Extraction Techniques Research Articles

Machine learning prediction of brain metastasis invasion pattern on brain MRI scans

Abstract Background Brain metastasis invasion pattern (BMIP) is an emerging biomarker associated with recurrence-free and overall survival in patients, and differential response to therapy in preclinical models. Currently, BMIP can only be determined from the histopathological examination of surgical specimens, precluding its use as a biomarker prior to therapy initiation. The aim of this study was to investigate the potential of machine learning (ML) approaches to develop a non-invasive magnetic resonance imaging (MRI)-based biomarker for BMIP determination. Methods From an initial cohort of 329 patients, a subset of 132 patients met inclusion criteria for this retrospective study. We evaluated the ability of an expert neuroradiologist to reliably predict BMIP. Thereafter, the dataset was randomly divided into training/validation (80% of cases) and test subsets (20% of cases). Ground truth for BMIP was histopathologic evaluation of resected specimens. Following MRI sequence co-registration, advanced feature extraction techniques deriving hand-crafted radiomic features with traditional ML classifiers and convolution-based deep learning (CDL) models were trained and evaluated. Different ML approaches were used individually or using ensembling techniques to determine the model with the best performance for BMIP prediction. Results Expert evaluation of brain MRI scans could not reliably predict BMIP, with an accuracy of 44-59% depending on the semantic feature used. Among the different ML and CDL models evaluated, the best performing model achieved an accuracy of 85% and an F1-score of 90%. Conclusion ML approaches can effectively predict BMIP, representing a non-invasive MRI-based approach to guide management of patients with brain metastases.

Patchy acute effluvium in and around the donor area following FUE hair transplant and its predilection for two-day session and high graft extraction cases

: With the advancements in Follicular unit extraction (FUE) technique of hair transplant (HT) advanced grades of baldness are taken up nowadays requiring higher donor grafts and surgery often extends to the second day. This has brought into light some lesser known complications of hair transplant like acute donor site effluvium (ADE).Data on ADE is scanty; more research is needed on associated clinico-etiological factors and presentations of the condition.of our study was to study various clinico-epidemiological factors in patients of acute donor effluvium and to assess the relationship of number of grafts extracted with severity of ADE. : It was a centre based retrospective cross sectional study in which 30 patients of ADE were enrolled. Clinical and photographic record of all patients of ADE presenting in the last 3 years was screened. Age, gender, co-morbidities, personal habits, grade of androgenetic alopecia, total graft extraction, severity of hair loss and duration of surgery was compiled along with photographs and follow up notes. Mean age of patients was 33.9±5.4 years. All patients were male. Out of 30 patients 5(16.6%) were smokers and 7(23.3%) alcoholic. In 22(73.3%) of the cases of ADE the HT lasted for more than a day. The maximum duration of surgery in all cases didn’t exceed 2 days. Out of 30 cases of ADE, total grafts extracted exceeded 4000 in 28(93.3%) of cases; only 2(6.7%) of the cases had a graft extraction of <4000. There was a significant and direct correlation between the severity of ADE hair loss (using SALT score) and the number of total scalp grafts extracted (r=0.657, p=0.024). ADE was seen more with advanced baldness requiring high scalp graft extraction of >4000 grafts and when surgery extended to second day. Hair loss in ADE correlated directly with the number of scalp grafts harvested.

Novel toxin biosynthetic gene cluster in harmful algal bloom-causing Heteroscytonema crispum: Insights into the origins of paralytic shellfish toxins.

Caused by both eukaryotic dinoflagellates and prokaryotic cyanobacteria, harmful algal blooms (HABs) are events of severe ecological, economic, and public health consequence, and their incidence has become more common of late. Despite coordinated research efforts to identify and characterize the genomes of HAB-causing organisms, the genomic basis and evolutionary origins of paralytic shellfish toxins (PSTs) produced by HABs remain at best incomplete. The PST saxitoxin has an especially complex genomic architecture and enigmatic phylogenetic distribution, spanning dinoflagellates and multiple cyanobacterial genera. Using filtration and extraction techniques to target the desired cyanobacteria from non-axenic culture, coupled with a combination of short and long read sequencing, we generated a reference-quality hybrid genome assembly for Heteroscytonema crispum UTEX LB 1556, a freshwater, PST-producing cyanobacterium thought to have the largest known genome in its phylum. We report a complete, novel biosynthetic gene cluster for the PST saxitoxin. Leveraging this biosynthetic gene cluster, we find support for the hypothesis that PST production has appeared in divergent Cyanobacteria lineages through widespread and repeated horizontal gene transfer. This work demonstrates the utility of long-read sequencing and metagenomic assembly toward advancing our understanding of PST biosynthetic gene cluster diversity and suggests a mechanism for the origin of PST biosynthetic genes.

Using the follicular unit extraction technique in treatment of male androgenetic alopecia.

The incidence of androgenetic alopecia in males is on the rise annually, with hair transplantation using follicular unit extraction (FUE) gaining increasing acceptance as an appropriate treatment for these individuals. A retrospective study was undertaken, involving 158 male patients diagnosed with androgenetic alopecia, who underwent treatment between January 2016 and December 2020 at the Medical Cosmetology Department of the First Affiliated Hospital of Xi'an Medical University. Demographic data and treatment characteristics were documented and analyzed. Clinical efficacy, operation-related complications, patient satisfaction rates, and quality of life scores were assessed and analyzed. Following FUE hair transplantation, over 90% of the hair follicles survived in 158 patients, with more than 85% of patients achieving a hair follicle survival rate exceeding 95% at 12 months post-operation. Patient satisfaction rates exceeded 98%, while the complication rate was below 6%. Our findings demonstrate that FUE is a minimally invasive hair transplant technique associated with a high hair follicle survival rate and optimal hair density. This approach proves effective in treating male androgenetic alopecia and merits further clinical application.

Resveratrol: Extraction Techniques, Bioactivity, and Therapeutic Potential in Ocular Diseases

Resveratrol: Extraction Techniques, Bioactivity, and Therapeutic Potential in Ocular Diseases

A Similar Feature Point Matching Method for Aerial Electric Power Tower Images Based on a One-Ring Neighborhood of Vertices

Due to the susceptibility of images to various factors such as scale changes, imaging conditions, and image noise, traditional feature point matching methods are difficult to achieve ideal matching accuracy, leading to many challenges in the automatic analysis and processing of power tower images. To improve the matching accuracy of similar feature points in aerial images of electric power tower, this study proposes a method for matching similar feature points in aerial images of electric power tower based on a vertex ring neighborhood, addressing the problem of large matching errors caused by factors such as scale and imaging conditions. This method first adopts the feature point extraction technique of electric power tower image based on decomposition and filtering, and constructs the Laplacian pyramid of the original image. Subsequently, filter banks are used to decompose the pyramid image in different directions, extract local extremum points as candidate feature point sets, and merge them to obtain the final feature point set. On this basis, taking into account the spatial relationships and geometric characteristics around the feature points, a texture mapping algorithm based on vertex ring neighborhood and patch classification is applied to construct a vertex ring neighborhood structure and extract geometrically representative electric power tower features. Finally, by using a texture feature point matching method based on the principle of similarity, the similarity between the real-time image and the reference image features is calculated for matching, and combined with the Babbitt coefficient to remove mismatched feature points, accurate matching of similar feature points in aerial electric power tower images is achieved. The experimental results show that this method has a mean square error of less than 0.1Pixel in matching similar texture feature points of aerial power tower images under various working conditions, significantly improving the matching accuracy and providing an effective tool for automatic analysis and processing of power tower images.

Signal Enhancement of Selected Norepinephrine Metabolites Extracted from Artificial Urine Samples by Capillary Electrophoretic Separation

The measurement of selected norepinephrine metabolites, such as 3,4-dihydroxyphenylglycol (DHPG), 3-methoxy-4-hydroxyphenylethylenglycol (MHPG), and vanillylmandelic acid (VMA), in biological matrices—including urine—is of great clinical importance for the diagnosis and monitoring of diseases. This fact has forced researchers to evaluate new analytical methodologies for their isolation and preconcentration from biological samples. In this study, the three most popular extraction techniques—liquid-liquid extraction (LLE), solid-phase extraction (SPE), and a new 3D-printed system for dispersive solid-phase extraction (3D-DSPE)—were investigated. Micellar electrokinetic chromatography (MEKC) with a diode array detector (DAD) at 200 nm wavelength was applied to the separation of analytes, allowing for the assessment of the extraction efficiency (R) and enrichment factor (EF) for the tested extraction types. The separation buffer (BGE) consisted of 5 mM sodium tetraborate decahydrate, 50 mM SDS, 15% (v/v) MeOH, 150 mM boric acid, and 1 mM of 1-hexyl-3-methylimidazolium chloride (the apparent pH of the BGE equaled 7.3). The EF for each extraction procedure was calculated with respect to standard mixtures of the analytes at the same concentration levels. The 3D-DSPE procedure, using DVB sorbent and acetone as the desorption solvent, proved to be the most effective approach for the simultaneous extraction and determination of the chosen compounds, achieving over 3-fold signal amplification for DHPG and MHPG and over 2-fold for VMA. Moreover, all extraction protocols used for the selected norepinephrine metabolites were estimated and discussed. It was also confirmed that the 3D-DSPE-MEKC approach could be considered an effective tool for sample pretreatment and separation of chosen endogenous analytes in urine samples.

Comparative volatiles profiling of two marjoram products via GC-MS analysis in relation to the antioxidant and antibacterial effects.

Marjoram (Origanum majorana L.), also known as "sweet marjoram" or "sweet oregano" is a Mediterranean herbaceous perennial herb cultivated in Egypt and widely consumed as an herbal supplement for treatment of several ailments. The main goal of this study was to assess volatiles' variation in marjoram samples collected from two different widely consumed commercial products using two different extraction techniques viz. head space solid phase microextraction (HS-SPME) and petroleum ether using gas chromatography mass spectrometry (GC-MS) analysis and multivariate data analysis. A total of 20 major aroma compounds were identified in samples extracted with HS-SPME found enriched in monoterpene hydrocarbons and oxygenated compounds. The major volatiles included β-phellandrene (20.1 and 14.2%), γ-terpinene (13.4 and 11.7%), 2-bornene (12.3 and 11.5%), p-cymene (9.8 and 4.6%) terpenen-4-ol (16.4 and 7.5%), sabinene hydrate (16.02 and 8.8%) and terpineol (4.2 and 3.2%) in MR and MI, respectively. Compared with HS-SPME, 51 aroma compounds were identified in marjoram samples extracted with petroleum ether, found more enriched in aliphatic hydrocarbons (42.8 and 73.8%) in MR and MI, respectively. While a higher identification score was observed in the case of solvent extraction, SPME appeared to be more selective in the recovery of oxygenated terpenes to account more for marjoram aroma. Multivariate data analysis using principal component analysis (PCA) revealed distinct discrimination between volatile composition of both marjoram samples. The total phenolic and flavonoid contents in marjoram samples were at (111.9, 109.1µg GA/mg) and (18.3, 19.5µg rutin eq/mg) in MR and MI, respectively. Stronger antioxidant effects were observed in MR and MI samples with IC50 at 45.5 and 56.8µg/mL respectively compared to IC50 6.57µg/mL for Trolox as assayed using DPPH assay. Moderate anti-bacterial effect was observed in MR and MI samples and expressed as a zone of inhibition mostly against Bacillus subtilis (16.03 and 15.9mm), B. cereus (12.9 and 13.7mm), Enterococcus faecalis (14.03 and 13.97mm), and Enterobacter cloacae (11.6 and 11.6mm) respectively.

Current Strategies to Modify the Functional Properties of Proteins Extracted from Pumpkin Seeds: A Comprehensive Review

The functional properties of pumpkin seed proteins remain unutilized in numerous food and industrial applications. Several current approaches aim to improve the functional properties of pumpkin seed proteins, allowing their innovative potential to develop and modify significantly. Several strategies can be implemented to alter the functional properties of proteins isolated from pumpkin seeds. The first is enzymatic hydrolysis, regardless of whether, proteases may free peptide binding and profoundly impact the protein structure and functionality. Thermal treatment can include heating and cooling to replace protein conformation and increase solubility, emulsification, and gelation properties. Chemical modification techniques, including acylation and glycation, can also be used to improve stability, viscosity, and foaming ability. Functional properties and, where possible, ingredients with many applications may include exceptional possibilities for proteins modified in food preparations, such as dairy replacements, plant-based meat analogues, and free gluten that have an outstanding aspect, satisfactory quality, and nutritional profiles. As multiple different proteins act as precursors of active peptides, they can also be used to generate bio-specific foods. This review briefly provides information about various types of protein extraction techniques and functional properties that are modified by different types of processing technologies.

Optimization of pretreatment protocol for strontium-90 analysis in marine fish bone samples.

For systematic monitoring of radioactive nuclides in marine products, this study aimed at streamlining and simplifying the analysis method for the prominent radioisotope, strontium-90 (90Sr). The DGA chelate solid-phase extraction technique was employed for enhanced efficiency. The study focused on optimizing the necessary pretreatment procedures while minimizing the steps involving HNO3 leaching. This protocol enabled the quantitative recovery of strontium, and it facilitated a rapid analysis without the need for a time-consuming evaporation step and without waiting for secular equilibrium between 90Sr and its progeny to be reached. The method incorporating the optimized pretreatment protocol was applied to three diverse marine fish species and the accurate quantification of 90Sr at background levels in surface seawater was achieved. The method obtained concentrations in bone samples from these species that ranged from 0.036 to 0.120 mBq per kg-dry, and chemical yield values were notably high, ranging from 87.7% to 92.5%.

One-step simultaneous and efficient extraction of multiple active compounds from Lycium barbarum by aqueous three-phase system: Focusing on polysaccharides

In the past few years, the aqueous three-phase system (A3PS) has attracted a lot of attention due to its effectiveness in the efficient separation. Polysaccharides, carotenoids and polyphenols were separated simultaneously from Lycium barbarum by an aqueous three-phase system (A3PS) formed by acetonitrile/betaine/K2HPO4 as a novel extraction technique. The system is characterized by small amount of machine reagents, few consumables and simple extraction steps. The effects of acetonitrile, betaine and K2HPO4 concentrations on the extraction efficiency were investigated by Response Surface Methodology. The optimum composition of A3PS were 26.2 % (w/w) acetonitrile, 10.2 % (w/w) betaine and 27.6 % (w/w) K2HPO4. Under this condition, the extraction efficiencies of carotenoids, polyphenols, and Lycium barbarum polysaccharides (LBP) were 42.12±0.10 %, 88.61±0.95 % and 84.63±0.46 %, respectively. The obtained LBP was characterized by several technologies. The LBP was proved to consist of mannose, ribose, rhamnose, glucuronic acid, galacturonic acid, glucose, galactose, xylose, arabinose and fucose in a proportion of 4.14:0.80:4.00:4.43:22.20:39.9:12.44:3.22:8.46:0.41. The molecular weight of LBP was 12554 Da. In addition, LBP exhibited excellent antioxidant activity. In general, as a rapid developing method of extraction and separation, A3PS had potential application in the simultaneous separation of multiple substances.

A systematic review on feature extraction methods and deep learning models for detection of cancerous lung nodules at an early stage -The Recent trends and challenges.

Lung cancer is one of the most common life-threatening worldwide cancers affecting both the male and female populations. The appearance of nodules in the scan image is an early indication of the development of cancer cells in the lung. The Low Dose Computed Tomography screening technique is used for the early detection of cancer nodules. Therefore, with more Computed Tomography (CT) lung profiles, an automated lung nodule analysis system can be utilized through image processing techniques and neural network algorithms. A CT image of the lung consists of many elements such as blood vessels, ribs, nodules, sternum, bronchi, and nodules. These nodules can be both benign and malignant, where the latter leads to lung cancer. Detecting them at an earlier stage can increase life expectancy by up to 5 to 10 years. To analyze only the nodules from the profile, respected features are calculated using image processing techniques. Based on the review, textural features were promising ones in medical image analysis and for solving computer vision problems. The significance of extracting the hidden features (textural) enables the higher performance of deep learning algorithms (DL) predominantly in medical imaging, which has resulted in improvement in accuracy in recent years. Earlier detection of cancerous lung nodules is possible through the combination of multi-featured extraction and classification techniques using image data. In this paper, we discussed the overview of lung cancer along with publicly available datasets for research purposes. The primary objective of the paper is to provide the importance of textural features when combined with different deep-learning models. It gives insights into their advantages, disadvantages, and limitations regarding possible research gaps. The paper compared the recent studies of deep learning models with and without feature extraction and concluded that DL models that include feature extraction were better than the others.

A Study on a Novel Production Forecasting Method of Unconventional Oil and Gas Wells Based on Adaptive Fusion

Reliable forecasting of unconventional oil and gas well production has consistently been a hot and challenging issue. Most existing data-driven production forecasting models rely solely on a single methodology, with the application effects of other mainstream algorithms remaining unclear, which to some extent hinders the generalization and utilization of these models. To address this, this study commences with data preparation and systematically develops a novel forecasting model based on the adaptive fusion of multiple mainstream data-driven algorithms such as random forest and support vector machine. The validity of the model is verified using actual production wells in the Marcellus. A comprehensive evaluation of multiple feature engineering extraction techniques concludes that the main controlling factors affecting the production of Marcellus gas wells are horizontal segment length, fracturing fluid volume, vertical depth, fracturing section, and reservoir thickness. Evaluation models based on these primary controlling factors reveal significant differences in prediction performance among mainstream data-driven methods when applied to the dataset. The newly developed model based on adaptive fusion optimized by genetic algorithms outperforms individual models across various evaluation metrics, which can effectively improve the accuracy of production forecasting, demonstrating its potential for promoting the application of data-driven methods in forecasting unconventional oil and gas well production. Furthermore, this will assist enterprises in allocating resources more effectively, optimizing extraction strategies, and reducing potential costs stemming from inaccurate predictions.

Public opinion mining in social media about Ethiopian broadcasts using deep learning.

Now adays people express and share their opinions on various events on the internet thanks to social media. Opinion mining is the process of interpreting user-generated opinion data on social media. Aside from its lack of resources in opinion-mining tasks, Amharic presents numerous difficulties because of its complex structure and variety of dialects. Analyzing every comment written in Amharic is a challenging task. Significant advancements in opinion mining have been achieved using deep learning. An opinion-mining model was used in this study to classify user comments written in Amharic as positive or negative. The domains that we focus on in this study are YouTube and Facebook. From the Ethiopian broadcasts YouTube and Facebook official pages, we gathered 11,872 unstructured data for this study using www.exportcomment.com , and Facebook page tools. Text preprocessing and feature extraction techniques were used, in addition to manual annotation by linguistic specialists. The dataset was prepared for the experiment after annotation, preprocessing, and representation. LSTM, GRU, BiGRU, BiLSTM, and a hybrid of CNN with BiLSTM classifiers from the TensorFlow Keras deep learning library were used to train the model using the dataset, which was split using the 80/20 train-test method, which proved effective for classification problems. Finally, we achieved of 94.27%, 95.20%, 95.49%, 95.62%, and 96.08% using GRU, BiGRU, LSTM, BiLSTM, and CNN with BiLSTM, respectively, in word2vec embedding model.

Combined LinkNet-MBi-LSTM for brain activity recognition with new Stockwell transform features.

Recognizing brain activity from EEG waves is an important field of study in biomedical engineering and neuroscience. Conventional approaches usually begin with signal processing techniques to extract features from the EEG data, and then machine learning algorithms are applied to classify the data. However, the spatial resolution of these EEG signals is low, which makes it difficult to pinpoint the exact location of the neural activity source in the brain. There are ongoing initiatives to use DL-based brain activity recognition algorithms to overcome these constraints. Therefore, this work presents a novel hybrid framework for brain activity detection using the enhanced Stockwell transform and an EEG signal that is called LinkNet and modified bidirectional-long short-term memory (LN-MBi-LSTM) model. This framework follows a methodical approach that includes stages for feature extraction, brain activity recognition and preprocessing. Firstly, the improved Weiner filtering (IWF) approach is used to preprocess the EEG input signal. The relevant features are then extracted using a feature extraction technique from the preprocessed EEG signal. To identify the brain activity, these recovered feature sets are subsequently processed separately using LinkNet and modified bidirectional-long short-term memory (MBi-LSTM). A thorough analysis that takes into account both simulation and experimental calculations is part of the validation process for the LN-MBi-LSTM model. Finally, this study demonstrates the therapeutic potential of the LN-MBi-LSTM framework by presenting a strong and verified model for brain activity recognition. With the highest precision of 0.997, the LinkNet-MBi-LSTM model distinguishes itself from other models and confirms its exceptional capacity to produce accurate positive predictions.

Comparative Evaluation of Camelina Seed Oils Obtained by Cold-Pressing and Solvent Extraction

This study aimed to analyze the physicochemical properties and nutritional quality of oil extracted from the camelina seed genotypes NS Zlatka and NS Slatka, grown in Serbia, using both Soxhlet extraction with n-hexane and the cold-pressing technique. Extraction technique did not have an effect on oil yield. Camelina oils exhibited satisfactory physicochemical characteristics, which were influenced by the extraction methods. The oils were rich in polyunsaturated fatty acids, with α-linolenic acid being the most abundant. They were characterized by a balanced ω-6 to ω-3 ratio (0.5), low atherogenicity index and thrombogenicity index values, and a relatively high hypocholesterolemic/hypercholesterolemic ratio. Cold-pressed oils contained significantly higher amounts of α- and γ-tocopherols and showed greater oxidative stability at moderate temperatures, as confirmed by the Schaal oven test. Despite this, their oxidative stability decreased at elevated temperatures (Rancimat test) compared to solvent-extracted oils. Conversely, solvent-extracted oils had higher levels of β-carotene and showed superior resistance to high-temperature conditions. Due to its unique characteristics, nutritional properties, and health-promoting attributes, cold-pressed camelina oil presents significant potential for application in food, nutraceutical, feed, and cosmetic industries.

Optimization of Sargassum bovianum Extraction Techniques for Germination of Wheat, Canola, and Corn Under Different Salinity Stress

Seaweeds are a cheap, eco-friendly, and rich source of plant growth stimulators that can mitigate the adverse effects of salinity stress. This study examined the impact of Sargassum bovianum extracts obtained through different techniques using pressure, heat, and microwave radiations on the germination and growth of wheat, corn, and canola seeds under varying salinity levels (500, 3500, and 6500 µS cm−1). The findings showed that pressure, microwave, and acidic extraction methods were the most effective in extracting polysaccharides, alginate, and nutrients from S. bovianum. Seaweed extract significantly improved the mean germination time (MGT) and germination index (GI) of wheat under high salinity stress and had a positive effect on wheat plumule length (PL) and germination percentage (GP). However, seaweed extract had no significant impact on canola seeds in salinity stress, except for improved canola PL. The PL and seedling vigor index (SVI) of corn were enhanced in low salinity levels, but most treatments reduced PL and SVI in high salinity. This study suggests that using heat, pressure, and microwave techniques for seaweed extraction results in higher polysaccharides and alginate content, leading to improved germination and plant growth, particularly in wheat and canola. These findings can help growers optimize the germination and growth of these important crops.

SOCIAL MEDIA FAKE ACCOUNT IDENTIFICATION USING ML

The proliferation of social media platforms has led to an increase in the creation of fake accounts. These accounts are used for various malicious activities, such as spreading false information, phishing, and identity theft. As a result, there is a growing need for effective methods to identify and eliminate fake accounts. This paper proposes a machine learning-based approach for social media fake account identification. This paper proposes a machine learning-based approach to identify fake accounts on social media platforms. Our method leverages a combination of feature extraction techniques and supervised learning algorithms to classify accounts as genuine or fake. We collect a large dataset of labeled social media accounts, including both genuine and fake profiles, and extract features from various sources such as profile information, network behavior, and content analysis. We experiment with multiple machine learning models, including Support vector machines(SVM),K-Nearest Neighbors Algorithm(KNN),Random forest, Logistic Regression & Artificial Neural Network(ANN) to evaluate their performance in identifying fake accounts. Our proposed method has significant implications for social media platform operators, policymakers, and researchers seeking to combat fake accounts and maintain online trust. The approach can be integrated with existing social media moderation tools to enhance the accuracy and efficiency of fake account identification. Key Words: Support vector machines(SVM),K-Nearest Neighbors Algorithm(KNN),Random forest, Logistic Regression & Artificial Neural Network(ANN),Python.

Mamba-VNPS: A Visual Navigation and Positioning System with State-Selection Space

This study was designed to address the challenges of autonomous navigation facing UAVs in urban air mobility environments without GPS. Unlike traditional localization methods that rely heavily on GPS and pre-mapped routes, Mamba-VNPS leverages a self-supervised learning framework and advanced feature extraction techniques to achieve robust real-time localization without external signal dependence. The results show that Mamba-VNPS significantly outperforms traditional methods across multiple aspects, including localization error. These innovations provide a scalable and effective solution for UAV navigation, enhancing operational efficiency in complex spaces. This study highlights the urgent need for adaptive positioning systems in urban air mobility (UAM) and provides a methodology for future research on autonomous navigation technologies in both aerial and ground applications.

Advancements in Geothermal Energy: Extraction Methods, Utilization Practices, and Future Improvements

Abstract. As a matter of fact, on account of the increasing energy requirements in the world and pressure on environmental conservation globally, geothermal power is becoming an increasingly important source of energy especially in recent years. With this in mind, the objective of this study is to examine as well as investigate developments in energy focusing on extraction techniques, utilization practices and future trends. At the same time, it also scrutinizes how Enhanced Geothermal Systems (EGS) have been evolving and their capabilities in terms of accessing reserves as well as integration with other sustainable energy types. Notably, advances in drilling and heat transfer technologies have significantly improved efficiency of systems. According to the analysis, the report concludes that despite existing challenges such as costs, geothermal energys negligible environmental impact coupled with technological advancements has positioned it as a worthwhile green alternative for tomorrow. These results shed light on guiding further exploration of geothermal energy.