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CNN-BASED INVESTMENT STRATEGY USING TECHNICAL INDICATORS
No full textSince the dawn of time, both individuals and institutions have been in pursuit of
strategies to amplify their wealth, recently, more in the financial world. This process of
increasing one’s wealth changed and evolved in the same manner as did technological
progress, particularly with the integration of artificial intelligence (AI) and machine
learning (ML) technologies in the financial analysis. Among these advancements and
innovations, Convolutional Neural Networks (CNNs) have emerged as a formidable
tool for forecasting market trends and predicting outcomes of transactions. Current
investment strategies mostly use numerical data, technical indicators derived from
numerical data, and the intricate algorithms of neural networks (NNs). However, the
current methodologies for data transformation misses the necessary diversity to fully
exploit the capabilities of advanced neural network models.
This paper aims to bridge this gap by proposing a novel approach that utilizes
models with technical indicators with new data format. The end goal of this paper is
to develop a robust investment strategy that utilizes power of NNs while using uncom-
mon data transformation in order to achieve great results in accuracy and reliability
of trend prediction. To achieve this goal, this paper introduces a methodological
know-how that methods that involve converting technical indicators into images and
feeding them to NNs, specifically NNs that are well adept in image classifications.
Particularly, leveraging CNN’s excellence in detecting unseen patterns within these
visual representations, offering a enhancing the effectiveness of investment strategies
through the power of image-based data analysis
DYNAMIC WIRELESS CHARGING OF ELECTRIC VEHICLES
No full textThe aim of this project is to implement and fabricate the scaled-down prototype of Wireless Power Transfer for Electric Vehicle charging.
The study will begin with an extensive analysis of the most recent publications on wireless charging technologies. IPT technology will primarily be investigated. It is expected that optimal operating parameters will be determined for this type of wireless power transform. The next step is to develop IPT system with the predetermined operating parameters. To ensure that the system is highly efficient and effective in real-world charging of electric vehicles, it will be tested and adjusted.
This project is significant because it has the potential to develop wireless charging technology and accelerate the adoption of electric vehicles. Wireless charging is becoming into a key technology for the vehicle industry as demand for environmentally friendly transportation rises. By designing a IPT system that overcomes the constraints of existing IPT systems, this research will deliver a major technical innovation that will assist to make wireless charging more accessible and efficient for EVs
EXPERIMENTAL VERIFICATION OF PERFORMANCE IMPROVEMENT OF RADAR DETECTION USING ROBUST WAVEFORM TECHNIQUES
No full textThis project was aimed at analyzing different
methods of RADAR performance improvement
and their efficiency in the experimental
setting in mitigating noises and interferences
in the received signal. The project will
focus on the experimental results acquired
from IWR1843BOOST 77 GHz electronic LFM
RADAR and on the effect of different techniques
on the object detection. Some of the
techniques that were studied include increasing
bandwidth for improved range resolution,
frequency hopping, and interpolation in the
STFT domai
EMOTION ESTIMATION THROUGH 3D CONVOLUTIONAL NEURAL NETWORK IN VIDEOS
No full textThe project aims to present the methods of emotion estimation with a use of convolutional neural networks (3DCNN). Recognizing human emotions allow us to create user friendly devices and allow for devices to respond effectively to user needs. One potential application is in the healthcare field. Tools with effective emotion recognition algorithms can be used to treat and diagnose patients with mental health conditions, such as anxiety or depression. In marketing, these tools can be used to adjust preferences to consumer wants as currently done by AI tools
IMPLICATIONS FOR PRIMARY DEPLETION OF CARBONATE SLOPE DEPOSITS FROM TENGIZ OIL FIELD AND RESERVOIR CHARACTERIZATION BASED ON AN OUTCROP ANALOGUE
No full textThis study examines the Paleozoic platform outcrop in the Bolshoi Karatau as an analogue for the giant oil fields in the Pricaspian basin, with a focus on the carbonate distal slope-basin deposits at the Akuyuk section. A detailed sedimentary log (39.6 m thick) reveals a predominance of grainstone lithofacies, consisting mainly of micro- to mm-scale skeletal fragments, microfossils. Notably, occurrences of rugose corals and cephalopod fragments were located mostly on the top of the individual beds, which are settled down.
Microfacies analysis divides the section into biosparite (grainstone) and biomicrite (packstone), predominantly comprising microfossils (foraminifers, calcipheres), peloids, and occasionally intraclasts. Deposition occurred in a shallow marine environment and transported through turbidites to the lower slope. Gamma-ray spectrometry analysis indicates very low radioactivity, suggesting predominantly pure carbonates and a marine depositional setting.
The proposed main depositional environment is a proximal lobe with mostly amalgamated turbidites, occasionally with muddier parts (packstones). Observations of not full Bouma sequences (only Ta or Ta and Te) (Bouma et al. 1962), mainly Ta (high concentrated flow without any visible sedimentary structures), and chertified trace fossils (thalassinoides) suggest episodic possible Te preservation. Thalassinoides accumulation in soft sediments obscures sediment appearance.
The study conducted six scanlines primarily in the lower part of the section, revealing a predominance of extensional fractures filled with calcite, perpendicular to bedding, and stylolites parallel to bedding, indicative of tectonic influence. Analysis of fracture characteristics such as aperture, height, and spacing showed distributions skewed towards smaller values, because of measurement limitations for smaller fractures. The mode and standard deviation for aperture were 0.15 mm and 1.6 mm, respectively, while for height, they were 0.16 m and 0.38 m. Relationships between fracture parameters indicated independence, suggesting complex control factors. The association of veins with faults or folds suggests formation during mountain-building events, contributing to the dynamic geological history of the study area.
Further analysis reveals a consistent pattern of densely spaced fractures within individual beds, indicating a strong influence of each layer's mechanical properties. However, variability in fracture heights and spacing suggests structural complexity. While dense spacing and bed confinement are prevalent, and variations highlight the nuanced impact of mechanical stratigraphy on fracture propagation.
In a primary depletion analytical model, outcrop sedimentological and fracture data serve as primary inputs, facilitating fluid transport between the matrix and fractures. This model examines how variables such as Vf/Vm and kf/km (sensitivity analysis) affect reservoir depletion, emphasizing rapid fracture depletion compared to constant matrix pressure. Variations in both volume coefficients and permeability coefficients significantly influence the flow behavior in natural fractured reservoirs: higher permeability coefficients lead to faster rate decline, and lower volume coefficients enhance this effect, promoting a rapid transition to fracture matrix flow.
The integration of sedimentological logging, microfacies analysis, gamma-ray spectrometry, fracture analysis, and analytical primary depletion modeling sheds light on the importance of field data in understanding fluid flow within naturally fractured reservoirs. These findings offer valuable insights for enhancing reservoir modeling
FORECASTING MECHANICAL PROPERTIES OF CONCRETE CONTAINING SECONDARY RAW MATERIALS
No full textClimate change due to the significant release of CO2 from cement industries has become a critical issue worldwide. However, secondary cementitious materials (SCMs), can be used as eco-friendly cement alternatives. Most of the previously conducted studies primarily rely on either experimental investigations or simple regression models to find out the optimal mixture design of concrete made with SCMs. However, in the experimental approach, few tests could be performed for optimization due to time limitations and the availability of resources. On the other hand, simple machine learning (ML) models can’t be relied on without extensive validation. Therefore, to overcome these limitations this study aims to use three ML techniques, such as artificial neural network (ANN), adaptive neuro-fuzzy interface system (ANFIS), and gene expression programming (GEP) method with experimental validation for forecasting the compressive strength (CS) and tensile strength (TS) of concrete incorporated with ground granulated blast furnace slag (GGBS) and silica fume (SF) as SCMs. A comprehensive dataset containing the eight most influential inputs of concrete with CS and TS as outputs was collected from the literature and used for model development. The efficiency of the developed model was evaluated using statistical measures and experimental validation. Additionally, sensitivity and parametric analysis were carried out to identify the coherence of developed models with experimental studies. Comparative analysis showed that ANFIS models surpassed other models with higher R2 and lower errors. Conversely, GEP demonstrated enhanced performance compared to ANFIS and ANN concerning the nearness of statistical measures among the training, validation, and testing sets. Further, GEP also gives predictive formulas that can be utilized for the pre-design of concrete mixtures made with SF and GGBS. Sensitivity and parametric analysis showed strong relevance with experimental studies validating the model's performance. Thus, the recommended models are reliable and can be used to promote the sustainable use of industrial wastes (SF and GGBS) in concrete
CYTOSKELETON DYNAMICS AND SPATIAL ORGANIZATION DURING EPITHELIAL-TO-MESENCHYMAL TRANSITION
No full textRATIONALE: Epithelial-to-mesenchymal transition (EMT) is a process that
occurs during normal physiological processes (embryogenesis and organ
formation) and if it is inappropriately activated it can lead to pathological
processes (formation of scars, cancer metastasis, etc.). EMT is well studied at
the morphological and transcriptome level. However, cytoskeleton changes
during this process are less well understood. The cytoskeleton consists of
microtubules, actin filaments, and intermediate filaments. In addition, there are
protein complexes named focal adhesions that provide cell attachment to the
extracellular matrix, and connect the actin cytoskeleton with the extracellular
matrix. To describe the changes in the behavior of the cytoskeleton, namely
microtubules and actin cytoskeleton during EMT is of particular interest. In
addition, describing the behavior of focal adhesions during EMT is also
important.
AIM: The objective of this study is to describe quantitatively morphological
changes that occurred in post-EMT MCF-7, A-549, and HaCaT cells, analyze
microtubule dynamics, spatial organization, and its contribution to cell motility,
identify changes in actin filament organization and study focal adhesion
turnover.
HYPOTHESIS: The dynamics of microtubules in cells undergoing EMT
v
might change. Cells undergoing EMT are expected to have more dynamic
microtubules. Cells undergoing EMT are expected to more efficiently adhere to
diverse substrates and therefore better spread. Focal contacts in cells
undergoing EMT are expected to be more pronounced and dynamic than in cells
not undergoing EMT.
METHODS: To study changes in post-EMT cells, EMT was induced in three
different cell models: MCF-7, A-549, and HaCaT. To evaluate that EMT
happened, western blot and quantitative polymerase chain reaction (q-PCR)
were applied to determine the level of expression of master regulators of EMT.
Cell images were recorded using bright field microscopy, and analyzed using
the Fiji Image J program.
In analyzed cells, microtubule networks and actin filaments were visualized by
immunofluorescence. To follow, describe, and measure microtubule dynamics
transfection with EB-3-RFP protein was conducted. To visualize focal
adhesions, two approaches were used: transduction with a talin red fluorescent
protein (Talin-RFP) and transient transfection with Ptag-RFP-vinculin. Films
were recorded using time-lapse fluorescent microscopy and analyzed using the
Fiji Image J program. All statistical analysis was performed using GraphPad
Prism (Dotmatics, USA) and a nonparametric Mann-Whitney U test or
parametric t-test with Welch correction. The actin filament measurements were
vi
completed using Matlab scripts.
CONCLUSION: This study showed morphological changes in three post-
EMT cell cultures studied. All types of cells increased in size. MCF-7 and
HaCaT became spread out, while A-549 became elongated. All three post-EMT
cell cultures had changes in microtubule organization and dynamics. Post-EMT
MCF-7 and HaCaT cells showed microtubules at a low density at cell borders,
while post-EMT A-549 cells had less covered nuclei by microtubules. In all
three studied models, the microtubule growth rate increased and the length of
the microtubule plus end tracks became longer. The average angle of
microtubule growth trajectories to cell radius decreased. Actin fibers rearranged
into stress fibers in post-EMT cells. The area of focal adhesions decreased in all
post-EMT cell cultures studied and focal adhesions appeared localized
throughout the inner areas of spread cells. These results indicate that
cytoskeletal changes make a significant contribution to the EMT process
AIRBORNE PARTICULATE MATTER IN ASTANA, KAZAKHSTAN: POTENTIALLY TOXIC ELEMENTS, LUNG BIOACCESSIBILITY, AND RISK ASSESSMENT
No full textThe degradation of air quality remains one of the most critical environmental concerns. Exposure to airborne pollutants is extensively associated with various health conditions, including respiratory and cardiovascular diseases, and premature death. The health risks of air pollution have been linked to particulate matter (PM) and its constituents. Potentially Toxic Elements (PTEs) in atmospheric PM are a critical factor contributing to its toxicity. This doctoral thesis addresses multiple aspects of air quality in Astana, Kazakhstan, offering a holistic understanding of the local air pollution situation through (1) analysis of PM and gaseous pollutant concentration; (2) proposing a modification to the toxicity assessment of PM-bound PTEs via in vitro lung bioaccessibility; (3) the assessment of health risk due to inhalation exposure to PM using bioaccessible concentration of PTEs; (4) morphological characterization of PM; (5) source identification; (6) studying precipitation chemistry and its role in air pollution; and (7) assessment of the public knowledge, perception and attitude towards local air quality in Astana. The methodological framework involved primary data analysis (342 PM samples collected in Astana, Kazakhstan from 2021 to 2023) and air pollution data obtained from monitoring stations located in the city (S1-S6) in 2018-2020. Annual and 24-hour mean concentrations of PM2.5, PM2.5-10, and gaseous pollutants (SO2, CO, NO2, NO, and HF) were, in general, higher than established national and international (World Health Organization (WHO)) maximum permissible levels (e.g., for PM2.5 annual mean of 29.7 μg/m3 in 2018-2019; and 24-hour mean of 28.7 μg/m3 (maximum: 534 μg/m3) for PM2.5 and 226 μg/m3 (maximum: 1,564 μg/m3) for PM2.5-10, respectively, in 2021-2023). To simulate real-life inhalation exposure to PM-bound PTEs, the assessment was conducted through optimization of in vitro lung bioaccessibility testing in simulated lung fluids (SLF) (i.e., modified Gamble’s solution (GS) and Artificial Lysosomal Fluid (ALF)). For a modification of commonly established methodology, a large set of PTEs (Cd, Co, Cr, Cu, Mn, Ni, Pb, Sb, V, and Zn) has been investigated using seven distinct formulations of GS, one ALF on two reference materials (SRM 2691 and BGS 102). The bioaccessibility of the selected PTEs generally increased in modified GS with the incorporation of 5% DPPC (phospholipid) (e.g., from 2.87% to 8.35% for V in BGS 102), 0.25% cholesterol (e.g., from 27.3% to 31.5% for Cr in SRM 2691), and 5% DPPC + 0.5% cholesterol (e.g., from 43.5% to 51.5% for Cu in BGS 102). Therefore, using DPPC + cholesterol may be recommended for routine bioaccessibility testing. The effect of the tested solid-to-liquid ratio (S/L) was sample and element-specific. Overall, a lower S/L led to a higher bioaccessibility % in ALF. For all PTEs, the peak bioaccessibility was reached at a 4-week extraction, suggesting a longer testing duration when feasible. The optimized parameters for in vitro bioaccessibility were later applied for inhalation bioaccessibility of selected PTEs (i.e., Cd, Co, Cr, Cu, Fe, Mn, Ni, Pb, V, and Zn) in PM2.5 collected in Astana, Kazakhstan. The highest bioaccessible concentration was observed for Fe (mean: 16,229 mg/kg, range: (906-30,419 mg/kg) and V (mean: 10,725 mg/kg, range: (687-27,092 mg/kg). The inhalation Health Risk Assessment (HRA) using a bioaccessible concentration of PTEs in PM2.5 revealed acceptable carcinogenic and non-carcinogenic risks for adult and children exposure, although the maximum Cancer Rate (CR) for adults was slightly higher (1.01 × 10-6) than the established United States Environmental Protection Agency (U.S. EPA) threshold (HIc > 1 × 10-6). Scanning Electron Microscopy (SEM) analysis determined several major PM particle groups, including bioaerosols, coal fly ash (CFA), dust (natural or construction), and soot particles. Irregularly shaped, small-sized particles of CFA are associated with respiratory conditions and neurodevelopmental disorders, while soot particles of complex shapes can penetrate deeply into the respiratory system. In precipitation analysis, the mean concentration of major ions (i.e., F-, Cl-, NO2-, NO3-, SO42-, PO43-, K+, Na+, NH4+, Ca2+, Mg2+) remained within permissible levels for groundwater, drinking, and surface water. However, in April, the highest F- concentration (1.82 mg/L) exceeded the WHO limit for drinking water (1.5 mg/L). The concentration of most heavy metals (i.e., Cd, Co, Cr, Cu, Mn, Pb) was below WHO's maximum permissible levels, except for V, which exhibited the highest average concentration of 108 µg/L in precipitation samples across four seasons. The chemical analysis of PM and precipitation revealed common sources, including coal/liquid fuel combustion and vehicular exhaust. PM2.5 concentration modeling via Multiple Linear Regression (MLR) and Machine Learning (ML) Random Forest (RF) algorithms revealed PM10 and CO as major predictors of PM2.5 concentration. A real-life pollution scenario using Conditional Bivariate Probability Function (CBPF) analysis also suggested a substantial contribution of coal-heated power plant activity (CHPPs) and coal combustion from residential heating, coupled with emissions from internal combustion engine vehicles. Structural equation modeling (SEqM) was employed to investigate the causal relationship between perceived air quality, environmental literacy, and willingness to pay (WTP) for environmental protection. The age, education, and health status of the participants significantly affected (p < 0.001) their level of environmental knowledge and awareness. The SEqM analysis indicates that knowledge is the major determinant in improving public awareness and perception of local air pollution (path value = 0.626). The findings of the current research work can assist healthcare professionals and environmental researchers in public health-related decision-making and establishing feasible air quality guidelines
DESIGN OF ANGLE-INSENSITIVE TRIPLE-BAND METAMATERIAL ABSORBER FOR ENERGY HARVESTING AT MICROWAVE FREQUENCIES
No full textIn the modern world there is an abundance of unused, free ambient energy, which includes
energy that is dispersed from wi-fi and internet usage. Harvesting this energy is of importance, as it can provide charge to self-sustainable, low-power application such as IoT devices. In this paper, angle-incensitive triple-band meta-material absorber was designed for resonant frequencies at 2.45GHz, 5GHz, 6GHz with an average absorption efficiency of 80%, 95% and 99% for the angle of incidence θ from 0° to 60° respectively. The simulation was done in CST studio suite, and the simulated absorber shows
good absorption qualities. Implemented design was a 9 by 9 unit cell metamaterial grid de-
ployed using RO4350B substrate and it showed some agreement with the simulations. Moreover, the design showed a better absorption qualities, covering a wider frequencies range than expected. Experimental absorption is perfect over the shifted frequency bands
EFFICIENT TECHNIQUES FOR PERFORMANCE ENHANCEMENT IN INTELLIGENT SURFACE-ENABLED WIRELESS NETWORKS
No full textOur study delves into the capabilities of Simultaneously Transmitting and Reflecting Reconfigurable Intelligent Surfaces (STAR-RIS) in boosting the performance of uplink-downlink (UL-DL) Non-Orthogonal Multiple Access (NOMA) networks. By segmenting STAR-RIS, we aim to improve NOMA users’ channel gains, enhancing the efficiency of NOMA integration and obviating the necessity for UL power adjustments. We thoroughly examine our approach across two key optimization problems: feasible region and max-min rate (MMR), ensuring compliance with QoS demands for both UL-DL. Our method deploys the independent roles of STAR-RIS partitioning and Base Station (BS) transmission power to derive explicit formulas that reveal the effectiveness of optimal STAR-RIS portion in satisfying UL-QoS needs, while BS power management guarantees DL-QoS satisfaction. The robustness of our analytical conclusions is confirmed through simulation experiments, which underscore the substantial benefits STAR-RIS technology presents to NOMA networks under these varied operational frameworks