Actual Function Research Articles

Discovery and verification of mmu_Circ_26986/hsa_Circ_0072463 as a potential biomarker and intervention target for sepsis-associated acute kidney injury

Approximately 60% of septic patients developed acute kidney injury (AKI). The mortality rate of septic AKI (SA-AKI) is two to three times higher than that of septic without AKI (SA-non-AKI). The actual functions and mechanisms of CircRNAs in the pathophysiology of SA-AKI remain incompletely understood. Herein, we observed that the mmu_Circ_26986 could be induced by lipopolysaccharide (LPS) and cecum ligation and puncture (CLP) in BUMPT cell line and C57BL/6 mouse kidney, respectively. Functionally, mmu_Circ_26986 suppressed BUMPT cell apoptosis induced by LPS. Mechanistically, mmu_Circ_26986 sponged miRNA-29b-1-5p to upregulate the expression of PAK7. Overexpression of mmu_Circ_26986 ameliorated the progression of CLP-stimulated AKI through miRNA-29b-1-5p/PAK7 axis. In addition, we found that hsa_Circ_0072463, homologous to mmu_Circ_26986, suppressed LPS-induced HK-2 cells apoptosis via regulation of miRNA-29b-1-5p/PAK7 axis. Furthermore, sepsis patients with AKI had a higher level of hsa_Circ_0072463 compared to those without AKI. The sensitivity, specificity and AUC of hsa_Circ_0072463 were 78.8%, 87.9% and 0.866, respectively. Spearman's test indicated a noticeable positive correlation between plasma hsa_Circ_0072463 and serum creatinine in sepsis patients (r = 0.725). In summary, this study reveals that the mmu_Circ_26986/hsa_Circ_0072463 miRNA-29b-1-5p/PAK7 axis mediates septic AKI, and hsa_Circ_0072463 is a potential diagnostic marker for septic AKI.

Modelled forest ecosystem carbon–nitrogen dynamics with integrated mycorrhizal processes under elevated CO2

Abstract. Almost 95 % of all terrestrial plant species form symbioses with mycorrhizal fungi that mediate plant–soil interactions: mycorrhizae facilitate plant nitrogen (N) acquisition and are, therefore, vital for plant growth, but they also build a pathway for plant-assimilated carbon (C) into the rhizosphere. Therefore, mycorrhizae likely play an important role in shaping the response of ecosystems to environmental changes such as rising atmospheric carbon dioxide (CO2) concentrations, which can increase plant N demand and the transfer of plant C assimilation to the soil. While the importance of mycorrhizal fungi is widely recognised, they are rarely represented in current terrestrial biosphere models (TBMs) explicitly. Here, we present a novel, dynamic plant–mycorrhiza–soil model as part of the QUINCY (QUantifying Interactions between terrestrial Nutrient CYcles and the climate system) TBM. This new model is based on mycorrhizal functional types that either actively mine soil organic matter (SOM) for N or enhance soil microbial activity through increased transfer of labile C into the rhizosphere, thereby (passively) priming SOM decomposition. Using the Duke Free-Air CO2 Enrichment (FACE) experiment, we show that mycorrhizal fungi can have important effects on projected SOM turnover and plant nutrition under ambient as well as elevated-CO2 treatments. Specifically, we find that including enhanced active mining of SOM for N in the model allows one to more closely match the observations with respect to observed decadal responses of plant growth, plant N acquisition and soil C dynamics to elevated CO2, whereas a simple enhancement of SOM turnover by increased below-ground C transfer of mycorrhizae is unable to replicate the observed responses. We provide an extensive parameter uncertainty study to investigate the robustness of our findings with respect to model parameters that cannot readily be constrained by observations. Our study points to the importance of implementing mycorrhizal functionalities in TBMs as well as to further observational needs to better constrain mycorrhizal models and to close the existing major knowledge gaps in actual mycorrhizal functioning.

Hydronephrosis severity as a predictor of postoperative renal function decline following laparoscopic radical nephroureterectomy.

This study aimed to investigate factors, including the degree of hydronephrosis, that may be associated with decreased renal function after radical nephroureterectomy (RNU). This study included 252 patients who underwent laparoscopic RNU with an estimated glomerular filtration rate (eGFR) ≥ 30ml/min/1.73 m2 in three institutions. We assessed the association between hydronephrosis grade and perioperative renal function and performed a stepwise multivariate linear regression analysis to identify factors associated with postoperative eGFR. Patients with preoperative eGFR ≥ 50ml/min/1.73 m2 were divided into a training set and an independent external validation set to develop a predictive model for postoperative renal function. The median preoperative and postoperative eGFR were 61.1 and 46.4ml/min/1.73 m2, respectively. The eGFR preservation rates were 66.9%, 66.6%, 88.1%, and 100.0% in groups without, with mild, moderate, and severe hydronephrosis, respectively, and this difference was statistically significant (p < 0.001). Multivariate analysis revealed that factors predictive of postoperative eGFR included sex, preoperative eGFR, clinical T stage (cT3-4), and the presence of moderate or severe hydronephrosis. Our predictive model, based on these factors, positively correlated with actual postoperative renal function, and the similarity in categories with or without renal function insufficiency between predicted and actual postoperative renal functions was 78% in both training and validation sets. Moderate or severe hydronephrosis is associated with a modest postoperative decline in renal function, while mild hydronephrosis is not. Our predictive model may be useful in predicting postoperative renal function insufficiency and guiding decision-making for perioperative medical treatment.

Predicting cognitive scores from wearable-based digital physiological features using machine learning: data from a clinical trial in mild cognitive impairment

BackgroundContinuous assessment and remote monitoring of cognitive function in individuals with mild cognitive impairment (MCI) enables tracking therapeutic effects and modifying treatment to achieve better clinical outcomes. While standardized neuropsychological tests are inconvenient for this purpose, wearable sensor technology collecting physiological and behavioral data looks promising to provide proxy measures of cognitive function. The objective of this study was to evaluate the predictive ability of digital physiological features, based on sensor data from wrist-worn wearables, in determining neuropsychological test scores in individuals with MCI.MethodsWe used the dataset collected from a 10-week single-arm clinical trial in older adults (50–70 years old) diagnosed with amnestic MCI (N = 30) who received a digitally delivered multidomain therapeutic intervention. Cognitive performance was assessed before and after the intervention using the Neuropsychological Test Battery (NTB) from which composite scores were calculated (executive function, processing speed, immediate memory, delayed memory and global cognition). The Empatica E4, a wrist-wearable medical-grade device, was used to collect physiological data including blood volume pulse, electrodermal activity, and skin temperature. We processed sensors’ data and extracted a range of physiological features. We used interpolated NTB scores for 10-day intervals to test predictability of scores over short periods and to leverage the maximum of wearable data available. In addition, we used individually centered data which represents deviations from personal baselines. Supervised machine learning was used to train models predicting NTB scores from digital physiological features and demographics. Performance was evaluated using “leave-one-subject-out” and “leave-one-interval-out” cross-validation.ResultsThe final sample included 96 aggregated data intervals from 17 individuals. In total, 106 digital physiological features were extracted. We found that physiological features, especially measures of heart rate variability, correlated most strongly to the executive function compared to other cognitive composites. The model predicted the actual executive function scores with correlation r = 0.69 and intra-individual changes in executive function scores with r = 0.61.ConclusionsOur findings demonstrated that wearable-based physiological measures, primarily HRV, have potential to be used for the continuous assessments of cognitive function in individuals with MCI.

A cascading failure model of cyber-coupled power system considering virus propagation

Cyber attacks from the information layer can have a serious impact on the operation of the cyber-coupled power system. In this paper, a practical cascading failure model used for investigating the impact of virus propagation on the cascading failure of cyber-coupled power system is proposed. The model takes into account the actual functionality of cyber-coupled system and the dynamic virus propagation in cyber network, and describes the effects of cyber node failure caused by virus infection and power flow overload on the cascading failure. Moreover, the impact of defense upgrade on the virus propagation is considered. Centralized control based on dispatching center and local regulation based on static power-frequency characteristic are introduced to describe the actual monitoring function of cyber system on the power grid. Then, the effects of defense upgrade strategy and modular community structure on the failure evolution are analyzed. The results indicate that the spread of virus destroys the monitoring function of cyber network on the power network and leads to more serious failure results. The effectiveness of defense upgrade strategies is related to the upgrade probability of information stations. In addition, the stronger community structure of cyber network can improve the robustness of cyber-coupled power system.

Comparison of Two Models of Distance Education for Lifelong Learning in China

Distance education has an important role in advancing lifelong learning. In China, there are two types of providers for distance education, open universities (OUs) and Institutes of Distance Education affiliated to regular universities (IDEs). IDEs are increasing because of technology development. This study compared OUs and IDEs in China from three aspects, including development goals, decision making on enrollment, and learning experience. Through corpus analysis and interviews, this study found that although the policy orientation of OUs and IDEs is different, their actual functions are the same. This is a phenomenon of institutional isomorphism, and government plays an important role in this convergence.

Diagnosed and subjectively perceived long-term effects of COVID-19 infection on olfactory function assessed by supervised machine learning.

Loss of olfactory function is a typical acute coronavirus disease 2019 (COVID-19) symptom, at least in early variants of SARS-CoV2. The time that has elapsed since the emergence of COVID-19 now allows for assessing the long-term prognosis of its olfactory impact. Participants (n = 722) of whom n = 464 reported having had COVID-19 dating back with a mode of 174 days were approached in a museum as a relatively unbiased environment. Olfactory function was diagnosed by assessing odor threshold and odor identification performance. Subjects also rated their actual olfactory function on an 11-point numerical scale [0,…10]. Neither the frequency of olfactory diagnostic categories nor olfactory test scores showed any COVID-19-related effects. Olfactory diagnostic categories (anosmia, hyposmia, or normosmia) were similarly distributed among former patients and controls (0.86%, 18.97%, and 80.17% for former patients and 1.17%, 17.51%, and 81.32% for controls). Former COVID-19 patients, however, showed differences in their subjective perception of their own olfactory function. The impact of this effect was substantial enough that supervised machine learning algorithms detected past COVID-19 infections in new subjects, based on reduced self-awareness of olfactory performance and parosmia, while the diagnosed olfactory function did not contribute any relevant information in this context. Based on diagnosed olfactory function, results suggest a positive prognosis for COVID-19-related olfactory loss in the long term. Traces of former infection are found in self-perceptions of olfaction, highlighting the importance of investigating the long-term effects of COVID-19 using reliable and validated diagnostic measures in olfactory testing.

Knowledge and Practice of Caregivers about Use of Oral Rehydration Therapy in Management of Diarrhea in Ishaka Adventist Hospital Bushenyi District

Diarrhoea induced dehydration remains a major cause of morbidity and mortality in children under 5 years in Ishaka Adventist Hospital’s catchment area. This study was therefore conducted to determine the Oral Rehydration Therapy (ORT) knowledge and practices of caregivers with under 5-year-old children. A descriptive cross-sectional study was undertaken to assess the knowledge and practices of caregivers of children under 5 years of age regarding ORT. Data was collected from 100 caregivers using stratified and convenient sampling, through an interviewer admitted structured questionnaire for knowledge on ORT and an observation checklist for use of ORT. Data was analyzed using Microsoft excel program and presented as descriptive statistics. Caregivers of children under 5 years of age, in general, were found to be equipped with average knowledge about oral rehydration Therapy although the majority did not know how to prepare ORT for use in the prevention/management of dehydration caused by diarrhoea. It can thus be concluded that the ORS knowledge and practices among caregivers were deficient. The implication of this finding is that more needs to be done to strengthen the quality of ORT promotional messages and education given to caregivers in terms of the actual function of ORT so that they can appreciate why it is critical to use it with the onset of diarrhoea. Keywords: Diarrhoea, Infant mortality, Oral Rehydration Therapy, Watery stool.

RNMPID: a database for riboNucleoside MonoPhosphates in DNA.

Ribonucleoside monophosphates (rNMPs) are the most abundant non-standard nucleotides embedded in genomic DNA. If the presence of rNMP in DNA cannot be controlled, it can lead to genome instability. The actual regulatory functions of rNMPs in DNA remain mainly unknown. Considering the association between rNMP embedment and various diseases and cancer, the phenomenon of rNMP embedment in DNA has become a prominent area of research in recent years. We introduce the rNMPID database, which is the first database revealing rNMP-embedment characteristics, strand bias, and preferred incorporation patterns in the genomic DNA of samples from bacterial to human cells of different genetic backgrounds. The rNMPID database uses datasets generated by different rNMP-mapping techniques. It provides the researchers with a solid foundation to explore the features of rNMP embedded in the genomic DNA of multiple sources, and their association with cellular functions, and, in future, disease. It also significantly benefits researchers in the fields of genetics and genomics who aim to integrate their studies with the rNMP-embedment data. rNMPID is freely accessible on the web at https://www.rnmpid.org.

Metaverse: a possible sustainability enabler in the transition from Industry 4.0 to 5.0.

Industry 4.0 has supported and changed the way companies operate over the last decade. Today, the increasing focus on sustainability in all its pillars is initiating a critical process and revision about the Fourth Industrial Revolution. A new scenario characterized by human-centricity, resilience and social sustainability, named Industry 5.0, is emerging.In this transition process, new and old enabling technologies are appearing, among them the Metaverse. Although its actual functions are not yet fully investigated, from different perspective this tool could adequately support the definition of Industry 5.0 by creating a symbiotic ecosystem between companies, workers and consumers. Against this backdrop, the aim of this paper is to investigate, through a systematic literature review, the role of the Metaverse in the approach to sustainability in order to understand in which way this technology can be useful for the transition. The results may support policy makers, managers and practitioners in understanding the role of Metaverse in sustainability and in the new wave of Industrial Revolution. Limits and future research steps are also discussed.

Optimization of coal chemical process parameters and energy saving and emission reduction based on genetic algorithm

Abstract Coal is an important energy resource. How to utilize it efficiently and cleanly is a hot topic nowadays. In the coal gasification process, the process parameter indexes have a significant impact, and the uncertainty of these factors will lead to a decrease in the quality of gas production. Therefore, in this paper, the uncertainty of process parameters is considered, and Aspen plus software with the Monte Carlo method is used to simulate the coal chemical process and measure the effect of uncertainty of process parameters on the yield of the coal gasification process. On this basis, in addition, coal flow rate, pressure, and steam/oxygen are taken as the process parameters and optimized, and three sets of multi-objective optimization models are established with gas calorific value, gasification efficiency, and gas yield, respectively, which are solved by improved multi-objective genetic algorithm based on crossover operator and variational operator to obtain Pareto curves, so as to adjust the parameter values according to the actual needs. The results show that the fluctuation of pressure has a big influence on the carbon conversion rate and gasification efficiency, and the carbon conversion rate and gasification efficiency can be made more stable by controlling the change of pressure. The improved genetic algorithm NSGA-II can reach the actual optimal objective function value in both high and low iteration times, providing the required parameters for the decision maker, and the optimal program results in TEC of 402,758 kW and CO2 content of 0.12%, which is effective in energy saving and emission reduction.

Predicting the pair correlation functions of silicate and borosilicate glasses using machine learning.

Glasses offer a broad range of tunable thermophysical properties that are linked to their compositions. However, it is challenging to establish a universal composition-property relationship of glasses due to their enormous compositions and chemical space. Here, we address this problem and develop a metamodel of the composition-atomistic structure relationship of a class of glassy materials via a machine learning (ML) approach. Within this ML framework, an unsupervised deep learning technique, viz., a convolutional neural network (CNN) autoencoder, and a regression algorithm, viz. random forest (RF), are integrated into a fully automated pipeline to predict the spatial distribution of atoms in a glass. The RF regression model predicts the pair correlation function of a glass in a latent space. Subsequently, the decoder of the CNN converts the latent space representation to the actual pair correlation function of the given glass. The atomistic structures of silicate (SiO2) and sodium borosilicate (NBS) based glasses with varying compositions and dopants are collected from molecular dynamics (MD) simulations to establish and validate this ML pipeline. The model is found to predict the atom pair correlation functions for many unknown glasses very accurately. This method is very generic and can accelerate the design, discovery, and fundamental understanding of the composition-atomistic structure relationship of glasses and other materials.

Program Pelatihan Operasional Kapal Speedboat Fiberglass Untuk Operator Kapal Tradisional Sampang-Gili Mandangin Dalam Rangka Mendukung Perkembangan Kapal Berbahan Fiberglass Di Pulau Madura

Mandangin Island is one of the islands in Sampang Regency which has an area of 1650 km 2 with a population of around 3000 people. Most of the people on this island, the majority of whom work as fishermen, generally travel around 12.2 km to Sampang City to buy daily necessities by boarding traditional wooden boats at the Tanglok Sampang port. According to information conveyed by the Head of the Sampang Regency Transportation Service, most or almost all of the passenger ships that dock at Tanglok Port are traditional wooden ships whose actual function is to transport goods, not passengers, which are quite old. The Transportation Department considers the existing ships to be less safe and comfortable and therefore require refreshing. Besides that, wood as the main material in nature is decreasing in quantity so the price is increasingly expensive. The method implemented in this community service is to carry out a fiber glass boat operational training program for traditional boat operators. With this activity, it is hoped that the continued use of fiber glass boats can begin to be used and reduce the use of wooden boats which are relatively more expensive.

Joint optimization of two-dimensional warranty period and maintenance strategy considering availability and cost constraints

Abstract Warranty can improve customer satisfaction and increase product sales, but it will bring additional economic burden to manufacturers, so making reasonable warranty decisions is particularly important. The current research focuses more on the interests of manufacturers, while there is less attention paid to indicators that consumers are concerned about, such as system availability. To solve this problem, a joint optimization model of the two-dimensional warranty period and preventive maintenance (PM) strategy considering availability and cost constraints was established. Based on the failure dependence analysis of multi-component systems, the actual failure rate function is constructed by using the failure dependence coefficient matrix. Based on the comprehensive consideration of imperfect PM and minimum maintenance, the two-dimensional warranty cost model and system availability model are established, respectively. Aiming at maximum availability and considering the warranty cost budget constraint, a joint optimization model of the two-dimensional warranty period and PM strategy was built, and a model-solving algorithm combining grid search and binary search was introduced. The effectiveness of the model is verified by the case analysis, and the necessity of the PM strategy and considering failure dependence are reflected in the model comparative analysis and parameter sensitivity analysis. Finally, reasonable suggestions are put forward for manufacturers’ warranty decisions through the result analysis.

Efficient slope reliability analysis using a surrogate-assisted normal search particle swarm optimization algorithm

Abstract Sampling of training data is the most important step in active learning slope reliability analysis, which controls the analysis accuracy. In this study, a novel surrogate-assisted normal search particle swarm optimization (SANSPSO) was proposed to enhance the accuracy and robustness of existing methodologies. In SANSPSO, the sampling process was considered a minimum problem with an objective function defined as the absolute value of the performance function. Initiated with a normal search paradigm and supplemented by three algorithm strategies, this approach seeks to preserve the continuity of the solution while refining the algorithm’s efficacy and efficiency. To reduce computation cost, surrogate-assistance was used, in which a surrogate model substitutes the objective function in most iterations. This surrogate model evolves during the iteration process and ultimately replaces the actual performance function within Monte Carlo simulation. Finally, this study presents a comparative study with five state-of-the-art methods across four explicit problems and three engineering cases, where test data suggest that the SANSPSO methodology yields a 20% improvement in accuracy and a 30% rise in stability under different dimensional problems relative to the most efficacious of the alternate methods assessed because of the improved and more consistent prediction of limit state function. These findings substantiate the validity and robustness of the SANSPSO approach.

Information System for the Educational Center

The goal of the research of the qualification thesis is the development of an intelligent information system of the educational centre using the Python programming language and the Django framework, the SQLite database system, and the chatbot with artificial intelligence ChatGPT. The developed system should facilitate the interaction of different types of users with learning/training centres in order to acquire new necessary skills. As you know, from now on every day more and more educational centres are opening, which provide their services for the study of this or that ability. Due to the onset of the pandemic period, all such educational centres are transferring their classes to the online mode, and the actual need to solve this type of issue arises more and more often. In this work, an analytical review of literary sources is carried out, a comparison of known means of implementation, i.e., so-called analogue systems, is carried out; the actual functioning of this type of system is checked. In addition, a goal tree is being built, which clearly outlines the general goal, namely, the optimization of all processes of the educational centre. In addition, an equally important step is the description of requirements, which is carried out in this work using the RUP methodology for the intelligent information system of the educational centre. The category of users is also determined and a generalized model of system functioning in the environment is built. To develop the system, an analysis of possible methods and software tools for creating and deploying the system is carried out. The most optimal methods for this case are chosen, namely the method of software synthesis and the method of using ready-made solutions, because their application will provide an opportunity to develop a system from scratch using the necessary components and provide an opportunity to integrate an already ready-made system with an intelligent component. Next, a recalculation of possible options among the integration of ready-made intelligent components is carried out, and a chat system with artificial intelligence is chosen, because it will give users the opportunity to search for answers to their questions without leaving the system and facilitate the learning process for each of the users.

Redistributive matronage: a moral economy of female traders and the ruling elite in Equatorial Guinea

ABSTRACT This paper explores the historicity of the marketplace in Malabo, Equatorial Guinea, focusing on testimonies from senior petty traders. It investigates the role of the marketplace as a site for redistributive matronage by analysing the historical accounts of market women. The article reveals key institutions, including the female section of the ruling party, market-sellers' associations, and the business-partner (socia) system, which collectively contribute to a resilient moral economy. This moral economy has persisted through various political-economic regimes since the country's independence in 1968. The paper connects the concept of the moral economy with debates on redistributive labour and patronage, providing insights into market women's daily activities as both generators and by-products of the country's political economy. Instead of presenting an alternative to political-economic analysis of Equatorial Guinea, this ethnographic investigation offers fundamental insights into the regime's actual functioning and its hegemonic reproduction.

PON1 has palmitoyl-protein thioesterase (PPT) activity, and can affect the presence of SR-B1 on the endothelial cell membrane.

The high-density lipoprotein (HDL)-associated enzyme paraoxonase 1 (PON1) is expressed almost exclusively in the liver and is then transported by HDL to the peripheral tissues. The lipophilic nature of PON1 enables its easy exchange between the lipoprotein and cell membranes in a process that is dependent on the HDL receptor scavenger receptor class B, type 1 (SR-B1). In endothelial cells, PON1 binding to the cell membrane leads to its internalization by endocytosis and subsequent lysosomal degradation. PON1 is a "promiscuous" enzyme with unusually broad substrate specificity in vitro, but its actual function and substrate are still unknown. The enzyme requires a lipid environment and becomes completely inactive upon delipidation. However, when PON1 binds HDL, its active site faces the lipoprotein's core and is inaccessible to external substrates. Hence, the HDL-bound PON1 is inactive toward substrates outside the particle's lipid core and is rapidly degraded and becomes inactive upon internalization. Consequently, the enzyme is only active in the cell membrane during its transit from HDL to the cytoplasm. To assign a function to PON1, we investigated whether it is a palmitoyl-protein thioesterase (PPT) that can hydrolyze the palmitoyl moieties of membrane proteins involved in HDL and cholesterol transport, such as SR-B1, ABCA1, or their neighboring caveola proteins to facilitate the release of HDL or trigger its endocytosis. This study shows that PON1 can hydrolyze palmitoyl-cysteine thioester bonds in vitro, has direct or indirect PPT activity in vivo, and can significantly decrease the presence of SR-B1 in the endothelial membrane.

Study of the peculiarities of social and spatial relations in the settlement system of Chernivtsi region

The article analyzes the compliance of the actual formation and functioning of territorial communities with the methodological recommendations. Using territorial communities as an example, the text highlights both the successes and shortcomings of meeting the fundamental social requirements of the local population, such as educational, healthcare, and administrative services. It identifies disparities in the arrangement of social and spatial connections within the Chernivtsi region's communities. Indeed, some communities do not meet the territorial and demographic requirements for formation, and certain social infrastructure institutions may not function as intended, which is not addressed in the languages of the TCs. Consequently, we must acknowledge that administrative-territorial reform will cause changes in the structure of local and regional settlement systems. The driving force behind these structural modifications is the socio-spatial connections. Keywords: administrative-territorial structure, decentralization, settlement systems, socio-spatial relations, territorial accessibility.

A generalization of the Taylor's theorem

In mathematics, Taylor's theorem is a formula that uses the information of a function to describe its proximity to a point. If the function is smooth enough, the conductive values can be used to construct a multiform to approximate the the function in the neighboring area of that point, which can even be extended to the convergence radius of the scale. Taylor’s theorem also gives the deviation between this multiform and the actual function value. The essay includes the proof and application of Taylor’s theorem and the Taylor’s theorem in multivariate functions. Special functions are set with unknown coefficients to deduce the Taylor’s theorem. Specific questions are solved by using the Taylor’s theorem as some examples. Taylor's theorem is frequently applied in real-world settings to solve problems. For instance, in engineering, it can be used to build a variety of numerical simulations and optimization algorithms. In physics, it can be used to analyze data from physical experiments. In finance, it can be used to manage risk and set prices for financial transactions.