Set Of Values Research Articles

ARE INTELLECTUAL CAPITAL AND ITS COMPONENTS AFFECTIVE ON FINANCIAL PERFORMANCE?

The factors that affect intellectual capital creation at firms and how these factors influence the financial performance of firms have been core questions of extant work in the channels of finance for many years. In this period, therefore, the efforts that firms make to increase their intellectual capital to create value and studies that examine the impact of the increased intellectual capital on the firm’s financial performance are notable. Thus, this study aims to explore the influence of intellectual capital and its components on the financial performance of the BIST Sustainability Index companies for the period between 2018:Q4-2024:Q1. A set of Intellectual Value Added Coefficient (VAIC) and its components Human Capital Efficiency (HCE), Structural Capital Efficiency (SCE), and Capital Employed Efficiency (CEE) are employed as indicators of intellectual capital. The financial performance is measured through Return on Assets (ROA) and Return on Capital Employed (ROCE). Control variables included in each model are Market to Book Value (MV/BV), Logarithm of Total Assets (SIZE), and Asset Turnover (ATR). The fixed effects regression results show that an increase in the intellectual capital of the companies listed in the BIST Sustainability Index has a positive effect on their financial performance.

Mapping wild nature areas to identify priority areas for urban rewilding in cities: A process-oriented approach

Urban rewilding initiatives are gaining traction in cities, yet little remains known about the spatial opportunities for transitioning greenspaces into rewilding sites. This paper advances a process-oriented framing of urban wild nature areas (WNAs) and proposes a novel methodological approach for mapping existing WNAs at the city-scale, thereby shedding light on the potentials for urban rewilding across greenspace types and urban environments. Using Vienna as a case study, we identify WNAs as greenspaces predominantly shaped by natural succession processes and devoid of vegetation management. NDVI timeseries derived from Sentinel-2-satellite data for the period 2017–2022 are processed using a Random Forest algorithm to distinguish between unmanaged and managed vegetation annually and integrated into a multi-year composite map of ‘urban vegetation management intensity’. Based on this map and a set of objective cut-off values, 1298 WNAs are identified, representing 29.5 % of the city’s total urban greenspace and 14.9 % of the city’s total area. WNAs are distributed across diverse urban settings and greenspace types, with the majority being found in formal greenspaces such as forests, meadows and parks, and low-density built-up areas. The key strength of the process-oriented approach lies in its ability to detect WNAs dominated by natural succession, regardless of historic origin, greenspace type, and governance structure, thereby providing a baseline against which the full potential for urban rewilding can be evaluated. We demonstrate the methods’ utility in identifying potentials for urban rewilding, emphasising the importance of directing rewilding efforts towards residential greenspaces, urban parks, and street greenery, especially in densely built inner-city districts.

From Simulations to Inference: Using Machine Learning to Tune Patient-Specific Finite-Element Models of the Middle Ear Towards Objective Diagnosis.

Computational models, particularly finite-element (FE) models, are essential for interpreting experimental data and predicting system behavior, especially when direct measurements are limited. A major challenge in tuning these models is the large number of parameters involved. Traditional methods, such as one-by-one sensitivity analyses, are time-consuming, subjective, and often return only a single set of parameter values, focusing on reproducing averaged data rather than capturing the full variability of experimental measurements. In this study, we applied simulation-based inference (SBI) using neural posterior estimation (NPE) to tune an FE model of the human middle ear. The training dataset consisted of 10,000 FE simulations of stapes velocity, ear-canal (EC) input impedance, and absorbance, paired with seven FE parameter values randomly sampled within plausible ranges. The neural network learned the association between parameters and simulation outcomes, returning the probability distribution of parameter values that can reproduce experimental data. Our approach successfully identified parameter sets that reproduced three experimental datasets simultaneously. By accounting for experimental noise and variability during training, the method provided a probability distribution of parameters, representing all valid combinations that could fit the data, rather than tuning to averaged values. The network demonstrated robustness to noise and exhibited an efficient learning curve due to the large training dataset. SBI offers an objective alternative to laborious sensitivity analyses, providing probability distributions for each parameter and uncovering interactions between them. This method can be applied to any biological FE model, and we demonstrated its effectiveness using a middle-ear model. Importantly, it holds promise for objective differential diagnosis of conductive hearing loss by providing insight into the mechanical properties of the middle ear.

Cardiac Troponin Levels in Patients with Chronic Kidney Disease: "Markers of High Risk or Just Noise''?

Kidney disease is linked to the development of cardiovascular disorders, further increasing morbidity and mortality in this high-risk population. Thus, early detection of myocardial damage is imperative in order to prevent devastating cardiovascular complications within this patient group. Over the years, cardiac biomarkers have been identified and are now widely used in everyday clinical practice. More specifically, available data suggest that cardiac troponin and its regulatory subunits (TnT, TnI, and TnC) reflect the injury and necrosis of myocardial tissue. While cTnC is identical in cardiac and skeletal muscle, TnT and TnI constitute cardiac-specific forms of troponin, and, as such, they have been established by international societies as biomarkers of cardiac damage and diagnostic indicators for acute myocardial infarction. Elevations in the levels of both cardiac troponins (cTnT and cTnI) have been also reported in asymptomatic patients suffering from chronic kidney disease. Therefore, if abnormal, they often generate confusion among clinicians regarding the interpretation and clinical significance of their numerical values in emergency settings. The aim of this review is to explore the reasons behind elevated troponin levels in patients with chronic kidney disease and identify when these elevated levels of biomarkers indicate the need for urgent intervention, considering the high cardiovascular risk in this patient group.

Comparing the measurement properties of the EQ-5D-5L, SF-6Dv2, QLU-C10D and FACT-8D among survivors of classical Hodgkin's lymphoma.

This study aimed to evaluate the measurement properties of EQ-5D-5L, SF-6Dv2, QLU-C10D, and FACT-8D in survivors of Classical Hodgkin's Lymphoma (CHL). A cross-sectional, web-based survey was conducted from May to August 2022 to collect data. Chinese value sets were used to estimate the utility scores for EQ-5D-5L, SF-6Dv2, and QLU-C10D, while the Australian value set was used for FACT-8D. The measurement properties assessed included ceiling and floor effects, convergent validity (assessing associations between similar dimensions/utility scores using Spearman's rank correlation and intraclass correlation coefficient), and known-group validity (measures could differentiate health-related quality of life (HRQoL) between risk groups). A total of 534 CHL survivors participated in the survey and completed the questionnaire. All dimensions of EQ-5D-5L, SF-6D (except for vitality), QLU-C10D, and FACT-8D showed ceiling effects, ranging from 18 to 91.6%. The EQ-5D-5L demonstrated the higher ceiling effects compared to other measures, with 33% of patients reporting full health on this scale. All 30 pairs of associations between similar dimensions from the four measures were statistically significant, with correlation coefficients ranging from 0.29 to 0.77. Regarding utility scores, the EQ-5D-5L utility score showed a stronger correlation with SF-6Dv2 than with the other two measures. Statistically significant correlations of utility scores between the four measures were observed. EQ-5D-5L can significantly differentiate HRQoL among all known-groups, while SF-6Dv2, QLU-C10D, and FACT-8D showed a less strong discriminant ability. EQ-5D-5L outperformed SF-6Dv2 in terms of agreement with cancer-specific PRMs and discriminant ability. However, SF-6Dv2 showed stronger associations with similar dimensions of QLU-C10D and FACT-8D, indicating high convergent validity. The generic PBMs are sensitive enough to measure HRQoL in survivors of CHL.

A method for evaluating the regulation capacity of thermostatically controlled load group based on state complementary control strategy

Electric heating load (EHL) is a type of thermostatically controlled load whose power consumption mode can be flexibly adjusted, as long as the customers’ thermal comfort is ensured. The key to unlocking the regulatory potential of EHL lies in characterizing and harnessing its post-aggregation regulatory capacity, given its inherent dispersion and heterogeneity. Firstly, the second-order equivalent thermal parameter (ETP) model, grounded in the thermal equilibrium of the room, is employed to analyze the operational characteristics and to construct a model for aggregating load group. Secondly, leveraging the analysis of individual EHL regulation capacity, the temperature state characteristic quantity is introduced as a sorting index. Considering the dynamic properties of baseline power over time, a control strategy is proposed, based on state complementary characteristics among EHLs, for a more comprehensive and dynamic system management. Finally, the key indexes for reporting and implementation phases are identified to evaluate the regulation capacity of load group. The case study evidences that the newly introduced control strategy and evaluation method can provide more stable regulation power support for power grid dispatching, and more deeply harness the regulation capacity to maximize its potential value. In addition, this paper also discusses the influence of related factors such as the number, the rated power, the inside temperature control interval, the inside temperature setting value and the outside temperature on the regulation ability of the EHL group, offering a robust framework for the dispatching center to devise well-informed and rational regulation instructions.

Parameter identification of rock mass in the time domain

A time domain rock mass parameter identification method considering the Rayleigh damping is proposed in this paper. Starting with a set of initial parameter values, the algorithm solves the finite-element (FE) equations updating the parameters in each iterative step until the result converges to a local optimum. Numerical FE analysis, displacement sensitivity analysis, and the truncated singular value decomposition method are integrated into the solution algorithm for parameter identification. The technique introduced is verified in numerical studies with in-situ experiments of rock mass structures. Our results show that the approach, compared with conventional methods, can reduce ambiguities caused by inadequate data and provide more accurate insights into the subsurface for rock mass quality evaluation.

A proportional fusion adaptation algorithm to reduce noise in sEMG signals of the lower limb

Abstract To address issues related to unknown noise pollution and the inadequate performance of single adaptive noise reduction algorithms in measuring lower limb surface electromyography (sEMG) signals, this paper proposes an algorithm that combines the fast convergence speed and strong noise reduction capability of recursive least-squares (RLS) with the low computational complexity of the normalized least-mean-square (NLMS) and improved proportionate NLMS (IPNLMS). Through the proportional fusion of RLS with NLMS and RLS with IPNLMS, the proposed algorithm greatly improves convergence speed, stability, and noise reduction performance while effectively reducing computational complexity. Moreover, considering the influence of the initial value of weights on the noise reduction performance during the updating process of the adaptive noise reduction algorithm, a weight initial value setting (WIS) module is proposed to optimize the initial value of the weights by the known amount of data. Based on 50 independent experiments, an adaptive noise reduction algorithm and WIS module were used to reduce the unknown noise in the lower limb sEMG signals, which was generated by white Gaussian noise, power line interference, or hybrid noise interfered by an unknown environment. The noise reduction performance was evaluated by using the average value of the signal-to-noise ratio (SNR), the root-mean-square error, and R-square. Compared with the RLS, NLMS, and IPNLMS algorithms for noise reduction of vastus lateralis, rectus femoris, and biceps femoris signals containing unknown noise, the SNR of RLS-NLMS, RLS-IPNLMS, WIS-RLS-NLMS, and WIS-RLS-IPNLMS is improved by an average of [7.92%, 55.54%, 55.63%], [7.45%, 54.90%, 54.99%], [19.70%, 72.38%, 72.71%], [19.32%, 71.84%, 72.19%]. The simulation results verify that the proportional fusion adaptive noise reduction algorithm and the WIS module effectively accelerate the convergence speed, enhance the noise reduction capability, and reduce the computational complexity.

Real-time flood forecasting and uncertainty analysis: a case study of the Krong H’nang hydropower reservoir

ABSTRACT Early and reliable flood forecasting is crucial for mitigating the impacts of floods and ensuring the safe operation of irrigation and hydroelectric facilities. Traditional methods often use a calibrated event-based rainfall-runoff model with a limited number of simulations, making it difficult to measure parameter uncertainties accurately. This paper introduces a new method for estimating the parameters of the HEC-HMS model and evaluating the uncertainties in real-time flood forecasting. First, the Latin Hypercube Sampling (LHS) procedure is used to efficiently sample the parameter values throughout the feasible parameter space. Next, thousands of parameter sets are simulated using a deterministic rainfall-runoff model, generating numerous initial solutions for the Shuffled Complex Evolution (SCE-UA) algorithm to search and evolve. Additional parameter sets are then generated as the search evolves, leading to new simulations until the algorithm reaches the convergence criteria. The optimal solutions are selected based on predefined multiple objective functions. Uncertainty of the forecast results is also computed using the Generalized Likelihood Uncertainty Estimation (GLUE) method and represented as confidence intervals. The method was applied to forecast flood hydrographs at the Krong H’nang hydropower reservoir in Vietnam, using data from 33 flood events from 2016 to 2021. The results demonstrate that the program can effectively forecast the flood hydrograph up to 4 hours in advance (the Kling-Gupta efficiency KGE > 0.8 and the absolute relative volume error |VE| < 10%). The observed values fall within the uncertainty range of Q5%-Q95%. Compared to the conventional method, which uses a fixed parameter set value, this approach is superior and therefore can assist policymakers and operators in more effectively managing reservoir operations.

Travel pattern recognition of urban rail passengers based on spatiotemporal sequence similarity

Understanding passenger travel patterns is helpful for the allocation of passenger resources in urban rail transit. Based on rail transit smart card data, this paper proposes a method to identify travel patterns by modeling individual spatiotemporal sequences. First, all the stations visited by individual passengers are extracted, and the similarity of the stations is calculated by the frequency of inter-station travel, the distance between stations and the activity duration of the stations. The main spatial activity area of the individual is divided using a hierarchical clustering algorithm. Then, the spatiotemporal sequence is inferred based on the individual's travel order. The sequence is a set of discrete values that characterize the spatiotemporal state. PCA-KL and K-Means++ are used to extract the similarity sequence structure to identify the passenger travel pattern. Finally, the rail transit smart card data of Xi'an in a certain month is taken as an example to identify its passenger travel pattern. The results show that complex passenger flow has 5 travel modes, of which 3 typical modes are commuting travel in a macro sense, accounting for 79% of the passenger flow. It can be seen that the pattern recognition based on the similarity of individual spatiotemporal sequences in this paper fully reflects the particularity and versatility of the research method, and is highly operational for different cities.

Polygenic liabilities and treatment trajectories in early-onset depression: a Danish register-based study.

The clinical course of major depressive disorder (MDD) is heterogeneous, and early-onset MDD often has a more severe and complex clinical course. Our goal was to determine whether polygenic scores (PGSs) for psychiatric disorders are associated with treatment trajectories in early-onset MDD treated in secondary care. Data were drawn from the iPSYCH2015 sample, which includes all individuals born in Denmark between 1981 and 2008 who were treated in secondary care for depression between 1995 and 2015. We selected unrelated individuals of European ancestry with an MDD diagnosis between ages 10-25 (N = 10577). Seven-year trajectories of hospital contacts for depression were modeled using Latent Class Growth Analysis. Associations between PGS for MDD, bipolar disorder, schizophrenia, ADHD, and anorexia and trajectories of MDD contacts were modeled using multinomial logistic regressions. We identified four trajectory patterns: brief contact (65%), prolonged initial contact (20%), later re-entry (8%), and persistent contact (7%). Relative to the brief contact trajectory, higher PGS for ADHD was associated with a decreased odds of membership in the prolonged initial contact (odds ratio = 1.06, 95% confidence interval = 1.01-1.11) and persistent contact (1.12, 1.03-1.21) trajectories, while PGS-AN was associated with increased odds of membership in the persistent contact trajectory (1.12, 1.03-1.21). We found significant associations between polygenic liabilities for psychiatric disorders and treatment trajectories in patients with secondary-treated early-onset MDD. These findings help elucidate the relationship between a patient's genetics and their clinical course; however, the effect sizes are small and therefore unlikely to have predictive value in clinical settings.

Study on risk factor analysis and model prediction of hyperuricemia in different populations.

The purpose of the present study was to explore the influencing factors of hyperuricemia (HUA) in different populations in Shandong Province based on clinical biochemical indicators. A prediction model for HUA was constructed to aid in the early prevention and screening of HUA. In total, 705 cases were collected from five hospitals, and the risk factors were analyzed by Pearson correlation analysis, binary logistic regression, and receiver operating characteristic (ROC) curve in the gender and age groups. All data were divided into a training set and test set (7:3). The training set included age, gender, total protein (TP), low-density lipoprotein cholesterol (LDL-C), and 15 other indicators. The random forest (RF) and support vector machine (SVM) methods were used to build the HUA model, and model performances were evaluated through 10-fold cross-validation to select the optimal method. Finally, features were extracted, and the ROC curve of the test set was generated. TP, LDL-C, and glucose (GLU) were risk factors for HUA, and the area under the curve (AUC) value of the SVM validation set was 0.875. The SVM model based on clinical biochemical indicators has good predictive ability for HUA, thus providing a reference for the diagnosis of HUA and the development of an HUA prediction model.

The value of nomogram analysis in prediction of cerebral spread of hepatic alveolar echinococcosis

BackgroundThe prognosis after brain metastasis of alveolar echinococcosis is inferior, but there is currently no effective method to predict brain metastasis. PurposeTo explore the value of a nomogram constructed based on a CT plain scan and enhanced imaging features combined with clinical indicators in predicting brain metastasis of hepatic alveolar echinococcosis (HAE). Materials and MethodsThe imaging characteristics and clinical indicators of 116 patients diagnosed with HAE in the Affiliated Hospital of Qinghai University from 2015 to 2022 were retrospectively collected. The data were randomly divided into a training set and a validation set according to 7:3, and the difference between the two groups was analyzed. Binary logistic regression analysis was used to obtain independent predictors of brain metastasis in HAE, and a prediction model was constructed based on this and expressed in the form of a nomogram. Receiver operating characteristic (ROC) curve and calibration curve (CRC) were used to evaluate model performance, and decision curve analysis (DCA) was used to assess the clinical value of the predictive model. ResultA total of 116 HAE patients were included (average age 38.07±15.09 years old, 54 males and 62 females, 81 patients (70 %) in the training set, and 35 patients (30 %) in the validation set). There was no statistically significant difference between CT plain scan and enhanced imaging features combined with clinical indicators between the training set and the validation set (p > 0.05). After statistical analysis, it was found that whether there is invasion of the inferior vena cava, whether there is invasion of the hepatic artery, and whether there is metastasis to other organs are independent predictors of brain metastasis in HAE. A prediction model was built based on these three variables. The area under the ROC curve (AUC), cutoff value, sensitivity, and specificity of the training set and validation set were 0.922 and 0.886, 0.6934 and 0.6643, 75.00 and 84.62, 94.34 and 81.82, respectively. CRC shows good consistency between the predicted probability and the actual value of the sample. DCA showed that the clinical value of the model was high. ConclusionThe nomogram constructed based on imaging features combined with clinical indicators can effectively predict whether HAE will develop brain metastasis, which is helpful for clinicians to quickly screen out high-risk patients with HAE developing brain metastases, evaluate patient prognosis, and is more conducive to the realization of individualized and precise medical decisions.

Combined Drought Index Using High-Resolution Hydrological Models and Explainable Artificial Intelligence Techniques in Türkiye

We developed a combined drought index to better monitor agricultural drought events. To develop the index, different combinations of the temperature condition index, precipitation condition index, vegetation condition index, soil moisture condition index, gross primary productivity, and normalized difference water index were used to obtain a single drought severity index. To obtain more effective results, a mesoscale hydrologic model was used to obtain soil moisture values. The SHapley Additive exPlanations (SHAP) algorithm was used to calculate the weights for the combined index. To provide input to the SHAP model, crop yield was predicted using a machine learning model, with the training set yielding a correlation coefficient (R) of 0.8, while the test set values were calculated to be 0.68. The representativeness of the new index in drought situations was compared with established indices, including the Standardized Precipitation-Evapotranspiration Index (SPEI) and the Self-Calibrated Palmer Drought Severity Index (scPDSI). The index showed the highest correlation with an R-value of 0.82, followed by the SPEI with 0.7 and scPDSI with 0.48. This study contributes a different perspective for effective detection of agricultural drought events. The integration of an increased volume of data from remote sensing systems with technological advances could facilitate the development of significantly more efficient agricultural drought monitoring systems.

The Most Socially Vulnerable Patients Benefit the Most Following Gender Affirming Facial Surgery.

While studies have shown that access to facial feminization surgery can be restricted by financial and geographic limitations, there is a lack of information on the impact of surgery on the most vulnerable patients. Therefore, this study assessed the impact of social vulnerability and neighborhood socioeconomic disadvantage on patient-reported outcomes after facial feminization surgery. Patients were surveyed pre and postoperatively using the FACE-Q Aesthetics Questionnaire and geo-coded using home addresses to obtain social vulnerability index (SVI) and Area Deprivation Index scores. Two sets of Pearson correlation values were calculated: (1) between SVI scores and each of the pre and postoperative FACE-Q modules and (2) between SVI scores and differences between pre and postoperative FACE-Q modules. Univariate linear regression analyses were performed for the latter. All analyses were repeated for Area Deprivation Index scores. Twenty patients participated in this study. Postoperative facial appearance satisfaction positively correlated with total SVI (r = 0.48, P = 0.031), socioeconomic status theme (r=0.47, P=0.037), and racial and ethnic minority theme (r = 0.48, P = 0.031) scores. The difference between pre and postoperative facial appearance satisfaction positively correlated with total SVI (coefficient = 37.40, r = 0.47, P = 0.035), racial and ethnic minority theme (coefficient = 44.00, r = 0.46, P = 0.040), and housing type and transportation theme (coefficient = 46.97, r = 0.46, P = 0.042) scores. Patients impacted by greater social vulnerability disproportionally experience the greatest benefit from gender-affirming facial surgery.

Optimised prediction of tunnel fire heat release rate using the ResNet18_2CLSTM model with bagging for multimodal data

Accurate predictions of HRR will improve preparedness and response strategies, enhance safety, and minimise damage in tunnel fires. In this study, a deep learning prediction model for HRR under multimodal data fusion is proposed. A multimodal dataset is first established to obtain flame images and flue gas time series data through model-scale tunnel fire experiments. During the model training process, ResNet18 was used to extract features from the flame image, and 2CLSTM was employed to understand the time series of the flame image features and flue gas features to establish the correlation with the HRR. It was evaluated that the error analyses of the measured and predicted values of the validation set yielded R2 greater than 0.85, with errors and standard deviations less than 4 kW. And the model predicted better in the flame growth and decay phases. However, there is some deviation in the predictions near the peak HRR. To address this issue, the Bagging algorithm was introduced to optimise the model. The results show that the ResNet18_2CLSTM model with Bagging reduces the RMSE by 20.47 % and increases the R2 by 4.64 % compared to the original model, and the accuracy is greatly improved.

Parametric optimization of the process of engineering a space monitoring radar based on an ontological analysis of functional characteristics for various technical solutions

The article presents a hypothesis about the possibility of reducing the risks of suboptimal choice of technical solutions when creating promising intelligence and information complexes (RICS), which is an urgent task both in the development of a space monitoring radar station (MCP radar) and in the creation of electronic warfare (EW). It is shown that when formalizing the RIC design process in the form of multiparametric optimization of parameters based on the ontological analysis of technical solutions, which ensures the formation of an optimal list of technical solutions while maximizing functional characteristics and minimizing cost. A three-dimensional matrix of functional characteristics values is compiled during design, taking into account the set values of cost and production time for each technical solution with its own unification coefficient. Using this matrix allows you to choose the optimal technical solution of the radar, taking into account the coefficient of unification and the quality of the element base - such a matrix is compiled for each technical solution. The main advantage of the proposed optimization is the ability to evaluate an unlimited number of radar layout options and assess the risks of a suboptimal choice for each technical solution.

METHOD OF RECOGNITION OF FPV-UAV RADIO SIGNALS FORMED ACCORDING TO CROSSFIRE AND EXPRESSLRS STANDARDS

Unmanned aerial vehicles (UAVs) with the First Person View (FPV) system are the most common type of attack UAVs used at the tactical level by the armed forces of the russian federation. Timely detection of facts of their using by the enemy and countering them are important tasks solved by units of the Ukrainian Defense Forces. An effective countermeasure way for FPV-UAVs is to create interference in their radio control channels frequency bands. To increase the radio suppression range, it is necessary to use interference matched in frequency and structure, the necessary condition for the formation of which is the presence of a priori information about the structure of the radio signal. Analysis of the available information on the construction of enemy FPV-UAVs shows that their radio control and telemetry channels are usually combined, have time division multiple access, the signals are formed according to CrossFire and ExpressLRS standards and have fixed sets of parameter values that can be used as features in recognition. The proposed FPV-UAV radio signal recognition technique is based on decision tree and minimum metric methods and uses a priori information on frequency, time and modulation parameters of radio signals. The combination of the two decision-making methods made it possible to reduce the computational complexity by eliminating operations for determining the parameters of radio signals that do not correspond to the decisions taken at the previous stages, and provides the possibility of adding new time parameters without changing the developed recognition technique. The method consists of a sequence of operations for estimation radio signal parameters, comparing them with known values, making decisions about the type of standard and command- telemetry radio line operation mode. Only one fragment with a duration of one frequency element is sufficient to recognize the FPV-UAV radio signal, which reduces the technical requirements for detection means in which the method can be used.

The news values of fake news

Fake news poses a significant threat to society by undermining public trust and consensus on critical issues. Although there is a considerable amount of research on the linguistic features of fake news texts, a comprehensive understanding of how language is used to persuade and promote specific ideologies within them is still lacking. This study addresses this gap by analyzing fake news discourse through the lens of news values. We apply the Discursive News Values Analysis (DNVA) framework and key semantic domain analysis to a corpus of fake news stories on vaccination, climate change, and COVID-19. We identify a set of news values that differentiate fake from mainstream news discourse. Our findings reveal that fake news emphasizes negativity, unexpectedness, consonance, and facticity, while also relying on the previously undocumented news values of subversiveness, causality, religiosity, and historicity. These values form a powerful discursive toolkit exploited by fake news writers to craft compelling false narratives.

Application of Paired Correlation Algorithms for the Distance Matrices Between DNA Chains

Authors:This paper is a continuation of some previous works by the authors. We consider various algorithms for calculating distances between genomes of similar species (we use primarily mitochondrial DNA, mtDNA) and various distance matrices between the same genomes obtained on the basis of these algorithms. We can say, just to simplify the situation a little, that all our publications on the subject of DNA analysis are associated with various applications of metrics set on such matrices. The paper also has a second subject, i.e., the study of the obtained distance matrices using special statistical characteristics. We consider two matrices obtained for the mtDNA of 32 species of monkeys; the species were selected so that they all belong to different genera. For them, we have obtained 2 matrices of distances between genomes corresponding to the Jaro –Winkler’s and Needleman –Wunsch’s algorithms. Next, we considered all the triangles obtained in these matrices, and for each of them we used a specially calculated badness. It is actually a measure of the deviation of the resulting triangle from some acuteangled isosceles one. For two sequences of such badness, we have considered variants of paired correlation. At the same time, in addition to the two standard pair correlation algorithms (Spearman’s and Kendall’s ones), we also considered a new algorithm proposed by us. The reason for considering this new algorithm is as follows. In the usual way of calculating the correlation, we consider only the set of pairwise values of two random variables, without taking into account the pairs themselves. Vice versa, in both of the mentioned pair correlation algorithms, despite their slight difference, we consider only the order of the elements in these pairs, not paying attention to the values themselves; we specifically note that this also applies to Spearman’s criterion, which is usually written about as being more suitable for measurements made on an ordinal scale. In our proposed algorithm, we tried to take into account both the value of both random variables and their order in pairs. The results obtained are of interest. Thus, the “pole” variants (i.e., the usual correlation formula and standard pair correlation algorithms) show some (though very small) correlation between two sequences of 4960 pairs of triangles: from 0.1 to 0.4, depending on the specific algorithm, on whether preliminary normalization was carried out, etc. And the “intermediate” variant (taking into account both the order of pairs and the values of random variables) showed a complete lack of connection: the absolute value of the correlation coefficient did not exceed 0.006. Even more interesting is another result obtained in the work, which can be called a small connection between two well-known algorithms for determining the distances between genomes, namely, algorithms of Jaro –Winkler and Needleman –Wunsch.