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Research of the impact of changes in vehicle ground clearance on aerodynamics and fuel consumption.
No full textAs fuel prices continue to fluctuate and environmental regulations become increasingly strict, vehicle manufacturers and users are increasingly seeking ways to improve vehicle fuel economy while simultaneously reducing their environmental impact. Ground clearance (the vertical distance from the bottom of the vehicle to the road surface) is one of the many factors influencing aerodynamic drag and, consequently, fuel consumption. However, there is a notable lack of comprehensive studies in scientific literature that quantitatively evaluates the impact of ground clearance on fuel consumption under real-world operating conditions. The theoretical part of the master's thesis explores the principles of automotive aerodynamics, discussing airflow dynamics around a moving vehicle and analyzing the sources of aerodynamic drag and their influence on vehicle efficiency. Existing studies found in the literature, which investigate the effect of ground clearance on aerodynamic characteristics, are reviewed, and the methodology of Computational Fluid Dynamics (CFD) application, along with aerodynamic coefficient calculation processes, is detailed. The literature review identified that although aerodynamic elements, such as spoilers and diffusers, are commonly used in vehicle design, the influence of ground clearance is typically discussed only at a theoretical level. There remains a lack of experimental and numerical studies that would evaluate the direct impact of ground clearance on fuel consumption. In the research part of the thesis, a numerical analysis was conducted using SolidWorks and ANSYS Fluent software. A 3D model of the vehicle was created and examined with three different ground clearance configurations – 110 mm, 130 mm, and 150 mm – and at three speeds – 90, 110, and 130 km/h. After determining aerodynamic drag coefficients and drag forces, theoretical fuel consumption values were calculated by evaluating changes in vehicle resistance. The calculations showed that reducing ground clearance by 20 mm decreased the drag coefficient at all tested speeds. At 90 km/h, the drag coefficient decreased from 0.291 to 0.281 (around 3.4% reduction), at 110 km/h from 0.287 to 0.278 (around 3.1%), and at 130 km/h from 0.285 to 0.274 (around 3.9%). Larger differences were observed when reducing ground clearance by 40 mm (from 150 mm to 110 mm), with the drag coefficient reductions recorded at all speeds: from 0.291 to 0.269 at 90 km/h (around 7.6% reduction), from 0.287 to 0.266 at 110 km/h (around 7.3%), and from 0.285 to 0.262 at 130 km/h (around 8.1%). Based on the theoretical calculations, it was determined that a 20 mm reduction in ground clearance results in approximately a 1.6–2.2% decrease in fuel consumption, while a 40 mm reduction achieves approximately a 3.5–4.6% decrease. In the experimental part of the study, real-world tests were conducted using an external fuel supply system to precisely measure the amount of fuel consumed independently from the vehicle’s ECU readings. The experimental results confirmed the trends observed in the numerical analysis – as ground clearance decreases, fuel consumption also decreases. At a speed of 90 km/h and a ground clearance of 150 mm, a fuel consumption of 4.55 l/100 km was recorded, while at a clearance of 110 mm, consumption dropped to 4.18 l/100 km (approximately an 8% reduction). At higher speeds, the differences became even more pronounced: reducing the clearance by 40 mm (from 150 mm to 110 mm) at 130 km/h resulted in fuel consumption decreasing from 6.6 l/100 km to 6.0 l/100 km, approximately a 9% reduction. Furthermore, reducing ground clearance by 20 mm (from 150 mm to 130 mm) also showed a noticeable 3–5% reduction in fuel consumption across all tested speeds: from 4.55 to 4.38 l/100 km at 90 km/h (approximately 3.7%), from 5.3 to 5.1 l/100 km at 110 km/h (approximately 3.8%), and from 6.6 to 6.28 l/100 km at 130 km/h (approximately 4.8%). These findings confirm that even a 20 mm reduction in ground clearance can save 3–5% in fuel consumption, while a 40 mm reduction results in savings exceeding 8–9%, especially at higher speeds. It was also noted that although experimental results were slightly higher due to environmental factors, the overall consistency between the numerical and experimental results demonstrates strong correlation between the two methods. The last part of the paper compares the results of experimental tests and theoretical calculations and makes recommendations. It is noted that although the experimental results are slightly higher, the overall consistency between the calculations and the experimental results shows a good correlation between the two methods. This confirms that as the ground clearance decreases, the fuel consumption of vehicles decreases
Radiomikos darbo eigos kūrimas galvos ir kaklo vėžiu sergančių pacientų prognozavimo modeliams.
No full textMedical physicists play an important role in integrating imaging, treatment planning, and therapy monitoring into cancer care. With the increased availability of high-resolution medical imaging and advanced computational tools, radiomics has emerged as a potent non-invasive method for quantifying tumor features. Delta radiomics, which examines changes in imaging features during or after therapy, sheds light on treatment-induced biological impacts. However, the practical application of delta radiomics in ordinary clinical workflows is still limited, primarily due to the complexity of data processing, diversity in radiomic feature extraction procedures, and the lack of defined, clinically validated implementation paths. The aim of this master's thesis was to create and test a reproducible delta radiomics-based process that would assist medical physicists in prognostic modelling for patients with head and neck cancer. Rather than focusing solely on prediction accuracy, the goal was to develop a simple and adaptable approach for clinical usage. In this work, pre- and post-treatment medical imaging data were used to identify delta radiomic features to verify the performance of the developed workflow. Two independent machine learning models were created: one to determine which medication causes the greatest radiomic alterations and another to discover variables linked with patient survival. These modelling tasks not only revealed the workflow's power to recognize clinically meaningful patterns but also emphasized its potential to discover non-invasive imaging biomarkers for treatment monitoring and survival prediction. The developed workflow includes key steps, such as balancing the dataset using the synthetic oversampling method SMOTE, correlation analysis to reduce over-sampling and feature selection using recursive feature removal with Random Forest and XGBoost and evaluating the performance of these methods. The CatBoost method is then used to build classification models based on the given features. Finally, Mann-Whitney U, Kruskal-Wallis and Dunn post hoc tests are used to assess the statistical significance and discriminatory power of the features included in the final models. This thesis advances medical physics by proposing a robust, useable delta radiomics methodology for developing imaging-based prognostic models. It emphasizes the critical role of medical physicists in enabling data-driven, individualized treatment evaluation and response assessment. By increasing the incorporation of quantitative imaging biomarkers into clinical radiation workflows, this study adds to the ongoing enhancement of customized oncology care and the employment of AI techniques in routine clinical practice
Abrazyvinio vandens srove atliekamo apdirbimo parametrų įtakos pluoštu armuotiems epoksidiniams kompozitams tyrimas.
No full textToday, there are advanced modern cutting methods available on the market for a wide range of materials, which help industrial companies to solve many material processing problems, such as very high cutting precision, designing more complex profiles, significantly reducing production time, and minimizing the amount of waste generated during production. Various composite materials are also now widely used due to the excellent properties of composite materials, such as flexibility in design, low density, high strength, and long-term durability. There is also a wide range of processing techniques for composite materials. Of these methods, abrasive water jet machining is one of the most popular and best methods offered by researchers due to the quality of the output it provides. This project presents a study of abrasive water jet machining (AWJM) of four types of fibre reinforced polymer composites, such as basalt, glass, carbon, and aramid. The abrasive water jet cut and texture quality of these fibre-reinforced composite laminates have been investigated by analysing various parameters such as surface roughness values, kerf width, and angle. The results of the experiment confirm that cutting composite materials using AWJM ensures high quality and precision. The influence of two different feed speeds (fine 50 mm/s and coarse 70 mm/s) on cutting quality was investigated and showed good machining results for all composite materials. With the exception of the carbon fibre reinforced epoxy composite, delamination was observed in all other fibre reinforced epoxy composites, which is very common in machining composites. From the cutting angles and surface roughness values, it was found that fine cutting provides better quality than coarse cutting. The analysis of the texturing results showed that only carbon and aramid fibre reinforced epoxy resin composites could be textured using predefined machining parameters. The cutting cost calculations show that coarse cutting is much more economical than fine cutting. This can reduce the environmental impact by reducing operating costs and waste after the machining process
Finansinių tendencijų vertinimui skirto prognozavimo modelio tyrimas.
No full textIn this thesis, chart pattern influence on the GA-LSTM-CNN model is investigated. Chart patterns are a branch of technical analysis that uses stock price movement shapes or candlesticks to predict future price movement. The GA-LSTM-CNN model was selected due to its combination of several neural network algorithms that achieve self-structuring, temporal and spatial pattern recognition. As it is not clear which features should be chosen for stock trading due to numerous trading strategies, a genetic algorithm provides an automatic search for features that best fit the use case. LSTM and CNN combinations provide the model with temporal and spatial pattern recognition capabilities, respectively. That is needed in stock trading, as patterns exist between features and in time for the same feature. Before the preliminary tests were conducted, chart patterns were selected from financial data using analytical rules outlined in the analysed research paper. Searching of chart patterns was done backwards to not give the model any knowledge of financial events in the future. Preliminary tests showed that the model selected chart patterns as one of the features. The results were similar to those of the model without chart patterns. Final tests revealed that chart patterns do not have a significant impact on individual model performance, but could be useful in an ensemble scenario. This thesis is separated into seven sections. The first section is a brief introduction to the problem, object of experiments and experimental steps taken. The second section is state-of-the-art research, where 29 sources are analysed to get a comprehensive overview of automated stock market trading. The third section contains explanations of used methods, models and metrics. The fourth section has descriptions of the training and testing environments, data used for experiments. The fifth section has comparison of model testing results. The sixth section is a discussion on problems encountered in the experiments, successes and future work. Finally, conclusion is given on model performances in the last section. In total, this work contains 41 figures and 53 pages
Visualising mathematics problems to improve learning results.
No full textMathematics develop critical thinking skills, solving real-world problems and contributing to scientific and technological progress. Developing critical thinking and problem-solving skills is particularly relevant in today's ever-changing world, and mathematical thinking also influences the way in which people deal with the tasks and challenges of their personal and professional lives. However, pupils' mathematical performance is poor, and they struggle to understand or solve mathematical problems and apply mathematical concepts to real-life situations. In order to find out what students' needs are, a survey was carried out and it was found that the most difficult problems for students are word problems, equations, inequalities and geometry problems. They also think that visualisations make lessons more interesting, enrich the learning material and ease the assimilation of new information. For the development of the visualisations, different technologies were analysed and Manim, for animations, and GeoGebra, for interactive tasks, were chosen as the tools with which a total of 50 visualisations were realized. The effectiveness of the animations and interactive tasks developed was subsequently tested with pupils in Year 10. Testing showed a positive change in mathematics results after diagnostic and summative evaluations
Optimizing the portfolio of Lithuanian pension funds by investing in real estate investments.
No full textThe global real estate market, estimated to be worth $379 trillion in 2023, surpassed the market cap of traditional equity markets, and its under-inclusion in pension fund strategies may be a missed opportunity. This paper analyses the opportunity to optimize pension fund investment portfolio by including REIT funds as an alternative asset class. Given the long-term investment strategy of pension funds and the growing need to diversify portfolios, the paper examines how real estate integration can affect the return and risk of the investment portfolio of the Lithuanian pension fund GoIndex. Based on a literature review and empirical research, the characteristics of real estate investments are discussed. The aim of the work is to determine whether the inclusion of real estate in an investment portfolio improves its risk-return ratio. To achieve this goal, a correlation analysis of portfolio assets and comparisons of statistical characteristics are performed. 3 different optimization methods are used to optimize portfolios in order to assess the weight each of them will assign to the added real estate class. At the end of the study, a portfolio sensitivity analysis is performed and Monte Carlo simulations are performed for one year in advance in order to see the behavior of portfolios under various scenarios. The conclusions present whether exchange-traded real estate funds can be included in pension fund investment portfolios as a means of optimizing their structure in order to improve the risk-return ratio
Application of sustainable engineering principles in heavy metal remediation.
No full textThe study analyzed the bioaccumulation capacity of the protozoan Tetrahymena thermophila in response to various heavy metal ion concentrations (Zn²⁺, Mo⁶⁺, Cu²⁺, Cr³⁺, Cr⁶⁺, Ni²⁺, Cd²⁺, Pb²⁺). It was determined that under toxic conditions, the protozoa modify their metabolism, exhibit altered movement patterns, change their ion-binding capacity, and display shifts in growth dynamics. A selective breeding method was investigated; however, due to the transmission of toxicity to daughter cells, this approach was abandoned and genetic modification is suggested as an alternative in all future studies. During the study, a filter made from textile waste was developed. Although not biodegradable, the filter demonstrates sustainable secondary use and is intended for disposal through incineration. It was successfully applied for the immobilization of Tetrahymena thermophila and the practical execution of the bioaccumulation process. The sustainability of the filter was supported by the use of Spirulina algae as the primary energy source. This work incorporates strong elements of multidisciplinarity and innovation, opening opportunities for the development of sustainable, passivefiltration-based biotechnological solutions for the removal of heavy metals from the environment
A twisted double donor in donor–acceptor–donor D2–D1–A–D1–D2 type emitters yields multicomponent charge-transfer emission /
No full textComplex donor arms in thermally-activated delay fluorescence (TADF) molecules can potentially provide additional options for charge-transfer (CT) emission through higher twisting disorder, leading to broader emission spectra. Here we design novel TADF emitters with double twisted donor moieties and show that a structural complication of the carbazole-based donor arms by changing the molecular structure from D-A-D to D2-D1-A-D1-D2 yields a transition from a dual to a triple emission band with an additional CT emission component, providing a corresponding red shift and increasing low-energy emission contribution. The revealed relationship between increased complexity of the donor moiety and the multicomponent CT emission band in the D-A-D structures provides a clue for design of TADF emitters with extended emission spectra
Analysis of the financial stability of the Lithuanian pension system.
No full textThe rapidly ageing population structure, long-term emigration and low birth rates are putting increasing pressure on the financial sustainability of the Lithuanian pension system. This Master's thesis analyses the demographic and economic factors affecting the sustainability of the pension system and assesses possible policy options to ensure the long-term stability of the system. The unfavourable demographic structure will lead to a declining working-age population and a steady increase in the proportion of pensioners in society. At the same time, increasing life expectancy will extend the duration of pension payments, which will increase the social security costs for old-age pensions. In the VK scenario, these costs are projected to reach 9,4 % of GDP in 2050, of which as much as 8,2 % will be for old-age pensions. This trend will significantly increase the pressure on public finances and threaten the long-term sustainability of the pension system. The analysis of the sustainability of the pension system was carried out using a microsimulation model, which allows to assess the impact of demographic and economic factors and policy decisions on replacement rates and the structure of pension benefits. The results show that by 2070, the overall replacement rate could fall from 53,1 % to 40,1 %, and for non-participants in Pillar II to as low as 31,2 %, while increasing the Pillar II contribution rate to 5 %, 7 %, or 10 % has a limited impact on the weighted replacement rate due to the low level of participation. More significant results can be achieved by strengthening Pillar I and by additional indexation of the individual pension component. With additional indexation of 2 %, the total replacement rate could reach 52,7 % in 2070, and 70,6 % for Pillar II contributors. However, such an outcome would require significantly higher contributions – the social security rate could rise to 13 % compared to the current 8.72 %. The analysis also revealed that the proposed reform of Pillar II, which would allow individuals to opt out of saving, could have adverse consequences. As Pillar II is intended to complement Pillar I and enhance pension adequacy in old age, participant withdrawals would undermine the overall effectiveness of the system. Model simulations indicate that the replacement rate would decline under all withdrawal scenarios. In the event that 60 % of participants choose to exit the scheme, the weighted replacement rate is projected to decrease by as much as 5.3 % by 2070
Piezoelectric polymer pvdf sensors for advanced energy harvesting and sensing applications /
No full textPolyvinylidene fluoride (PVDF), a well-known piezoelectric polymer, has emerged as a promising material for advanced energy harvesting and sensing applications due to its flexibility, durability, and exceptional piezoelectric properties. This study investigates the use of PVDF sensors for converting ambient mechanical energy into electrical power, addressing key challenges in scalable and sustainable energy harvesting systems. A specially designed energy harvesting circuit was developed, incorporating a rectifier, energy storage capacitors, and an LED load to demonstrate practical energy utilization. Previous research highlights the capability of PVDF-based systems in biomechanical energy capture and self-powered sensing [1]. Furthermore, hybrid composites of PVDF with advanced materials, such as reduced graphene oxide, have been shown to enhance energy conversion efficiency [2]. This work establishes PVDF as a versatile piezoelectric polymer, paving the way for innovative solutions in energy harvesting and sensing technologies for diverse applications [3]. [...]