16168 research outputs found
Sort by
Gene sco1417 encodes a positive regulator of the de novo biosynthesis of pyridoxal 5'-phosphate (vitamin B6) in Streptomyces coelicolor M145 /
No full textBACKGROUND: Actinomycetes of the genus Streptomyces are renowned for their highly developed and diverse specialized metaboliс pathways, and there is an extensive body of data on their specific and pleiotropic levels of regulation. Much less is known about routes leading to essential metabolites in this genus. In this work, we focused on elucidating the function of the highly conserved across Streptomyces gene SCO1417 for GntR type regulator in the model strain S. coelicolor A3(2).
RESULTS: Combining the results of knockout and promoter probe experiments, we show that the gene SCO1417 controls pyridoxal 5'-phosphate (PLP; vitamin B6) biosynthesis, and thus is a member of the PdxR group of the transcriptional regulators. The Sco1417 protein is a transcriptional repressor of its gene and an activator of the expression of the PLP synthase genes, SCO1523 (pdxS) and SCO1522 (pdxT). According to electrophoretic mobility shift assays, out of several tested B6 vitamers, only PLP served as a Sco1417 effector molecule. We also provide data on the location of the Sco1417 binding site within the promoter region of pdxST.
CONCLUSIONS: Our work portrays for the first time an evidence-based picture of the genetic control of vitamin B6 biosynthesis in S. coelicolor M145. Given the high conservancy and synteny of pdx homologs in the other streptomycetes, we suggest that the described genetic circuit is a general feature for the entire genus
A dynamic analysis of a cantilever piezoelectric vibration energy harvester with maximized electric polarization due to the optimal shape of the thickness for first eigen frequency /
No full textThis study presents an analytical and experimental approach to enhance cantilever-based piezoelectric energy harvesters by optimizing thickness distribution. Using a gradient projection algorithm within a state-space framework, the unimorph beam’s geometry is tailored while constraining the first natural frequency. The objective is to amplify axial strain within the piezoelectric layers, thereby increasing electric polarization and maximizing the conversion efficiency of mechanical vibrations into electrical energy. The steady-state response under harmonic base excitation at resonance was modeled to evaluate the harvester’s dynamic behavior against uniform-thickness counterparts. Results show that the optimized beam achieves significantly higher output voltage and energy harvesting efficiency. Simulations reveal effective strain concentration in regions of high piezoelectric sensitivity, enhancing power generation under resonant conditions. Two independent experimental setups were employed for empirical validation: a non-contact laser vibrometry system (Polytec 3D) and a first resonant base excitation setup. Eigenfrequencies matched within 5% using a Polytec multipath interferometry system, and constant excitation tests showed approximately 30% higher in optimal shapes electrical potential value generation. The outcome of this study highlights the efficacy of geometric tailoring—specifically, non-linear thickness shaping—as a key strategy in achieving enhanced energy output from piezoelectric harvesters operating at their fundamental frequency. This work establishes a practical route for optimizing unimorph structures in real-world applications requiring efficient energy capture from low-frequency ambient vibrations
Investigation of the influence of filter approximation on the performance of reactive power compensators in railway traction drive systems /
No full textIn reactive power compensators applied in drives with asynchronous motors, a control strategy focusing on the compensation of higher-order current harmonics is implemented. Control schemes of such compensators typically employ low-pass Butterworth filters with fixed cut-off frequencies to isolate the reactive power component. However, the impact of alternative filter types on compensator performance remains insufficiently explored. Furthermore, in the control systems under consideration, stator phase current signals of the asynchronous motor are used as reference inputs. This approach proves effective under the steady-state operating conditions of the drive. Under non-steady-state operating conditions—typical for traction drive systems—this approach becomes ineffective due to the increased complexity in obtaining accurate reference current signals. As a result, the performance of the filters also deteriorates. It is therefore proposed to investigate the impact of alternative filter types on the efficiency of compensator operation. To address this challenge, the following strategies are suggested: implement higher-order harmonic compensation in the system of stator phase supply voltages of the asynchronous motor; use the control signals from the Field-Oriented Control (FOC) algorithm as reference inputs; and adapt the cut-off frequencies of the filters dynamically to match the frequency of the supply voltage. The simulation results indicate that the use of an elliptic filter in compensator control systems yielded the highest effectiveness. Moreover, the results confirmed the efficiency of the proposed solutions under both steady-state and non-steady-state operating conditions of the traction drive. These approaches support the development of reactive power compensators integrated into traction drive systems for railway rolling stock
The evaluation of bonding quality through multidimensional data fusion /
No full textAdhesive bonded structures have attracted significant interest from various industries including those involved in transportation such as the aerospace, rail, marine, and automotive. due to their advantageous properties. Adhesives possess the capability to join complex structures and dissimilar materials, distribute load homogenously by offering high strength-to-weight ratio. However, the use of adhesive bonds is constrained by the absence of reliable techniques for their non-destructive evaluation. The aim of this study is to enhance the reliability of nondestructive testing of adhesive joints by means of the multidimensional data fusion of the ultrasonic and radiographic data to broaden their application areas. Data fusion can be defined as a process of combining data from various sources to generate more complete and accurate data thereby improving accuracy. In this study adhesive joints featuring various types of bonding defects were investigated employing radiography and conventional pulse-echo ultrasonic techniques. Subsequently, a data fusion was implemented integrating the data acquired by different techniques. The investigation also involved the development and refinement of advanced data processing techniques, particularly designed for data fusion applications. The work highlights the necessity of a comprehensive non-destructive evaluation to assess the quality of the adhesive bonds. Consequently, the application of multi-dimensional data fusion of radiographic and ultrasonic data has yielded more comprehensive results
Production technology for polyethylene terephthalate film by coextrusion.
No full textThe aim of this Master’s thesis is to design a production facility for polyethylene terephthalate (PET) film manufacturing based on the co-extrusion process, taking into account modern engineering and environmental solutions that ensure high product quality, efficient raw material utilization, and the integration of recycled materials into the production cycle. The project consists of a literature review, research section, engineering part, and an assessment of occupational health and safety. The proposed PET film production line is based on the A–B–A three-layer co-extrusion principle, employing a combination of various raw materials including virgin PET, mechanically recycled PET, and functional additives (plasticizer AB and colorants). In order to reduce environmental impact, the technology allows the incorporation of up to 60% recycled PET. The system includes solutions for direct melt dehumidification inside the extruder using vacuum pumps, thereby eliminating the need for a separate pellet drying stage. A closed-loop cooling system for the calender is implemented to reduce waste generation during the cooling process. The research section analyses the properties of different raw materials virgin PET, recycled PET, and functional additives and their influence on the mechanical and optical properties of the final film. The effect of the plasticizer AB on the film's coefficient of friction is also evaluated. In the engineering section, the production technology and co-extrusion equipment are selected, along with calculations of equipment parameters and the material balance. The required line throughput, material flows, raw material feeding, and cooling solutions are assessed. Construction solutions for the PET film production facility are selected, and both economic and financial assessments of the project are performed. The environmental part presents physical and atmospheric air pollution calculations, the amount and management of generated waste, and total electricity consumption. Occupational risk assessment is conducted, and fire safety solutions are proposed, including an evacuation plan, signage systems, and fire extinguishing equipment. The graphical section includes five drawings: the layout of the PET film production building with equipment arrangement, a process flow diagram, a side view of the production line within the facility, a rear view of the technological line, and the site layout plan
Environmental life cycle assessment of recycling natural fiber textile into biochar.
No full textThis thesis evaluates the production of biochar from natural fiber textile waste (textile biochar – TB) as a strategy for climate change mitigation and sustainable textile waste management. To assess the feasibility and potential environmental benefits of this strategy, a TB production experiment was conducted, and a life cycle assessment (LCA) was applied in accordance with ISO 14040, ISO 14044 and EN 15804 standards. The LCA was carried out using the “Ecochain Helix” software, and the carbon sequestration potential of the biochar was estimated based on the “Puro.earth” methodology. Three scenarios were compared in the study: (1) a baseline scenario in which the natural fiber textile product is incinerated for energy recovery at the end of its life cycle; (2) an innovative scenario where the same product is converted into biochar; and (3) a business-as-usual scenario involving conventional raw materials, production conditions, and waste management practices. The results showed that the second biochar scenario had the lowest climate change impact. Moreover, by replacing incineration with biochar conversion, the end-of-life stage shifted from having a negative to a positive environmental impact. The produced biochar was found to meet the requirements of the European Biochar Certificate (EBC) and can be used as a soil amendment and long-term carbon sink. Before being applied to soil, it can also serve as a component in various industrial products, and companies producing biochar may participate in the carbon credit market. This study demonstrates that recycling natural fiber textile waste into biochar can not only significantly contribute to more sustainable textile waste management, but also help achieve broader goals related to ecosystem restoration, the circular economy and climate change mitigation
Biologiškai skaidžios popieriaus pagrindo pakuotės medžiagos su skaidumą skatinančiais priedais sukūrimas žiedinės ekonomikos kontekste.
No full textConsidering the increasing environmental requirements and the need to reduce the use of fossil-based plastics and packaging waste, this dissertation focused on the creation of a biodegradable material for single-use fast food packaging that complies with the principles of the circular economy. Initially, the criteria for environmental performance and integration of single-use packaging into the circular economy were defined, taking into account the origin of raw materials, their recyclability, and biodegradability. Optimal alternative packaging raw materials were identified, including primary wood pulp, grain processing by-products, and a Saccharomyces cerevisiae additive. During the experimental phase, laboratory-scale composites with various filler concentrations and yeast additive were developed. These composites were subjected to physical–mechanical, barrier, and biodegradation property assessments. The findings demonstrated that up to 40% of primary raw materials can be replaced with selected alternative fillers – wheat bran and wheat grain production residues – without compromising the functional characteristics of the material. It was also determined that the Saccharomyces cerevisiae additive increased the hydrophobicity of the material, indicating its potential for use in bio-based barrier coatings. Furthermore, both the yeast additive and selected fillers enhanced the biodegradability of the material under aerobic and anaerobic conditions. The research confirmed that the developed material potentially meets the functional requirements for fast food packaging and is suitable for biological recycling
Multimodal convolutional mixer for mild cognitive impairment detection /
No full textBrain imaging is important in detecting Mild Cognitive Impairment (MCI) and related dementias. Magnetic Resonance Imaging (MRI) provides structural insights, while Positron Emission Tomography (PET) evaluates metabolic activity, aiding in the identification of dementia-related pathologies. This study integrates multiple data modalities—T1-weighted MRI, Pittsburgh Compound B (PiB) PET scans, cognitive assessments such as Mini-Mental State Examination (MMSE), Clinical Dementia Rating (CDR) and Functional Activities Questionnaire (FAQ), blood pressure parameters, and demographic data—to improve MCI detection. The proposed improved Convolutional Mixer architecture, incorporating B-cos modules, multi-head self-attention, and a custom classifier, achieves a classification accuracy of 96.3% on the Mayo Clinic Study of Aging (MCSA) dataset (sagittal plane), outperforming state-of-the-art models by 5%–20%. On the full dataset, the model maintains a high accuracy of 94.9%, with sensitivity and specificity reaching 89.1% and 98.3%, respectively. Extensive evaluations across different imaging planes confirm that the sagittal plane offers the highest diagnostic performance, followed by axial and coronal planes. Feature visualization highlights contributions from central brain structures and lateral ventricles in differentiating MCI from cognitively normal subjects. These results demonstrate that the proposed multimodal deep learning approach improves accuracy and interpretability in MCI detection
Surface corrosion detection for ferrous-metal parts: application of artificial intelligence, python and microscopic images /
No full textThis paper presents a novel method for the identification of surface damage, in particular corrosion, in ferrous metals based on generative artificial intelligence (GenAI), showing how to automate damage identification and corrosion recognition. The methodology involved using optical microscopy to capture electrochemical corrosion patterns, followed by image preprocessing and classification using AI algorithms implemented in Python. High-quality microscopic images have been recorded, based on selected ferrous metals. Python code lines were generated using ChatGPTTM based on queries created by the authors, and this method was applied to the corrosion analysis. Quantitative evaluation confirmed Python code parameters-dependent detection accuracy and repeatability, demonstrating the robustness of the proposed technique. The results were discussed in terms of possible industrial applications. In addition, the limitations of the results obtained, which sometimes fall short of the claims inspector's expectations, were discussed. Compared to traditional corrosion detection methods such as visual inspection and non-destructive testing, AI-based methods are a faster and more cost-effective solution that can process large volumes of images in real time and produce consistent results. Further research directions are also suggested, including the analysis of other types of damage and improving the accuracy of the model. In addition to technical efficiencies, the broader impact of these studies is that they can contribute to predictive maintenance, reduce downtime and improve safety in industries with high ferrous metal use
Anomaly detection and removal strategies for in-line permittivity sensor signal used in bioprocesses /
No full textIntroduction: In-line sensors, which are crucial for real-time (bio-) process monitoring, can suffer from anomalies. These signal spikes and shifts compromise process control. Due to the dynamic and non-stationary nature of bioprocess signals, addressing these issues requires specialized preprocessing. However, existing anomaly detection methods often fail for real-time applications. Methods: This study addresses a common yet critical issue: developing a robust and easy-to-implement algorithm for real-time anomaly detection and removal for in-line permittivity sensor measurement. Recombinant Pichia pastoris cultivations served as a case study. Trivial approaches, such as moving average filtering, do not adequately capture the complexity of the problem. However, our method provides a structured solution through three consecutive steps: 1) Signal preprocessing to reduce noise and eliminate context dependency; 2) Anomaly detection using threshold-based identification; 3) Validation and removal of identified anomalies. Results and discussion: We demonstrate that our approach effectively detects and removes anomalies by compensating signal shift value, while remaining computationally efficient and practical for real-time use. It achieves an F1-score of 0.79 with a static threshold of 1.06 pF/cm and a double rolling aggregate transformer using window sizes w1 = 1 and w2 = 15. This flexible and scalable algorithm has the potential to bridge a crucial gap in process real-time analytics and control