under multiaxial loading
- Ricardo Branco
- José Domingos Costa
- L.p. Borrego
- Zbigniew Marciniak
- Wojciech Macek
- Filippo Berto
2024 Pełny tekst Procedia Structural Integrity
The goal of this paper is to evaluate the capabilities of different one-parameter fatigue laws to estimate crack initiation in notched components under multiaxial loading. Fatigue damage is accounted for through stress-based, strain-based, and energy-based approaches while the cyclic plasticity at the notch-controlled process zone is estimated using linear-elastic simulations. The results show that energy-based formulations established from both the absorbed energy at the mid-life cycle and the energy absorbed throughout the entire life are more accurate.
One year performance evaluation of low volume road with cold recycled base course on the basis of FWD and Dynamic Modulus tests
- Mariusz Jaczewski
- Cezary Szydłowski
- Bohdan Dołżycki
2024
Article presents results of assessment of performance of trial section of flexible pavement with cold recycled base constructed on low volume road within typical contract conditions. Performance evaluation was made based on Falling Weight Deflectometer (FWD) test performed during construction stage – 2 times on the top of cold recycled base course – 28 and 180 days after compaction of base course and 2 times on the top of the wearing course – 270 and 360 days after compaction of base course. Deflections and back calculated moduli were analyzed. Additionally, performance of the cold recycled base was determined on the basis of Dynamic Modulus test conducted on the material col-lected during trial section construction. Moduli were determined for 7, 14, 28, 90, 180 and 360 days after the specimen compaction. Performed analysis showed that the development of rheological properties shows similar trends for both field and laboratory tests and proved high bearing capacity of pavements with cold recycled bases.
Ontology-based text convolution neural network (TextCNN) for prediction of construction accidents
- Shi Donghui
- Li Zhigang
- Jozef Zurada
- Andrew Manikas
- Jian Guan
- Paweł Weichbroth
2024 Pełny tekst KNOWLEDGE AND INFORMATION SYSTEMS
The construction industry suffers from workplace accidents, including injuries and fatalities, which represent a significant economic and social burden for employers, workers, and society as a whole.The existing research on construction accidents heavily relies on expert evaluations,which often suffer from issues such as low efficiency, insufficient intelligence, and subjectivity.However, expert opinions provided in construction accident reports offer a valuable source of knowledge that can be extracted and utilized to enhance safety management.Today this valuable resource can be mined as the advent of artificial intelligence has opened up significant opportunities to advance construction site safety. Ontology represents an attractive representation scheme.Though ontology has been used in construction safety to solve the problem of information heterogeneity using formal conceptual specifications, the establishment and development of ontologies that utilize construction accident reports are currently in an early stage of development and require further improvements. Moreover, research on the exploration of incorporating deep learning methodologies into construction safety ontologies for predicting construction incidents is relatively limited.This paper describes a novel approach to improving the performance of accident prediction models by incorporating ontology into a deep learning model.A domain word discovery algorithm, based on mutual information and adjacency entropy, is used to analyze the causes of accidents mentioned in construction reports. This analysis is then combined with technical specifications and the literature in the field of construction safety to build an ontology encompassing unsafe factors related to construction accidents.By employing TransH model, the reports are transformed into conceptual vectors using the constructed ontology. Building on this foundation, we propose a TextCNN model that incorporates the ontology specifically designed for construction accidents. We compared the performance of the model against five traditional machine learning models, namely Naive Bayes, support vector machine, logistic regression,random forest, and multilayer perceptron, using three different data sets:One-Hot encoding, word vector, and conceptual vectors. The results indicate that the TextCNN model integrated with the ontology outperformed the other models in terms of performance achieving an impressive accuracy rate of 88% and AUC value of 0.92.
OOA-modified Bi-LSTM network: An effective intrusion detection framework for IoT systems
- Siva Surya Narayana Chintapalli
- Satya Prakash Singh
- Jaroslav Frnda
- Bidare Parameshachari Divakarachar
- Vijaya Lakshmi Sarraju
- Przemysław Falkowski-Gilski
2024 Pełny tekst Heliyon
Currently, the Internet of Things (IoT) generates a huge amount of traffic data in communication and information technology. The diversification and integration of IoT applications and terminals make IoT vulnerable to intrusion attacks. Therefore, it is necessary to develop an efficient Intrusion Detection System (IDS) that guarantees the reliability, integrity, and security of IoT systems. The detection of intrusion is considered a challenging task because of inappropriate features existing in the input data and the slow training process. In order to address these issues, an effective meta heuristic based feature selection and deep learning techniques are developed for enhancing the IDS. The Osprey Optimization Algorithm (OOA) based feature selection is proposed for selecting the highly informative features from the input which leads to an effective differentiation among the normal and attack traffic of network. Moreover, the traditional sigmoid and tangent activation functions are replaced with the Exponential Linear Unit (ELU) activation function to propose the modified Bi-directional Long Short Term Memory (Bi-STM). Themodified Bi-LSTM is used for classifying the types of intrusion attacks. The ELU activation function makes gradients extremely large during back-propagation and leads to faster learning. This research is analysed in three different datasets such as N-BaIoT, Canadian Institute for Cybersecurity Intrusion Detection Dataset 2017 (CICIDS-2017), and ToN-IoT datasets. The empirical investigation states that the proposed framework obtains impressive detection accuracy of 99.98 %, 99.97 % and 99.88 % on the N-BaIoT, CICIDS-2017, and ToN-IoT datasets, espectively. Compared to peer frameworks, this framework obtains high detection accuracy with better interpretability and reduced processing time.
Open Strategy for Digital Business. Managing in ICT-Driven Environments
- Ewa Lechman
- Joanna Radomska
- Ewa Stańczyk-hugiet
2024
This book offers the reader a novel perspective on how digital contexts and open strategy approaches – the act of opening up strategic initiatives beyond company managers to involve front-line employees, stakeholders, and entrepreneurs – are related. Going beyond the claim that digital media drives open strategy by containing a detailed analyses of the interrelations between the two, the authors examine how ICT have diffused globally and trace the emerging links between digitally driven environments and open strategizing approaches. This book also draws a general picture of how and why digital technologies create new networks. A more competitive, transparent, empowered, and inclusive environment would enhance development and encourage novel approaches to strategies implemented. Real-life exemplifications of how and why digital technologies contribute to open strategizing are also provided. Various drivers impacting the necessity to develop more relational advantage are discussed and intertwined with the description of challenges observed in the case of imposing openness. A useful resource for researchers of strategic management and information systems, as well as those looking at digital strategy and transformation.
Operational Performance and Weld Bead Characteristics of Experimental Tubular-Wires for Underwater Welding
- Orlando Castellanos-Gonzalez
- Eduardo Sanchez Lobo
- Dariusz Fydrych
- Bruno Silva Cota
- José Gedael Fagundes Júnior
- Andrés M. Moreno-Uribe
2024 Advances in Science and Technology Research Journal
Aiming to evaluate new formulations and their operational behavior underwater, two experimental tubular wires with different chemical compositions in their internal flux were initially manufactured, employing a pilot machine and a unique manufacturing process. Weld beads were deposited on a plate placed in a flat position inside a tank using a mechanized system and the IMC 300 welding power source. The work was done at a depth of 0.3 meters of water, and both reverse and direct polarities were used. Arc voltage at 28 V, wire feed speed at 4.5 m/min and welding speed at 250 mm/min were maintained in all experiments. As a result, the weld bead morphology and the electrical variables related to arc voltage and welding current were analyzed. It was concluded that the metal transfer showed a predominance of short circuits and a decrease in the average value of the Short Circuit Index by 67% when using the positive polarity configuration compared to the negative polarity. Moreover, the average penetration value was 38 % higher for the oxidizing-based tubular wire than for the rutile-based tubular wire, which has a possible relationship with the more significant presence of oxygen in the flux. In addition, the results present an analysis of the performance of the process and the characterization of the weld beads obtained to guide possible modifications in the parameters.
Opracowanie i wprowadzenie nowoczesnego systemu utrzymania ruchu urządzeń opartego na strategii planowo – zapobiegawczej na obiekcie offshore
- Paweł Magulski
2024 Pełny tekst
W pracy przedstawiono metodykę utrzymania ruchu maszyn i urządzeń na obiekcie offshore. Opracowany autorski system utrzymania ruchu, który został wdrożony na morskiej platformie wydobywczej „Petrobaltic” gdzie do przesyłu i przetwarzania danych wykorzystano dostępną infrastrukturę informatyczną. Założenia realizowano z wykorzystaniem metod naukowych takich jak analiza case study, metody eksperckie i inne. Pracę zrealizowano w ośmiu etapach, w ramach których stworzono bazy danych systemów i urządzeń z podziałem na lokalizacje funkcjonalne; opracowano i wdrożono do istniejących systemów ERP listy zadań przeglądowych wynikających z eksploatacji urządzeń, wzbogaconych o kalendarze upływu ważności certyfikatów, atestów, przeglądów poszczególnych urządzeń; opracowano i wdrożono do istniejących systemów ERP zadania remontowe dla urządzeń w myśl metodologii „Planned Maintenance”; przypisano do poszczególnych lokalizacji funkcjonalnych pełną dokumentację urządzeń wchodzących w jej skład; określono i wdrożono podstawowe wymogi dokumentacyjne pozwalających na tworzenie baz danych prowadzonych remontów; w oparciu o stworzone lokalizacje funkcjonale określono krytyczności urządzeń oraz wprowadzono efektywne zarządzania magazynami części zamiennych i środkami eksploatacyjnymi w oparciu o analizę ryzyka. Praca zawiera prezentację wdrożenia na bazie systemu SAP PM, w której przedstawiono funkcjonujący schemat utrzymania ruchu wraz z wzorcami przetwarzania informacji i realizacji zawiadomień przeglądowych uwzględniającą dalsze postępowania w przypadku konieczności realizacji zawiadomienia awaryjnego, remontowego lub protokołu z dodatkowych prac. Badania nad funkcjonowaniem wdrożonego systemu w przedsiębiorstwie LOTOS Petrobaltic potwierdziły właściwe działanie systemu. Wdrożony system poprawił stan techniczny platformy Petrobaltic należącego do przedsiębiorstwa LOTOS Petrobaltic, zwiększając bezpieczeństwo oraz zapewniając lepszą dostępność techniczną instalacji, maszyn i urządzeń, zgodnie z istniejącymi ograniczeniami organizacyjnymi, założeniami technicznymi, ekonomicznymi co zostało potwierdzone wskaźnikami skuteczności procesu utrzymania ruchu. System funkcjonuje zgodnie z oczekiwaniami nieprzerwanie od 2021, a wszystkie założenia wdrożenia zostały zrealizowane.
Opracowanie metodologii rozpoznawania i klasyfikowania emocji w filmach przy użyciu sztucznych sieci neuronowych
- Dawid Weber
2024 Pełny tekst
Celem rozprawy doktorskiej jest opracowanie metodologii pozwalającej na rozpoznawanie i klasyfikację emocji w filmie za pomocą sztucznych sieci neuronowych. W pracy przedstawiono tematykę związaną z kolorowaniem sceny filmowej w kontekście oddziaływania koloru na emocje widza. W celu analizy wpływu filmow na emocje widza dokonano wyboru tytułow filmowych, następnie przeprowadzono szereg wstępnych testow subiektywnych pozwalających na wybor i potwierdzenie sześciokolorowego modelu emocji oraz przypisanie do danego fragmentu filmowego odpowiedniej etykiety emocji. Wyniki testow subiektywnych pozwoliły na przygotowanie bazy danych fragmentow filmow, ktorą następnie wykorzystano do treningu i testow modeli uczenia głębokiego. W drugiej części pracy przygotowano analizę sygnałow audio i wideo poprzez rożne sposoby parametryzacji tych sygnałow, a następnie dokonano klasyfikacji klas emocji na podstawie sygnału audio oraz wideo. Modele o najwyższej dokładności dla zbioru testowego zostały wybrane do stworzenia modelu multimodalnego. W trzeciej części pracy przygotowano model bimodalny wykorzystujący dwa wybrane wcześniej modele klasyfikacji sygnałow fonicznych oraz wideofonicznych. Model bimodalny wykazał się wyższą dokładnością podczas testow niż pojedynczy model klasyfikacji wideo, przy niewielkim koszcie wzrostu liczby parametrow modelu i stopnia skomplikowania.
Optical method supported by machine learning for dynamics of C‐reactive protein concentrations changes detection in biological matrix samples
- Patryk Sokołowski
- Kacper Cierpiak
- Małgorzata Szczerska
- Maciej Wróbel
- Aneta Łuczkiewicz
- Sylwia Fudala-Książek
- Paweł Wityk
2024 Pełny tekst Journal of Biophotonics
In this article we present the novel spectroscopy method supported with machine learning for real-time detection of infectious agents in wastewater. In the case of infectious diseases, wastewater monitoring can be used to detect the presence of inflammation biomarkers, such as the proposed C-reactive protein, for monitoring inflammatory conditions and mass screening during epidemics for early detection in communities of concern, such as hospitals, schools, and so on. The proposed spectroscopy method supported with machine learning for real-time detection of infectious agents will eliminate the need for time-consuming processes, which contribute to reducing costs. The spectra in range 220–750 nm were used for the study. We achieve accuracy of our prediction model up to 68% with using only absorption spectrophotometer and machine learning. The use of such a set makes the method universal, due to the possibility of using many different detectors.
Optimal shape and stress control of geometrically nonlinear structures with exact gradient with respect to the actuation inputs
- Ahmed Manguri
- Domenico Magisano
- Robert Jankowski
2024 Pełny tekst Structures
This paper presents an efficient and robust optimization methodology for stress and shape control of actuated geometrically nonlinear elastic structures, applied to 3D trusses. The actuation inputs, modeled as prescribed strains, serve as the optimization variables. The objective is to minimize total actuation while satisfying several constraints: (i) actuation bounds in each actuated element and (ii) target ranges for nodal displacements and element stresses. Optimizing large nonlinear structures is computationally intensive. While gradient-based methods typically converge faster than gradient-free ones, their main bottleneck lies in numerical gradient evaluation, requiring multiple time-consuming nonlinear structural analyses (finite differences) with inaccuracies that may slow down convergence. The novelty of the proposal is an implicit differentiation approach to quickly compute the exact gradient of the nonlinear finite element solution with respect to the actuation inputs. This is implemented within the structural solver and leverages the already factorized tangent stiffness matrix to make the gradient cost negligible. As a result, the number of structural analyses and overall optimization time are significantly reduced.
Optimisation of the Energy Consumption of a Small Passenger Ferry with Hybrid Propulsion
- Magdalena Kunicka
2024 Pełny tekst Polish Maritime Research
The main goal in the design phase is to create a safe ship with a very efficient (and preferably zero-emission) propulsion system. To obtain such ships, new concepts are being developed for both propulsion systems and individual components. The choice of a propulsion system is not straightforward. To optimise the selection of the propulsion system, it is valuable to optimise the energy demand of this unit, which can be done by creating operational movement profiles that indicate the differences in energy demand needed to cover the same route within similar times. Optimisation can be performed based on many different criteria, especially for crowded waterways, and can not only reduce the amount of energy needed to power the propulsion system but also increase navigational safety. In this work, optimisation is carried out by searching the space of all possible solutions, which allows for an in-depth analysis according to various criteria.
Optimising Sequencing Batch Reactor Operation Cycle Planning Using Evolutionary Algorithm
- Tomasz Ujazdowski
- Robert Piotrowski
2024
The objective of this research was to optimise the operation cycle of the Sequencing Batch Reactor (SBR). Appropriate time balances of aerobic to anaerobic phases, as well as a set dissolved oxygen level are the key to ensuring the quality of effluent from the wastewater treatment process. The proposal to solve this optimisation problem was based on multi-objective optimisation using an evolutionary multi-objective optimisation algorithm called ϵv-MOGA. Three indicators of effluent quality and a process cost factor were adopted as functions to be optimised. The results were tested using multi-level control system of a complex SBR simulation model. The research is based on a case study of the Water Resource Recovery Facility (WRRF) in Swarzewo, Northern Poland.
Optimization of Division and Reconfiguration Locations of the Medium-Voltage Power Grid Based on Forecasting the Level of Load and Generation from Renewable Energy Sources
- Karol Sidor
- Piotr Miller
- Robert Małkowski
- Michał Izdebski
2024 ENERGIES
The article addresses challenges in optimizing the operation of medium voltage networks, emphasizing optimizing network division points and selecting the best network configuration for minimizing power and energy losses. It critically reviews recent research on the issue of network configuration optimization. The optimization of the medium voltage power grid reconfiguration process was carried out using known optimization tools. The novelty lies in the inclusion of a probabilistic approach in the decision-making process in forecasting loads and generation from renewable energy sources (RES). Optimization studies utilizing heuristic optimization methods were completed, and an algorithm was developed for forecasting load and power generated from RES based on historical data and current weather data obtained from weather API. The solution proposed in the article allows multiple applications, including optimizing network division points’ locations (which decreases financial costs of modernizing network infrastructure) as well as improving the reconfiguration process, resulting in lower power losses while maintaining voltage requirements.
Optimization of Hydrogen Utilization and Process Efficiency in the Direct Reduction of Iron Oxide Pellets: A Comprehensive Analysis of Processing Parameters and Pellet Composition
- Angelo Perrone
- Pasquale Cavaliere
- Behzad Sadeghi
- Leandro Dijon
- Aleksandra Mirowska
2024 Pełny tekst STEEL RESEARCH INTERNATIONAL
The article deals with the H2 consumption for different processing conditions and the composition of the processed pellets during the direct reduction process. The experiments are carried out at 600–1300 °C, with gas pressures of 1–5 bar, gas flow rates of 1–5 L min−1, and basicity indices of 0 to 2.15. Pellets with different compositions of TiO2, Al2O3, CaO, and SiO2 are analyzed. The gas flow rate is crucial, with 0–10 L min−1 leading to an H2 consumption of 0–5.1 kg H2/kg pellet. The gas pressure (0–10 bar) increases the H2 consumption from 0 to 5.1 kg H2/kg pellet. Higher temperatures (600–1300 °C) reduce H2 consumption from 5.1 to 0 kg H2/kg pellet, most efficiently at 950–1050 °C, where it decreases from 0.22 to 0.10 kg H2/kg pellet. An increase in TiO2 content from 0% to 0.92% lowers H2 consumption from 0.22 to 0.10 kg H2/kg pellet, while a higher Fe content (61–67.5%) also reduces it. An increase in SiO2 content from 0% to 3% increases H2 consumption from 0 to 5.1 kg H2/kg pellet. Porosity structure influences H2 consumption, with the average pore size decreasing from 2.83 to 0.436 mm with increasing TiO2 content, suggesting that micropores increase H2 consumption and macropores decrease it.
Optimization of Microwave Components Using Machine Learning and Rapid Sensitivity Analysis
- Sławomir Kozieł
- Anna Pietrenko-Dąbrowska
2024 Pełny tekst Scientific Reports
Recent years have witnessed a tremendous popularity growth of optimization methods in high-frequency electronics, including microwave design. With the increasing complexity of passive microwave components, meticulous tuning of their geometry parameters has become imperative to fulfill demands imposed by the diverse application areas. More and more often, achieving the best possible performance requires global optimization. Unfortunately, global search is an intricate undertaking. To begin with, reliable assessment of microwave components involves electromagnetic (EM) analysis entailing significant CPU expenses. On the other hand, the most widely used nature-inspired algorithms require large numbers of system simulations to yield a satisfactory design. The associated costs are impractically high if not prohibitive. The use of available mitigation methods, primarily surrogate-based approaches, is impeded by dimensionality-related problems and the complexity in microwave circuit characteristics. This research introduces a procedure for expedited globalized parameter adjustment of microwave passives. The search process is embedded in a surrogate-assisted machine learning framework that operates in a dimensionality-restricted domain, spanned by the parameter space directions being of importance in terms of their effects on the circuit characteristic variability. These directions are established using a fast global sensitivity analysis procedure developed for this purpose. Domain confinement reduces the cost of surrogate model establishment and improves its predictive power. The global optimization phase is complemented by local tuning. Verification experiments demonstrate the remarkable efficacy of the presented approach and its advantages over the benchmark methods that include machine learning in full-dimensionality space and population-based metaheuristics.
Optimization of vortex-assisted supramolecular solvent-based liquid liquid microextraction for the determination of mercury in real water and food samples
- Muhammad Farooque Lanjwani
- Adil Elik
- Ayşenur Öztürk Altunay
- Mustafa Tuzen
- Hameed Haq
- Grzegorz Boczkaj
2024 JOURNAL OF FOOD COMPOSITION AND ANALYSIS
A novel method was developed for sample preparation for spectrophotometric determination of Hg(II) in water and food samples. The method was based on vortex-assisted supramolecular solvent-assisted liquid-liquid microextraction (VA-SUPRASs-LLME). Analytical parameters such as pH, chelating agent, solvent type and volume, vortex time and salting out effect were optimized. Surface and normal probability plots were drawn for the variables using the optimization data. Microwave-assisted digestion of samples was performed before the extraction procedure. L-cysteine was found to be more effective as a ligand for Hg(II). Five different SUPRASs were prepared and used for the extraction of Hg(II). A 1-decylamine/thymol/water at a 1:2:1 molar ratio assisted by a salting effect was found most effective for optimal extraction. Limits of detection and limit of quantification were found 0.6 μg L−1 and 2.0 μg L−1 with a very good linearity range of 2–350 μg L−1. Intra-day and inter-day precisions were in the range of 1.8–4.0 % with a preconcentration factor 150. The accuracy of the method estimated by analysis of certified reference materials was 96–98.5 %. Finally, the new method was used for the determination of Hg(II) in real water, food samples, and certified reference materials (NIST, IAEQ/W-4(simulated freshwater), and DORM-4; fish protein).
Optimization-based stacked machine-learning method for seismic probability and risk assessment of reinforced concrete shear walls
- Farzin Kazemi
- Neda Asgarkhani
- Robert Jankowski
2024 EXPERT SYSTEMS WITH APPLICATIONS
Efficient seismic risk assessment aids decision-makers in formulating citywide risk mitigation plans, providing insights into building performance and retrofitting costs. The complexity of modeling, analysis, and post-processing of the results makes it hard to fast-track the seismic probabilities, and there is a need to optimize the computational time. This research addresses seismic probability and risk assessment of reinforced concrete shear walls (RCSWs) by introducing stacked machine learning (Stacked ML) models based on Bayesian optimization (BO), genetic algorithm (GA), particle swarm optimization (PSO), and gradient-based optimization (GBO) algorithms. The study investigates 4-, to 15-Story RCSWs assuming different bay lengths and soil types to build a comprehensive database based on the incremental dynamic analysis (IDA) subjected to 56 near-field pulse-like and no-pulse records. Having 227,200 and 63,384 data points for a median of IDA curve (MIDA) and seismic probability curve, respectively, the proposed Stacked ML models have shown good performance on curve fitting ability by accuracy of 99.1% and 99.4% for MIDA and seismic fragility curves, respectively. In addition, the proposed models can estimate the mean annual frequency, λ, which is a key parameter in seismic risk assessment of buildings. To provide the results of the study for general buildings, a user-friendly GUI is proposed that facilitates result utilization, offering insights into seismic performance levels, providing the estimated MIDA and seismic failure probability curves, and mean annual frequency calculations for specific performance levels and seismic hazard curves.
Optimized Protocol for RNA Isolation from Penicillium spp. and Aspergillus fumigatus Strains
- Aleksandra Siniecka-Kotula
- Martyna Mroczyńska-Szeląg
- Anna Brillowska-Dąbrowska
- Lucyna Holec-Gąsior
2024 CURRENT ISSUES IN MOLECULAR BIOLOGY
Efficient RNA isolation from filamentous fungi is crucial for gene expression studies, but it poses significant technical challenges due to the robust cell walls and susceptibility of RNA to degradation by ribonucleases. This study presents the effectiveness of two RNA isolation protocols for four species of filamentous fungi: Penicillium crustosum, Penicillium rubens, Penicillium griseofulvum, and Aspergillus fumigatus. Both protocols utilized Fenzol Plus for cell lysis but varied in the mechanical disruption methods: bead-beating versus manual vortexing. The results show that the bead-beater method (Protocol 1) yielded significantly higher RNA quantities, with better purity and integrity, as demonstrated by higher A260/A280 and A260/A230 ratios. RNA concentrations ranged from 30 to 96 µg/g of dry biomass in Penicillium species and up to 52 µg/g in A. fumigatus. The use of chloroform in Protocol 1 also enhanced RNA purity, effectively separating contaminants such as DNA, proteins, and polysaccharides. This optimized protocol is highly efficient and can be applied in routine laboratories handling large numbers of fungal samples, making it a robust method for downstream applications such as cDNA synthesis and transcriptome analysis.
Optimizing CO2 Purification in a Negative CO2 Emission Power Plant
- Milad Amiri
- Jaroslaw Mikielewicz
- Paweł Ziółkowski
- Dariusz Mikielewicz
2024 CHEMICAL ENGINEERING & TECHNOLOGY
In the pursuit of mitigating CO2 emissions, this study investigates the optimisation of CO2 purification within a Negative CO2 Emission Power Plant using a spray ejector condenser (SEC) coupled with a separator. The approach involves direct-contact condensation of vapour, primarily composed of an inert gas (CO2), facilitated by a subcooled liquid spray. A comprehensive analysis is presented, employing a numerical model to simulate a cyclone separator under various SEC outlet conditions. Methodologically, the simulation, conducted in Fluent, encompasses three-dimensional, transient, and turbulent characteristics using the Reynolds Stress Model (RSM) turbulent model and mixture model to replicate the turbulent two-phase flow within a gas-liquid separator. Structural considerations are delved into, evaluating the efficacy of single and dual inlet separators to enhance CO2 purification efficiency. The study reveals significant insights into the optimisation process, highlighting a notable enhancement in separation efficiency within the dual inlet cyclone compared to its single inlet counterpart. Specifically, a 90.7% separation efficiency is observed in the former, characterised by symmetrical flow patterns devoid of wavering CO2 cores, whereas the latter exhibits less desirable velocity vectors. Furthermore, the investigation explores the influence of key parameters, such as liquid volume fraction (LVF) and water droplet diameter, on separation efficiency. It is ascertained that a 10% LVF with a water droplet diameter of 10 μm yields the highest separation efficiency at 90.7%, whereas a 20% LVF with a water droplet diameter of 1 μm results in a reduced efficiency of 50.79%. Moreover, the impact of structural modifications, such as the addition of vanes, on separation efficiency and pressure drop is explored. Remarkably, the incorporation of vanes leads to a 9.2% improvement in separation efficiency and a 16.8% reduction in pressure drop at a 10% LVF. The findings underscore the significance of structural considerations and parameter optimisation in advancing CO2 capture technologies, with implications for sustainable energy production and environmental conservation.