Correlation Graph Research Articles

Long-term outcomes of complex medical rehabilitation of patients with rhinosinusitis with nasal polyposis and comorbidities

BACKGROUND: Comparative evaluation of courses of alternating magnetic therapy, low-intensity laser therapy, low-intensity laser radiation and hypoxic training in complex medical rehabilitation of atopic dermatitis and asthma patients with rhinosinusitis with nasal polyposis showed high efficacy of the additional use of physical factors in enhancing the regression of major clinical manifestations of the disease and improving the quality of life. AIM: The aim of the study was to evaluate the long-term (6, 12, and 18 months) outcomes of complex medical rehabilitation of patients with rhinosinusitis with nasal polyposis, including those with atopic dermatitis and asthma, to assess the preventive potential of instrumental physiotherapy, and to determine the optimal time parameters for retreatment with these therapeutic options. MATERIALS AND METHODS: Patients were divided into groups with isolated rhinosinusitis (n=90), atopic rhinosinusitis (n=93) or asthma (n=105) according to the severity of rhinosinusitis with nasal polyposis. Each group was in turn randomized into 3 subgroups: the control one, the comparator one, and the experimental one. All subjects received a standard treatment program (endonasal Nasonex, 2 doses twice a day; daily nasal rinsing twice a day). Patients in the comparator subgroups also received dupilumab (300 µg once every 2 weeks); the experimental subgroups received alternating magnetic therapy and low-intensity red laser radiation (633 nm). For atopic dermatitis, narrowband medium wave (311 nm) and broadband long wave (320–400 nm) radiation was added to this treatment program. For asthma, alternating magnetic therapy and low-intensity laser radiation were supplemented with hypoxic interval training. RESULTS: Positive changes in evaluated clinical parameters were reported in all groups during the entire follow-up period. However, the presence of comorbidities reduced the therapeutic potential of the standard treatment program, according to the results of the long-term observation. The addition of dupilumab significantly improved the condition of patients with isolated rhinosinusitis with nasal polyposis. However, a history of atopic dermatitis and especially asthma reduced the duration of disease remission. The addition of physical factors to the treatment program, pathogenetically justified in the treatment of atopic dermatitis (phototherapy) and asthma (hypoxic training), reliably prolonged the remission period. Complex physiotherapy leads to long-term reduction of pathological correlations, which is manifested by decrease in weight of correlation graphs. CONCLUSION: The main aim of the follow-up phase of the study was to evaluate the duration of the clinical result achieved, which determines the preventive potential of instrumental physiotherapy techniques, frequency and regularity of physiotherapeutic interventions. Retreatment is recommended after an average of 9–12 months for isolated rhinosinusitis with nasal polyposis and 15–18 months for atopic dermatitis and asthma.

Mechanical properties variation of samples fabricated by fused deposition additive manufacturing as a function of filler percentage and structure for different plastics

One of the key factors in manufacturing products by fused deposition molding (FDM) or layer-by-layer printing technology is the material intensity of the product. The task of reducing the amount of material required to manufacture a product without significant loss of mechanical properties is one of the most practically important technological tasks. Material saving in FDM printing of products allows to reduce financial costs and increase the speed of manufacturing of the final product without reducing (or not significantly reducing) the quality properties of the product. In our work it is demonstrated that using Combs filling type and materials of poly lactic acid (PLA) and polyethylene terephthalate glycol (PETG) it is possible to achieve material savings of up to 23% at 50% filling for PLA and 17% at 75% filling for PETG without significant reduction of product strength in comparison with other filling types. Exceptions are PLA samples with 100% fill and Lateral fill. Application of Kruskal-Wallis criterion and Dunn’s criterion with Bonferroni multiple comparison correction showed that there were no statistically significant differences within the strength limits of samples made by FDM printing technology from PLA and PETG plastics (p-value = 0.0514), as well as samples with Triangle and Grid filling type (p-value = 1). Based on this result, three groups of samples statistically significantly differing in ultimate strength were identified by methods of hierarchical cluster analysis; in each group (except for group 1, which included samples made of PLA plastic with Lateral filling type and 100% filling), correlation analysis was performed (Spearman correlation was used). The results of the correlation analysis showed a stable average correlation between the percentage of filling, modulus along the secant 0.05–0.2% strain, ultimate strength and strain corresponding to the yield stress. Analysis of the correlation graph showed that the main parameter correlating with all mechanical properties of the specimen is the 0.05–0.2% strain modulus. Based on this conclusion, robust regression equations predicting the 0.05–0.2% strain modulus as a function of the percentage of specimen filling were constructed for the two selected groups. Analysis of the equations showed that in the third group of specimens, the average modulus of 0.05–0.2% strain is more than twice the modulus of 0.05–0.2% strain in the second group. The detected statistical regularities can be explained by the mechanism of strain hardening, the actual value of which depends on the structure of the macrodefect (type of filling), properties and volume of the material (percentage of filling) used in the fabrication of samples using FDM printing technology.

MTS, WST-8, and ATP viability assays in 2D and 3D cultures: Comparison of methodologically different assays in primary human chondrocytes.

Tissue engineering enables the production of three-dimensional microtissues which mimic naturally occurring conditions in special tissues. These 3D culture systems are particularly suitable for application in regenerative medicine or experimental pharmacology and toxicology. Therefore, it is important to analyse the cells in their 3D microenvironment with regard to viability and differentiation. Tetrazolium assays (WST-8 and MTS) are still the methods of choice for estimating the number of living, metabolically active cells, with WST-8 being cell-impermeable compared to MTS. In contrast to these methods, the ATP assay is an endpoint method based on the luciferase-induced reaction of ATP with luciferin after cell lysis. We compared three methodologically different proliferation/toxicity assays (MTS, WST-8, ATP) in monolayer (2D) and 3D culture systems to improve the technically challenging determination of the number of viable cells. Chondrocytes were isolated from human articular cartilage. Three different test systems (MTS, WST-8, ATP) were applied to monolayer cells (2D, varying cell numbers) and spheroids (3D, different sizes) in 96-well plates. The intracellular ATP concentration was determined by luciferase-induced reaction of ATP with luciferin using a luminometer. Formazan formation was measured spectrophotometrically after different incubation periods. Evaluation was performed by phase contrast microscopy (toxicity), correlation of cell count and ATP concentration or absorption signal (Gompertz function) and propidium iodide (PI) staining to proof the cell lysis of all cells in spheroids. In 2D culture, all three assays showed a good correlation between the number of seeded cells and the ATP concentration or absorption data, whereas the MTS-assay showed the lowest specificity. In 3D culture, the spheroid sizes were directly related to the number of cells seeded. The absorption data of the WST-8 and MTS assay correlated only for certain spheroid size ranges, whereas the MTS-assay showed again the lowest specificity. Only the measured intracellular ATP content showed a linear correlation with all spheroid sizes ranging from 100-1000 μm. The WST-8 assay revealed the second-best sensitivity which allows the measurement of spheroids larger than 240 μm. Phase contrast observation of monolayer cells showed toxic effects of MTS after 6 h incubation and no signs of toxicity of WST-8. Staining with propidium iodide showed complete lysis of all cells in a spheroid in the ATP assay. Among tetrazolium-based assays, WST-8 is preferable to MTS because of its non-toxicity and better sensitivity. When determining the number of viable cells in the 2D system, caution is advised when using the ATP assay because of its two-phase slope of the correlation graph concerning cell number and intracellular ATP. In 3D systems of human chondrocytes, the ATP-assay is superior to the other two test systems, as the correlation graph between cell number and intracellular ATP is biphasic. Since differentiation processes or other metabolic events can influence the results of proliferation and toxicity assays (determination of viable cells), this should be taken into account when using these test systems.

The research on the factors affecting the World Happiness index

Abstract. The purpose of this study was to use data from the World Happiness Report to conduct an in-depth analysis of the factors that influence the World Happiness Index (WHI) and to look for other factors that may influence happiness. In this paper, the correlation graph shows that among the original six variables, Social Support has the strongest correlation with the ladder score, which means that Social Support has the greatest impact on the happiness index, followed by GDP per capita; Generosity, on the other hand, was the weakest associated with ladder scores and had the least effect on happiness. Then the linear regression and scatter plot are used to prove this conclusion. Therefore, this paper can consider whether to delete Generosity as an influential factor. A map was then used to show happiness levels and geographical distribution in different countries. At the same time, the distribution of the Gini coefficient is also shown by a regional distribution map. From the perspective of the Gini coefficient and education level, these two factors also have a certain positive correlation with the happiness index, which are likely to be the potential influencing factors of the happiness index. From a global map perspective, the happiest countries are mostly located in Europe, North America and Oceania, while the happiest countries are mostly located in Africa. The study's sample size was not large enough, it was prone to make errors and raise questions about happiness scores in some countries.

Anemia in patients with chronic kidney disease and its association with cardiac abnormalities

Abstract Introduction Chronic kidney disease (CKD) is a pathology characterized by multisystemic comorbidities, predominantly associated with cardiovascular disease and anemia. However, the precise association between the degree of anemia and the development of cardiac abnormalities in patients with CKD undergoing hemodialysis remains unclear. Purpose To determine the association between the degree of anemia and cardiac abnormalities in patients with CKD receiving hemodialysis treatment. Methods A cross-sectional multicenter study that included individuals with CKD undergoing hemodialysis was conducted. Electronic medical records containing information regarding medical conditions and pertinent laboratory parameters was collected, and cardiac function was determined using echocardiography. A Spearman rank correlation analysis was conducted to evaluate the associated between the degree of anemia and echocardiographic parameters, with significant set at p <0.05. Results Ninety-nine individuals were included, with a median age of 48 years. The population was categorized based on hemoglobin (Hb) levels into non/mild anemia (Hb >10 mg/dL) and moderate/severe anemia (Hb <9.9 mg/dL) groups (table). Significant differences were observed in left ventricle (LV) posterior wall thickness (10mm in non/mild vs. 11mm in moderate/severe, p<0.015), left atrial (LA) volume (34 mL/m2 in non/mild vs. 39 mL/m2 in moderate/severe, p<0.007) and left ventricular ejection fraction (LVEF) (59% in non/mild vs. 55% in moderate/severe, p<0.025). Negative correlations (figure) were observed between Hb levels and LV mass index (r= -0.21, p= 0.03) and pulmonary artery systolic pressure (PASP)(r= -0.27, p= 0.006). Conclusion The degree of anemia demonstrated an association with cardiac abnormalities, as evidenced by alterations in echocardiographic parameters such as LVEF, LA volume and PSAP. This study underscores the importance of further prospective investigations to enhance early identification and stratification of anemia in patients with CKD aiming to mitigate further cardiac complications.Clinical demographics tableScatter plots with correlation graphs

Association of visceral adipose tissue with echocardiographic abnormalities in type 2 diabetes patients

Abstract Introduction Ectopic deposition of adipose tissue, particularly visceral adipose tissue (VAT) has been identified as a significant contributor to cardiometabolic complications among individuals with type 2 diabetes (T2D). An increase in VAT has been suggested to cause cardiac dysfunction, which may lead to echocardiographic abnormalities such as alterations in speckle-tracking and left ventricular ejection fraction (LVEF). However, this relationship has not been fully elucidated. Purpose This study aims to determine the association between VAT and echocardiographic abnormalities in patients with T2D. Methods A cross-sectional study in a tertiary hospital which included 195 individuals with T2D, the diagnosis of autoimmune diabetes was excluded. VAT was measured using electrical bioimpedance (BIA), and cardiac function was determined using echocardiography. A Spearman rank correlation analysis was conducted to evaluate the association between VAT and echocardiographic parameters, with significance set at p < 0.05. Results Within our cohort, the median age was 57 years, with 37% men. Based on the third quartile (P75) cutoff (5 liters for men, 3.75 liters for women), the population was categorized into P1-75 and P75-100 (Table). There were significant differences in LVEF (61% in P1-75 vs. 58% in P75-100, p = 0.041), left ventricular global longitudinal strain (LV GLS) (P1-75: -20.3%, P75-100: -19.2%, p = 0.012), and conduit phase of left atrial strain (LAScd) (P1-75: 25%, P75-100: 20%, p = 0.046). Positive correlations (Figure) were observed between VAT and indexed LV mass (r = 0.171, p = 0.020), basal diameter of the right ventricle (r = 0.308, p = 0.001), and LV GLS (r = 0.197, p = 0.006). Additionally, negative correlations were observed between VAT and LVEF (r = -0.342, p = <0.001) and right ventricular fractional area change (r = - 0.227, p= 0.001). Conclusion Increased VAT was associated with cardiac dysfunction, as indicated by altered LV GLS and LAScd. This study underscores the relevance of VAT in influencing both functional and structural changes in the left and right ventricles. It also highlights the significance of monitoring VAT for comprehensive cardiovascular risk assessment in T2D patients.Clinical demographics tableScatter plots with correlation graphs

PHYTOCHEMICAL PROFILING BY LC-ESI-MS/MS AND DETERMINATION OF ANTIOXIDANT, ANTI-ALZHEIMER AND ANTITYROSINASE ACTIVITIES OF TWO ASPARAGACEAE SPECIES: Scilla hyacinthoides L. AND Scilla ingridiae SPETA

In this study, in vitro antioxidant, antialzheimer and antityrosinase activity, total phenolic and flavonoid components, chemical profiling, identified by LC-ESI-MS/MS analysis and also chemometric analysis, of two different parts of Scilla species (Scilla hyacinthoides L. and Scilla ingridiae Speta) were determined in detailed perspective. The highest contents of total phenolics and total flavonoids were determined in S1TU (S. hyacinthoides aerial parts) extract (11.72 µg GAE/mg) and S2TU (S. ingridiae aerial parts) extract (31.53 µg QE/mg). The highest ABTS and CUPRAC activities were found in S1TU and S2TU extracts. The inhibitory activities of the extracts on the enzyme acetylcholinesterase were investigated. S1TU and S2TU extracts again showed the highest activity. Although the antirosinase enzyme inhibitory activities of the extracts were generally similar and high, the S2TA extract (S.ingridiae corm parts) showed the highest activity. Liquid chromatography-electrospray ionization-mass spectrometry (LC-ESI-MS/MS) was used to determine the content of phenolic components of the extracts. Fourteen different bioactive components were determined in the analyzes and their amounts were measured. The data obtained were analyzed chemometrically such as pricipal component analysis (PCA),hierarchical comoponent analysis (HCA) and their relationships to each other were supported visually and numerically with Pearson correlation graphs, heat maps, etc. The research results have shown that the various components of the plant have a good effect on various biological activities, so that it can be used for various purposes in the future (especially with a good degree of antityrosinase activity), taking into account the results. The biological activities demonstrated here can rationalize the use of the plant in traditional medicine.

Sensor self-diagnosis method for a nickel top-blowing furnace based on graph interactive dynamic fusion

Abstract As modern industry gradually advances towards greater automation and intelligence, the scale of nickel top-blowing furnace smelting systems is continuously expanding, leading to an increasing need for sensor maintenance. Traditional periodic evaluations and manual maintenance methods are no longer sufficient to meet the development needs of intelligent sensors. To address this issue, this paper proposes a sensor self-diagnosis method based on graph interactive dynamic fusion, called DLGCN-GIDF. First, a combination of knowledge-driven and data-driven approaches is introduced. By constructing a dual-layer architecture based on a functional module graph network and a sensor graph network, a sensor correlation graph model for the nickel top-blowing furnace system is established. Next, with the aid of a GIDF module, the relative weights between functional modules and sensors are integrated to perform spatiotemporal correlation-based graph fusion. This enables the prediction of spatiotemporal data for sensors from a system perspective. Finally, the goal of sensor self-diagnosis is achieved using a standardised residual testing algorithm. Taking a nickel top-blowing furnace smelting system as an example, the feasibility and effectiveness of our method of sensor fault self-diagnosis are verified.

An Adaptive Spatiotemporal Decouple Graph Convolutional Network Based Quality‐Related Fault Detection Method for Complex Industrial Processes

ABSTRACTWith the rapid development of industrial technology, industrial processes become increasingly complex, presenting characteristics of large‐scale and multi‐unit collaboration. However, most current fault detection methods focus on nonlinearity, dynamics, and other characteristics, while neglecting spatiotemporal information. To address this issue, an adaptive spatiotemporal decouple graph convolutional network based quality‐related fault detection method is proposed in this article. First, the temporal graph convolutional network and spatial graph convolutional network are combined organically in the form of joint training. Second, considering that fixed graph structures cannot reflect the dynamic relationships among nodes, we proposed an adaptive weighted mask mechanism to construct dynamic correlation graph embedded with priori knowledge. Multi‐attention mechanism is used to integrate spatiotemporal information, besides, we designed a decoupling layer to avoid information redundancy. Finally, the proposed spatiotemporal graph convolutional network is used to establish a regression model, the quality‐related latent variables are extracted by the decoupling layer, and the statistic is constructed based on Kullback–Leibler divergence. The effectiveness and feasibility of the proposed method are illustrated with the hot strip mill process and the Tennessee Eastman process.

Eco-benevolent synthesis of ZnO-NPs and ZnO-MFs from Inula oculus-christi L. (Asteraceae) with effective antioxidant, antimicrobial, DNA cleavage, and decolorization efficiencies.

As a result of the changes occurring globally in recent years, millions of people are facing challenging and even life-threatening diseases such as cancer and the COVID-19 pandemic, among others. This phenomenon has spurred researchers towards developing and implementing innovative and environmentally friendly scientific methods, merging disciplines with significant technological potential, such as nanotechnology with medicinal plants. Therefore, the focus of this research is to synthesize zinc nanoparticles (ZnO-NPs) and microflowers (ZnO-MFs) using extracts of the medicinal plant I. oculus christi prepared in n-hexane and methanol as new bioreduction and capping agents through a simple and environmentally friendly chemical approach. Optical, thermal, and morphological structural analyses of ZnO-NPs and ZnO-MFs were conducted using Ultraviolet-Visible (UV-Vis) spectroscopy, Fourier Transform Infrared (FT-IR) spectroscopy, Thermogravimetric Analysis (TGA), and Field Emission Scanning Electron Microscopy (FE-SEM). Metabolic profiles of extracts from different plant parts were analyzed using Gas Chromatography-Mass Spectrometry (GC-MS) and supported by visualization of contents through Principal Component Analysis (PCA), hierarchical cluster analysis heatmaps, and Pearson correlation graphs. Interestingly, ZnO-NPs and ZnO-MFs exhibited strong antioxidant properties and demonstrated particularly potent antimicrobial activity against Micrococcus luteus NRRL B-4375, Escherichia coli ATCC 25922, and Candida albicans ATCC 10231 strains compared to standard antibiotics. Furthermore, ZnO-NPs and ZnO-MFs showed excellent plasmid DNA-cleavage activity of pBR322 with increasing doses. The photocatalytic performance of the synthesized ZnO-NPs and ZnO-MFs was evaluated for methylene blue (MB), congo red (CR), and safranin-O(SO) dyes, demonstrating remarkable color removal efficiency. Overall, the results provide a promising avenue for the green synthesis of ZnO-NPs and ZnO-MFs using I. oculus-christi L. inflorescence and pappus extracts, potentially revolutionizing biopharmaceutical and catalytic applications in these fields.

Incorporating human and organizational failures into the formation pattern for different Arctic maritime accidents using a data-driven Bayesian network

To gain a comprehensive understanding of formation patterns for various maritime accidents in Arctic waters, the present study integrates human and organizational failures (HOFs) with accident risk factors (AIFs) to examine the individual or collective impacts of different AIFs and HOFs on Arctic maritime accidents. Firstly, a novel database is established within the improved Human Factors Analysis and Classification System (HFACS), encompassing 14 AIFs with multiple states and 33 HOFs with binary states. Secondly, the Association rule mining (ARM) is employed to capture the causal relationships among HOFs, while Augmented Naive Bayes (ANB) is introduced to account for the interdependency of AIFs, thereby developing the data-driven BN model for analyzing and predicting Arctic maritime accidents. Finally, the criticality and sensitivity of AIFs and HOFs across different Arctic maritime accidents are identified and ranked by BN forward and backward inference. Furthermore, three scenarios are simulated to demonstrate effective management strategies for prevention and mitigation of Arctic maritime accidents. Additionally, a correlation graph depicting the relationship between HOFs and AIFs is generated for each accident scenario, facilitating crew onboard ships as well as shipping companies and maritime authorities in customizing targeted safety measures aimed at preventing Arctic maritime accidents.

Comparison of Mango (Mangifera indica) Dehydration Technologies: A Systematic Review

The convective hot-air drying technology can cause physicochemical, nutritional, and organoleptic losses in the mango (Mangifera indica). The present Systematic Review was carried out with the objective of comparing mango dehydration technologies to identify the effects on the physicochemical, nutritional, and organoleptic properties of the fruit. Through a review of published scientific and conference papers in the Scopus database, adjusted to the Preferred Reporting Items for Systematic reviews and Meta-Analyses (PRISMA) methodology, a total of 134 documents dated between 2000 and December 6 of 2022 were obtained; 76 of these documents were finally included in the bibliographic and theoretical analysis. Selection parameters emphasizing the relationship between the articles and the research topic, evidenced by including at least one of three dehydration technologies and the fruit of interest with an experimental or theoretical approach to the dehydration subject; review articles and surveys were excluded. Correlation graphs of bibliographic variables were made using the data mining software VantagePoint (version 15.1) , which was graphically restructured in Microsoft Excel with the support of statistical analysis. Of the resulting articles, it was found that the countries with authors who participated most in scientific production like India, Brazil, Colombia, the United States, and Thailand, were those related to mango production or importation. Furthermore, the freeze-drying technology allows operating at lower temperatures than convective hot-air drying, contributing to the preservation of ascorbic acid, among other compounds. The refractance window has the shortest operation time to obtain moisture values between 10 and 20%. The dehydrated samples using the refractance window are smooth, homogeneous, non-porous, and comparable to the color obtained with freeze-drying, which is acceptable for industrial applications.

Navigating climate complexity and its control via hyperchaotic dynamics in a 4D Caputo fractional model

This interdisciplinary study critically analyzes current research, establishing a profound connection between sea water, sea ice, sea temperature, and surface temperature through a 4D hyperchaotic Caputo fractional differential equation. Emphasizing the collective impact on climate, focusing on challenges from anthropogenic global warming, the study scrutinizes theoretical aspects, including existence and uniqueness. Two sliding mode controllers manage chaos in this 4D fractional system, assessed amid uncertainties and disruptions. The global stability of these controlled systems is also confirmed, considering both commensurate and non-commensurate 4D fractional order. To demonstrate the intricate chaotic motion within the system, we employ the Lyapunov exponent and Poincare sections. Numerical simulations are conducted by using the predictor-corrector method. The effects of surface temperature on chaotic dynamics are discussed. The crucial role of sea ice reflection in climate stability is highlighted in two scenarios. Correlation graphs, comparing model and observational data using the predictor-corrector method, enhance the proposed 4D hyperchaotic model’s credibility. Subsequently, numerical simulations validate theoretical assertions about the controllers’ influence. These controllers indicate which variable significantly contributes to controlling the chaos.

Learning from models: high-dimensional analyses on the performance of machine learning interatomic potentials

Machine learning interatomic potential (MLIP) has been widely adopted for atomistic simulations. While errors and discrepancies for MLIPs have been reported, a comprehensive examination of the MLIPs’ performance over a broad spectrum of material properties has been lacking. This study introduces an analysis process comprising model sampling, benchmarking, error evaluations, and multi-dimensional statistical analyses on an ensemble of MLIPs for prediction errors over a diverse range of properties. By carrying out this analysis on 2300 MLIP models based on six different MLIP types, several properties that pose challenges for the MLIPs to achieve small errors are identified. The Pareto front analyses on two or more properties reveal the trade-offs in different properties of MLIPs, underscoring the difficulties of achieving low errors for a large number of properties simultaneously. Furthermore, we propose correlation graph analyses to characterize the error performances of MLIPs and to select the representative properties for predicting other property errors. This analysis process on a large dataset of MLIP models sheds light on the underlying complexities of MLIP performance, offering crucial guidance for the future development of MLIPs with improved predictive accuracy across an array of material properties.

Well logging restoration based on spatial-temporal information mining from muti-scale correlation graph representation

Geological reservoir characterization using well-loggingg data often faces challenges, such as data distortion or even missing values. To tackle this, traditional signal restoration theories and machine learning techniques, such as neural networks, have been commonly employed. However, they struggle to fully explore correlation information among logging curves within the same well and lack adaptability across different wells. In this paper, a novel Logging Curve Restoration method based on Spatial-Temporal Information Mining (LCR-STIM) using multi-scale correlation graph representation is proposed. Specially, a novel multi-scale graph representation technique is employed to build spatial (i.e., horizontal) nonlinear relationships among logging curves. Subsequently, deep forest model is introduced to generate multi-scale predictions to uncover the rich spatial information hidden in the multi-scale graph representations. Finally, to fully utilize temporal information in the obtained different multi-scale predictions, Long Short-Term Memory (LSTM) network is employed to combine the multi-scale restoration results to predict target logging data accurately. Taking the well logging data of 41 wells from Daqing oil field as training data and the target logging curves from 3 other wells as the test data, experimental results shown that the restoration performance can be improved by at least 20% compared with the state-of-the-art comparison methods.

Control of multi-modal scattering in a microwave frequency comb

Control over the coupling between multiple modes of a frequency comb is an important step toward measurement-based quantum computation with a continuous-variable system. We demonstrate the creation of square-ladder correlation graphs in a microwave comb with 95 modes. The graphs are engineered through precise control of the relative phase of three pumps applied to a Josephson parametric oscillator. Experimental measurement of the mode scattering matrix is in good agreement with theoretical predictions based on a linearized equation of motion of the parametric oscillator. The digital methods used to create and measure the correlations are easily scaled to more modes and more pumps, with the potential to tailor a specific correlation graph topology.

Multi-criteria optimization calculations to improve the efficiency of an agricultural moving power units in fleet

BACKGROUND: Due to the fact that the development of one target function for separately assessing each agricultural machine used in the fleet seems insufficient to fully assess the efficiency of agricultural moving power units in fleet, a multi-criteria formulation of the problem was applied using the previously developed hardware and software complex MOVI 1.4 and the development of computer programs to carry out their optimization calculations of the efficiency of the agricultural moving power units in fleet the example of a specific farm. AIMS: The goal of the work is to develop software tools for carrying out multi-criteria optimization calculations in order to increase the efficiency of agricultural moving power units in fleet in the context of digitalization of agriculture. METHODS: The analysis is based on the collection of scientific publications, scientific articles and other sources of information on the development of research and experimental design work on the creation of intelligent transport and technical means and the improvement of methodological and software for multi-criteria optimization calculations of the efficiency of the agricultural moving power units in fleet. Methods of scientific generalization and statistical processing of available information and analytical materials from domestic and foreign sources were also used. RESULTS: To calculate the values of quality criteria for the MES fleet, an interface subprogram for MOVI 1.4 was developed in the Pascal programming language, adequate for implementing calculations based on mathematical models of criteria at trial points. After introducing functional and criterion restrictions, an admissible set and a set of Pareto-optimal solutions were constructed, and correlation graphs between quality criteria were constructed. The scientific novelty lies in the development of computer programs for multi-criteria justification of the efficiency of the agricultural moving power units in fleet. CONCLUSION: The practical value lies in the fact that the developed mathematical and software tools can be successfully used for multi-criteria optimization of the efficiency of an agricultural moving power units in fleet using the example of specific agricultural organizations, regions or the country as a whole.

Multi-view graph-based interview representation to improve depression level estimation

Depression is a serious mental illness that affects millions worldwide and consequently has attracted considerable research interest in recent years. Within the field of automated depression estimation, most researchers focus on neural network architectures while ignoring other research directions. Within this paper, we explore an alternate approach and study the impact of input representations on the learning ability of the models. In particular, we work with graph-based representations to highlight different aspects of input transcripts, both at the interview and corpus levels. We use sentence similarity graphs and keyword correlation graphs to exemplify the advantages of graphical representations over sequential models for binary classification problems within depression estimation. Additionally, we design multi-view architectures that split interview transcripts into question and answer views in order to take into account dialogue structure. Our experiments show the benefits of multi-view based graphical input encodings over sequential models and provide new state-of-the-art results for binary classification on the gold standard DAIC-WOZ dataset. Further analysis establishes our method as a means for generating meaningful insights and visual summaries of interview transcripts that can be used by medical professionals.

Stock Trend Prediction with Machine Learning: Incorporating Inter-Stock Correlation Information through Laplacian Matrix

Predicting stock trends in financial markets is of significant importance to investors and portfolio managers. In addition to a stock’s historical price information, the correlation between that stock and others can also provide valuable information for forecasting future returns. Existing methods often fall short of straightforward and effective capture of the intricate interdependencies between stocks. In this research, we introduce the concept of a Laplacian correlation graph (LOG), designed to explicitly model the correlations in stock price changes as the edges of a graph. After constructing the LOG, we will build a machine learning model, such as a graph attention network (GAT), and incorporate the LOG into the loss term. This innovative loss term is designed to empower the neural network to learn and leverage price correlations among different stocks in a straightforward but effective manner. The advantage of a Laplacian matrix is that matrix operation form is more suitable for current machine learning frameworks, thus achieving high computational efficiency and simpler model representation. Experimental results demonstrate improvements across multiple evaluation metrics using our LOG. Incorporating our LOG into five base machine learning models consistently enhances their predictive performance. Furthermore, backtesting results reveal superior returns and information ratios, underscoring the practical implications of our approach for real-world investment decisions. Our study addresses the limitations of existing methods that miss the correlation between stocks or fail to model correlation in a simple and effective way, and the proposed LOG emerges as a promising tool for stock returns prediction, offering enhanced predictive accuracy and improved investment outcomes.

BJLD-CMI: a predictive circRNA-miRNA interactions model combining multi-angle feature information.

Increasing research findings suggest that circular RNA (circRNA) exerts a crucial function in the pathogenesis of complex human diseases by binding to miRNA. Identifying their potential interactions is of paramount importance for the diagnosis and treatment of diseases. However, long cycles, small scales, and time-consuming processes characterize previous biological wet experiments. Consequently, the use of an efficient computational model to forecast the interactions between circRNA and miRNA is gradually becoming mainstream. In this study, we present a new prediction model named BJLD-CMI. The model extracts circRNA sequence features and miRNA sequence features by applying Jaccard and Bert's method and organically integrates them to obtain CMI attribute features, and then uses the graph embedding method Line to extract CMI behavioral features based on the known circRNA-miRNA correlation graph information. And then we predict the potential circRNA-miRNA interactions by fusing the multi-angle feature information such as attribute and behavior through Autoencoder in Autoencoder Networks. BJLD-CMI attained 94.95% and 90.69% of the area under the ROC curve on the CMI-9589 and CMI-9905 datasets. When compared with existing models, the results indicate that BJLD-CMI exhibits the best overall competence. During the case study experiment, we conducted a PubMed literature search to confirm that out of the top 10 predicted CMIs, seven pairs did indeed exist. These results suggest that BJLD-CMI is an effective method for predicting interactions between circRNAs and miRNAs. It provides a valuable candidate for biological wet experiments and can reduce the burden of researchers.