Central Processing Research Articles

Apocynin alleviates thioacetamide-induced acute liver injury: Role of NOX1/NOX4/NF-κB/NLRP3 pathways

The liver has a distinctive capacity to regenerate, yet severe acute injury can be life-threatening if not treated appropriately. Inflammation and oxidative stress are central processes implicated in the pathophysiology of acute livery injury. NOX isoforms are important enzymes for ROS generation, NF-κB and NLRP3 activation, its inhibition could be vital in alleviating acute liver injury (ALI). Here in our study, we used apocynin, a natural occurring potent NOX inhibitor, to exploreits potential protective effect against thioacetamide (TAA)-induced ALI through modulating crucial oxidative and inflammatory pathways. Rats were injected once with TAA (500 mg/kg/i.p) and treated with apocynin (10 mg/kg/i.p) twice before TAA challenge. Sera and hepatic tissues were collected for biochemical, mRNA expression, western blot analysis and histopathological assessments. Pretreatment with apocynin improved liver dysfunction evidenced by decreased levels of aminotransferases, ALP, GGT and bilirubin. Apocynin reduced mRNA expression of NOX1 and NOX4 which in turn alleviated oxidative stress, as shown by reduction in MDA and NOx levels, and elevation in GSH levels andcatalase and SOD activities. Moreover, apocynin significantly reduced MPO gene expression. We also demonstrate that apocynin ameliorated inflammation through activating IκBα and suppressing IKKα, IKKβ, NF-κBp65 and p-NF-κBp65, IL-6 andTNF-α. Additionally, apocynin potentiated the gene expression of anti-inflammatory IL-10 and reduced levels of hepatic NLRP3, Caspase-1 and IL-1β. These results suggest that apocynin protects against ALI in association with the inhibition of NOX1 and NOX4 and regulating oxidative and inflammatory pathways.

People who have more science education rely less on misinformation—Even if they do not necessarily follow the health recommendations

AbstractRecent research has highlighted the role of science education in reducing beliefs in science‐related misinformation and stressed its potential positive impact on decision‐making and behavior. This study implemented the Elaboration Likelihood Model to explore how individuals' abilities and motivation interact with the type of processing of scientific information in the peripheral vs. central persuasion routes. A representative sample of adults (N = 500) completed an online questionnaire during the second wave of COVID‐19 (November 2020) focused on two COVID‐19‐related dilemmas involving social distancing recommendations. First, we examined whether relying on misinformation was associated with participants' stances and the complexity of their arguments and found that relying on misinformation was associated with the intention to reject social distancing recommendations and with the use of simple arguments. Second, we explored how motivation, operationalized as personal relevance, and abilities, operationalized as the highest level of science education, science knowledge, and strategies to identify misinformation, were associated with viewpoints and justifications. We found that personal relevance was associated with the intention to reject the recommendations but also with more complex arguments, suggesting that people did not intend to reject scientific knowledge but rather tended to contextualize it. Abilities were not associated with stance but were positively correlated with argument complexity. Finally, we examined whether motivation and abilities are associated with relying on scientific misinformation when making science‐related decisions. Respondents with higher levels of science education and motivation relied less on misinformation, even if they did not necessarily intend to follow the health recommendations. This implies that motivation directs people to greater usage of the central processing route, resulting in more deliberative use of information. Science education, it appears, impacts the information evaluation decision‐making process more than its outcome.

Anomalous Temperature Dependence of the Triplet-Triplet Energy Transfer in Cereibacter sphaeroides I(L177)H Mutant Reaction Centers.

In photosynthetic reaction centers, quenching of the primary donor triplet state by energy transfer to the carotenoid molecule provides efficient suppression of generation of singlet-excited oxygen, potent chemical oxidant. This process in the Cereibacter sphaeroides reaction centers is thermoactivated, and discontinues at temperatures below 40K. In these reaction centers, substitution of amino acid residue isoleucine at the 177position of the L-subunit with histidine results in the sharp decrease of activation energy, so that the carotenoid triplets are populated even at 10K. Activation energy of the T-T energy transfer was estimated as 7.5cm-1, which is more than 10-fold lower than activation energy in the wild type reaction centers. At certain temperatures, the energy transfer in the mutant is decelerated, which is related to the increase of effective distance of the triplet-triplet transfer. To the best of our knowledge, the described mutation presents the first reaction center modification leading to the significant decrease in activation energy of the T-T energy transfer to carotenoid molecule. TheI(L177)H mutant reaction centers present a considerable interest for further studies of the triplet state quenching mechanisms, and of other photophysical and photochemical processes in the reaction centers of bacterial photosynthesis.

Cannabidiol Modulation of Nicotine‐Induced Toxicity: Assessing Effects on Behavior, Brain‐Derived Neurotrophic Factor, and Oxidative Stress in C57BL/6 Male Mice

ABSTRACTHigh doses of nicotine administered to rodents serve as a model for studying anxiety and test compounds' potential anxiolytic effects. At these doses, anxiety in rodents is accompanied by disruption of brain‐derived neurotrophic factor (BDNF). The endocannabinoids and nicotine modulate several central nervous system processes via their specific receptors, impacting locomotion, anxiety, memory, nociception, and reward. Cannabidiol (CBD), an active ingredient of Cannabis sativa L., is devoid of psychoactive actions and has gained attention for its anxiolytic, antioxidant, and anti‐inflammatory properties, among others. This work aims to examine the potential anxiety‐reducing properties of CBD in a well‐established experimental mouse model of anxiety‐like behavior induced by high doses of nicotine on male C57BL/6 mice. In this context, the open‐field behavioral test was specially conducted to assess CBD's effects on anxiety‐like behavior and locomotion. Brain neuronal plasticity, modulated by BDNF, along with a diverse array of blood's metabolic markers, was examined as a means of evaluating systemic toxicity under various treatments. Finally, oxidative stress was evaluated through the measurement of glutathione (GSH), superoxide dismutase (SOD), and malondialdehyde (MDA), while pro‐inflammatory cytokine assessments were conducted to evaluate redox status and immune system function. Our research suggests that CBD shows potential in reducing anxiety‐like behaviors induced by high doses of nicotine, by mitigating changes in BDNF protein levels in cerebral hemispheres and cerebellum. At the same time, CBD targets specific liver enzymes, maintains tissue's systemic toxicity (i.e., renal, kidney, and pancreatic), balances redox status (SOD, GSH, and MDA), and regulates the secretion of pro‐inflammatory cytokines (TNF‐alpha and IL‐6).

Inflammatory biomarkers in depression: scoping review.

Inflammation is increasingly recognised as a fundamental component of the pathophysiology of major depressive disorder (MDD), with a variety of inflammatory biomarkers playing pivotal roles. These markers are closely linked to both the severity of symptoms and the responsiveness to treatments in MDD. This scoping review aims to explore the scientific literature investigating the complex relationships between inflammatory biomarkers and depression, by identifying new studies and critical issues in current research. Following the PRISMA Extension for Scoping Reviews guidelines, we systematically searched databases including PubMed, Scopus, PsycINFO, Open Grey and Cochrane Library. Our search focused on articles published from 1 January 2020 to 1 May 2024. We included studies evaluating inflammatory biomarkers in adult patients with MDD, utilising observational and randomised controlled trial designs, and review studies. Our analysis examined 44 studies on the complex interplay between inflammation and its multiple effects on MDD. Significant associations between specific inflammatory biomarkers and depression severity were found, requiring cautious interpretation. We also highlight several methodological limitations in the current studies, which warrant caution in directly applying these findings to clinical practice. However, identified methodologies show potential for using these biomarkers as diagnostic tools or therapeutic targets, including anti-inflammatory interventions. The findings emphasise the need for sophisticated, integrative research to understand inflammation's role in MDD. Future studies should identify specific biomarker panels for diagnosing depression and bridging peripheral biomarker measurements with central neuroinflammatory processes, leading to better diagnostic and treatment strategies.

Mechanisms by which microbiome-derived metabolites exert their impacts on neurodegeneration.

Recent developments in microbiome research suggest that the gut microbiome may remotely modulate central and peripheral neuronal processes, ranging from early brain development to age-related changes. Dysbiotic microbiome configurations have been increasingly associated with neurological disorders, such as neurodegeneration, but causal understanding of these associations remains limited. Most mechanisms explaining how the microbiome may induce such remote neuronal effects involve microbially modulated metabolites that influx into the 'sterile' host. Some metabolites are able to cross the blood-brain barrier (BBB) to reach the central nervous system, where they can impact a variety of cells and processes. Alternatively, metabolites may directly signal to peripheral nerves to act as neurotransmitters or exert modulatory functions, or impact immune responses, which, in turn, modulate neuronal function and associated disease propensity. Herein, we review the current knowledge highlighting microbiome-modulated metabolite impacts on neuronal disease, while discussing unknowns, controversies and prospects impacting this rapidly evolving research field.

IoT based Smart Aquaculture Monitoring System for Fish Tank Management

Aquaculture, the farming of aquatic organisms, plays a critical role in meeting the increasing global demand for seafood. Effective monitoring and management of aquaculture systems are essential for ensuring optimal conditions and maximizing productivity. This research presents a comprehensive aquaculture-based fish tank monitoring system designed to enhance operational efficiency and fish welfare. At the heart of the system is the Arduino Uno microcontroller, serving as the central processing unit to coordinate various functions. Key components include pH sensor for continuous monitoring of acidity levels, turbidity sensor for detecting suspended particles, and water level sensor for maintaining optical water levels within the tank. In the event of abnormal pH levels or high turbidity, the system triggers automated alerts, sending notifications to the designated owner through SMS and transmitting data to an IoT website for remote monitoring. To ensure consistent environmental conditions, two pump motors are utilized: one for automatic water replenishment in case of water level fluctuations and another for removing water in case of particle accumulation. Furthermore, the system integrates a servo motor mechanism for precise dispensing of fish feed at predetermined intervals, promoting healthy growth and nutrition. Control over pump motors and feeding schedules is facilitated through a user-friendly website interface, providing operators with seamless management capabilities.

Design and Development of an IoT-Based Leakage Detection System for Kitchens

In Nigeria, the frequent loss of lives and properties due to LPG usage for cooking necessitates urgent intervention. This paper presents an Internet of Things (IoT) based detection system to monitor LPG leakage in kitchens. Key components include a solenoid valve for gas flow control, an MQ6 sensor for LPG monitoring, and an STM32F411CEU6 microcontroller, programmed in embedded C, as the central processing unit of the system. The system introduces an emergency shutdown mechanism using an MMBT3904 NPN transistor. Upon detecting a leak, the MQ6 sensor informs the microcontroller, which prompts the user with a Short Message Service (SMS) and calls, and then triggers the transistor to close the solenoid valve if the leakage poses an explosion risk, thus preventing potential disasters. Additionally, a custom mobile app allows for remote monitoring and control of LPG leakage. To address Nigeria's unreliable electricity supply, the system includes a 5V power pack, a solar panel, and a battery for continuous power supply to the system. Deploying this system will significantly reduce the risks associated with LPG leaks in kitchens across Nigeria.

Distributed Joint Beamforming for RIS-assisted Cell-Free MIMO with Discrete Phase Shifts

Abstract Both reconfiurable intelligent surface (RIS) and cell-free multiple-input multiple-output (CF-MIMO) system are holding promise to become the predominant research direction in 6G networks. This paper studies the problem of weighted sum-rate maximization(WSR) in RIS-assisted CF-MIMO networks with discrete phase shifts. We utilize alternating optimization (AO) to iteratively calculate active and passive beamforming. At access points (APs) side, we employ a distributed algorithm to design the active beamforming, leveraging information interact among APs without centralized designing at central processing unit(CPU). Facing the constraint of discrete phase shift in RIS, we adopt semidefinite relaxation(SDR) projection and proximal gradient descent(PGD) to design the passive beamforming respectively. Simulation results indicate that our proposed algorithms achieve performance comparable to the distributed framework with continuous phase shift while reducing the computational time.

Design and Development of an Intelligent Laboratory Management System Based on STM Processors

Abstract In response to the escalating inventory of laboratory equipment, the inadequacy of traditional manual management practices has become increasingly apparent. Current management challenges are underscored by historical data, which reveals a borrowing rate of approximately 30%, a concerning return rate of only about 5%, and an equipment loss rate of 10%. To address these inefficiencies, this paper introduces an advanced, intelligent laboratory equipment management system leveraging STM32 and GM65 technologies, designed to significantly enhance the return rate and minimize equipment loss. The system is meticulously engineered, featuring three integral modules: the scanning module, the core board module, and the communication module. The scanning module employs GM65 technology to rapidly scan and decode QR codes, facilitating the swift transmission of critical data to the core board for intelligent processing and interaction with the laboratory equipment. At the heart of the system, the core board module serves as the central processing unit, adeptly managing data from the scanning module. It intelligently discerns various information types and dynamically updates the TFT-LCD screen with the current status of equipment, ensuring that all users have access to real-time and accurate information regarding the borrowing and returning of equipment. The communication module is pivotal in bolstering the system's connectivity capabilities. Designed with a robust structure, the system is characterized by its simplicity of operation and robust practicality. It not only significantly improves the efficiency of laboratory equipment management but also plays a transformative role in modernizing laboratory management practices. By streamlining the management process and ensuring the optimal utilization of resources, this system provides a solid foundation for the sustainable advancement of laboratory operations.

Home Automation System using OpenCV and Arduino

The research focuses on the integration of computer vision capabilities through OpenCV with Arduino-based hardware to create an efficient and versatile home automation system. The system employs cameras for image processing, allowing it to recognize and respond to various environmental changes within the home. The hardware setup utilizes Arduino microcontrollers as the central processing units, enabling seamless communication between the OpenCV algorithms and the connected devices. A user-friendly interface facilitates remote monitoring and control of home appliances through a mobile or web application. By combining the power of OpenCV for image processing and Arduino for real-time control, this home automation system provides a smart and adaptive solution for enhancing security, energy efficiency, and overall convenience within a household. The research’s modular design allows for future expansions and integration with emerging technologies, ensuring scalability and relevance in the rapidly evolving field of smart home automation.

Factors determining high academic attainment in primary school (based on the longitudinal study of year 2 and year 4 Russian schoolchildren)

Introduction. The study attempts to solve the problem of predicting academic attainment in primary school. The main emphasis in selecting factors to predict success in learning was on personal and contextual ones. Among personal factors, executive functions were considered, which are described by many authors as significant for successful learning. Among the contextual factors, the main focus is on marks given to schoolchildren in each subject by their teacher. An attempt is made to predict children’s academic attainment in Year 4 based on data on academic attainment in Year 2. Materials and Methods. 53 primary schoolchildren were examined first in Year 2 and then in Year 4. The following executive functions were studied: working memory (using the O. M. Razumnikova’s technique) and inhibitory control (using E. G. Vergunova and E. I. Nikolaeva’s inventory). In addition, final marks in all subjects were analyzed for each year. All schoolchildren were assigned to one of the following groups: ‘excellent students’, ‘good students’, ‘C students’. This parameter was called ‘academic attainment’. First, using factor analysis, correlations between the studied parameters were identified separately for Year 2 and Year 4, then a regression analysis was carried out, with the help of which the factors influencing high academic attainment in Year 2 and Year 4 for the same children were identified. Finally, a factor analysis was conducted in order to assess academic attainment in Year 4 based on data collected in Year 2. Results. Factor analysis revealed that marks given to primary schoolchildren by the main teacher in Year 2 correlate with each other, that is, a halo effect is noted when marks are given not for a particular student’s answer, but according to teacher’s general impression about them. As part of the regression analysis, it was shown that in Year 2, academic attainment is determined only by final marks in the Russian language. Only in Year 4 the influence of inhibitory control and working memory on academic attainment was found. An attempt to predict academic attainment based on the results of Year 2 showed that academic attainment in Year 4 can only be predicted on the basis of final assessment in the Russian language. It can be assumed that this subject is more difficult for children than others; therefore it requires maximum inclusion of executive functions. Conclusions. The study revealed that executive functions do not affect academic attainment in Year 2, but inhibitory control is a significant factor predicting academic attainment in Year 4. Inhibitory control at the behavioral level is assessed as the level of manifestation of volitional quality in a child. A significant relationship was found in Year 4 between high scores in Reading and high scores in Music, Art, and Science. This indicates that central processes that underlie activities in these subjects are interconnected. Finally, it was found that a significant factor predicting academic attainment in Year 4 is the mark for Russian language in Year 2.

Life Cycle Assessment of Natural Gas Production in Java Sea-East Java Province, Indonesia

Energy products including natural gas play a very important role in the context of global economic, industrial, and infrastructure development. However, it is undeniable that the production process from raw material extraction to ready-to-use products has an impact on the environment. This study was conducted to evaluate the environmental impact of natural gas production in the Kepodang field, East Java Province, Indonesia. The next objective is to analyze the magnitude and significance of the impact or hotspot analysis. The characterization results for 4 main categories show: Global Warming Potential 70.02 kg CO₂ eq, Ozone Layer Depletion 1.11E-06 kg CFC-11 eq, Acidification Potential 0.65 mol H+ eq, Marine Eutrophication 0.30 kg N eq, and Freshwater Eutrophication 2.17E-03kg P eq. The results also show that throughout the life cycle of this product, the largest contributor to environmental impacts is in the downstream phase, especially the user gas metering unit process. This is due to the 200 km of pipelines for product transportation from the Central Processing Platform (CPP) to the user gate. Pipeline parameters (Ecoinvent database) refer to long-distance pipelines with high capacity. In addition, hotspots were also identified in the core phase: gas measurement and chromatography, gas turbine generators, and flare gas.

Superexchange Electron Transfer and Protein Matrix in the Charge-Separation Process of Photosynthetic Reaction Centers.

In type-II reaction centers, such as photosystem II (PSII) and reaction centers from purple bacteria (PbRC), light-induced charge separation involves electron transfer from pheophytin (PheoD1) to quinone (QA), occurring near a conserved tryptophan residue (D2-Trp253 in PSII and Trp-M252 in PbRC). This study investigates the route of the PheoD1-to-QA electron transfer, focusing on the superexchange coupling (|HPheoD1···QA|) in the PSII protein environment. |HPheoD1···QA| is significantly larger for the PheoD1-to-QA electron transfer via the unoccupied molecular orbitals of D2-Trp253 ([Trp]•--like intermediate state, 0.73 meV) compared to direct electron transfer (0.13 meV), suggesting that superexchange is the dominant mechanism in the PSII protein environment. While the overall impact of the protein environment is limited, local interactions, particularly H-bonds, enhance superexchange electron transfer by directly affecting the delocalization of molecular orbitals. The D2-W253F mutation significantly decreases the electron transfer rate. The conservation of D2-Trp253/D1-Phe255 (Trp-M252/Phe-L216 in PbRC) in the two branches appears to differentiate superexchange coupling, contributing to the branches being either active or inactive in electron transfer.

Amylum forms typical self-assembled amyloid fibrils.

Amyloid fibril formation is a central biochemical process in pathology and physiology. Over decades, substantial advances were made in elucidating the mechanisms of amyloidogenesis, its links to disease, and the production of functional supramolecular structures. While the term "amyloid" denotes starch-like features of these assemblies, no evidence of amyloidogenic behavior of polysaccharides has been so far reported. Here, we investigate the potential of amylum (starch) not only to self-assemble into hierarchical fibrillar structures but also to exhibit canonical amyloidogenic properties. Ordered amylum structures were formed through a sigmoidal growth process with characteristic amyloid features including typical nanofibril morphology, binding to indicative dyes, inherent luminescence, apple-green birefringence upon Congo red staining, and notable mechanical rigidity. These findings shed light on polysaccharide self-assembly and expand the generic amyloid phenomenon.

Painful stimulation increases functional connectivity between supplementary motor area and thalamus in patients with small fibre neuropathy.

The lead symptom of small fibre neuropathy (SFN) is neuropathic pain. Recent functional magnetic resonance imaging (fMRI) studies have indicated central changes in SFN patients of different etiologies. However, less is known about brain functional connectivity during acute pain processing in idiopathic SFN. We conducted fMRI with thermal heat pain application (left volar forearm) in 32 idiopathic SFN patients and 31 healthy controls. We performed functional connectivity analyses with right supplementary motor area (SMA), left insula, and left caudate nucleus (CN) as seed regions, respectively. Since pathogenic gain-of-function variants in voltage gated sodium channels (Nav) have been linked to SFN pathophysiology, explorative connectivity analyses were performed in a homogenous subsample of patients carrying rare heterozygous missense variants. For right SMA, we found significantly higher connectivity with the right thalamus in SFN patients compared to controls. This connectivity correlated significantly with intraepidermal nerve fibre density, suggesting a link between peripheral and central pain processing. We found significantly reduced connections between right SMA and right middle frontal gyrus in patients with Nav variants. Likewise, connectivity between left CN and right frontal pole was decreased. Aberrant functional connectivity in SFN is in line with previous research on other chronic pain syndromes. Functional connectivity changes may be linked to SFN, highlighting the need to determine if they result from peripheral changes causing abnormal somatosensory processing. This understanding may be crucial for assessing their impact on painful symptoms and therapy response. We found increased functional connectivity between SMA and thalamus during painful stimulation in patients with idiopathic SFN. Connectivity correlated significantly with intraepidermal nerve fibre density, suggesting a link between peripheral and central pain processing. Our findings emphasize the importance of investigating functional connectivity changes as a potential feature of SFN.

Phagosome Isolation with Magnetic Beads and Purification of Toll-Like Receptor (TLR) Complexes in Phagosomes.

Phagocytosis is a central process by which macrophage cells internalize and eliminate microbes as well as apoptotic cells. The nascent phagosome undergoes a complex maturation process involving sequential fusion with endosomal compartments. The endosomal TLRs, including TLR3, -7, -8, and -9, play a critical role in innate immunity by sensing bacterial or viral nucleic acids and are preferentially transported to the phagosomal membrane of innate immune cells upon activation. Therefore, phagosome isolation is helpful for studies on pathogenic invasion and the functions of phagosome proteins, including endosomal TLRs.

Genome-wide analysis of the biophysical properties of chromatin and nuclear proteins in living cells with Hi-D.

To understand the dynamic nature of the genome, the localization and rearrangement of DNA and DNA-binding proteins must be analyzed across the entire nucleus of single living cells. Recently, we developed a computational light microscopy technique, called high-resolution diffusion (Hi-D) mapping, which can accurately detect, classify and map diffusion dynamics and biophysical parameters such as the diffusion constant, the anomalous exponent, drift velocity and model physical diffusion from the data at a high spatial resolution across the genome in living cells. Hi-D combines dense optical flow to detect and track local chromatin and nuclear protein motion genome-wide and Bayesian inference to characterize this local movement at nanoscale resolution. Here we present the Python implementation of Hi-D, with an option for parallelizing the calculations to run on multicore central processing units (CPUs). The functionality of Hi-D is presented to the users via user-friendly documented Python notebooks. Hi-D reduces the analysis time to less than 1 h using a multicore CPU with a single compute node. We also present different applications of Hi-D for live-imaging of DNA, histone H2B and RNA polymerase II sequences acquired with spinning disk confocal and super-resolution structured illumination microscopy.

Limit theorems of occupation times of normalized binary contact path processes on lattices

Abstract The binary contact path process (BCPP) introduced in Griffeath (1983) describes the spread of an epidemic on a graph and is an auxiliary model in the study of improving upper bounds of the critical value of the contact process. In this paper, we are concerned with limit theorems of the occupation time of a normalized version of the BCPP (NBCPP) on a lattice. We first show that the law of large numbers of the occupation time process is driven by the identity function when the dimension of the lattice is at least 3 and the infection rate of the model is sufficiently large conditioned on the initial state of the NBCPP being distributed with a particular invariant distribution. Then we show that the centered occupation time process of the NBCPP converges in finite-dimensional distributions to a Brownian motion when the dimension of the lattice and the infection rate of the model are sufficiently large and the initial state of the NBCPP is distributed with the aforementioned invariant distribution.

Revealing Hidden Features of Chaotic Systems Using High-Performance Bifurcation Analysis Tools Based on CUDA Technology

Bifurcation analysis is an essential tool in nonlinear dynamics. Bifurcation diagrams help to discover subtle features of investigated dynamics such as chaotic and periodic regimes, hidden attractors and fixed points. However, plotting high-resolution bifurcation diagrams can be a computationally challenging task, especially in multiparametric evaluation. It should be noted that the bifurcation analysis is a task with natural parallelism and thus can be efficiently solved using hybrid central-graphics processing architectures. In this paper, we propose an advanced algorithm and special software for plotting bifurcation diagrams using calculations accelerated by graphics processing unit in combination with a highly efficient semi-implicit ordinary differential equation solver. Time series processing is based on the extraction of amplitude and phase features for the density-based spatial clustering of applications with noise to determine oscillation periodicity. We showcase the features of the application of proposed solutions on a set of test chaotic systems. The performance of the analysis algorithms is investigated in comparison with conventional solutions based on central processing unit and several approaches known from the literature. We explicitly show that the proposed algorithm outperforms known solutions in both calculation speed and precision.