Index Of Conditions Research Articles

Estimation of adaptability parameters of collection hulled oats varieties according to productivity in the Kirov region

The current paper has presented the study results conducted on the experimental plot of the Falenskaya breeding station of the branch of the FSBI FANC of the North-East in 2021–2023. The purpose of the study was to evaluate the collection hulled oat varieties according to grain productivity and adaptability parameters in the soil and climatic conditions of the Kirov region. The materials for the study were 9 collection oat varieties and the standard variety ‘Krechet’. According to the trait ‘productivity’, there have been estimated adaptability parameters. During the testing years, the studied variety samples differed significantly in productivity variability (V = 16.8–30.0 %). The factor ‘year’ had the greatest effect on the formation of oat yields (90.3 %). In the arid year of 2021, the varieties’ productivity was low (276–401 g/m2), with a negative index of environmental conditions (Ij = -163), in the most favorable year of 2022, it was 463–682 g/m2 and Ij = 104, respectively. The variety samples ‘k-3967 0144’ (-190 g/m2) and ‘15584 Mutant 258’ (-214 g/m2) were characterized by high stress resistance. High genetic flexibility of mean productivity under contrasting conditions was established in the variety samples ‘15472 Sumphony’ (504 g/m2), ‘k-3967 0144’ (496 g/m2) and ‘15340 Uran’ (487 g/m2). There have been identified such gene sources as plastic ‘k-3967 0144’, ‘15472 Sumphony’, ‘15340 Uran’, ‘k-3951 0146’, ‘k-3954 0149’ (IEP = 1.13–1.0), and with high stability of the trait ‘productivity’ ‘k-3967 0144’, ‘15584 Mutant 258’, ‘k-3954 0149’ (SF = 1.47–1.78). Low variability and high homeostaticity were found in the variety samples ‘k-3967 0144’, ‘15584 Mutant 258’, ‘k-3954 0149’ (V = 16.8–22.8 %; Hom = 31.31–20.75) with a high stability index (Is = 5.95–4.38). The adaptability coefficient has confirmed the high adaptability of variety samples ‘k-3967 0144’, ‘15472 Sumphony’ and ‘15340 Uran’ (KA =112.3–108 %). In adaptive breeding for further work, the varieties ‘k-3967 0144’, ‘15584 Mutant 258’, ‘k-3954 0149’, ‘15472 Sumphony’, ‘15340 Uran’ are of great interest, since they are the most adaptive in terms of the sum of ranks. Based on a comprehensive estimation of productivity and adaptability parameters, ‘k-3967 0144’ was recognized as the best variety sample, which can give a large mean productivity (526 g/m2) in changing weather conditions.

Evaluating Australia's financial conditions: A comparative analysis and extension of a dynamic factor model index

AbstractThis article investigates the unique financial conditions in Australia in the wake of COVID‐19, a subject of critical importance given the recent unprecedented economic events such as the fastest rate rises since the 1990s and the presence of persistently high inflation. This study aims to contribute to the literature by examining the speed of interest rate changes and its implications on financial conditions, particularly in an environment characterised by negative real interest rates. The research aims to extend and refine the financial conditions index (FCI) developed by the Reserve Bank of Australia by incorporating a momentum variable to capture the speed of interest rate changes. A dynamic factor model is utilised to investigate 73 data series across nine main categories, constructing the FCI on a quarterly basis. The results indicate that rapid interest rate changes significantly impact financial conditions, holding significant weights within the model, thus requiring careful policy considerations.

Towards understanding post-COVID-19 condition: A systematic meta-analysis of transcriptomic alterations with sex-specific insights

BackgroundPost COVID-19 Condition (PCC), characterized by lingering symptoms post-acute COVID-19, poses clinical challenges, highlighting the need to understand its underlying molecular mechanisms. This meta-analysis aims to shed light on the transcriptomic landscapes and sex-specific molecular dynamics intrinsic to PCC. MethodsA systematic review identified three studies suitable for comprehensive meta-analysis, encompassing 135 samples (57 PCC subjects and 78 recovered subjects). We performed meta-analysis on differential gene expression, a gene set enrichment analysis of Reactome pathways, and weighted gene co-expression network analysis (WGCNA). We performed a drug and disease enrichment analysis and also assessed sex-specific differences in expression patterns. Key findingsA clear difference was observed in the transcriptomic profiles of PCC subjects, with 530 differentially expressed genes (DEGs) identified. Enrichment analysis revealed that the altered pathways were predominantly implicated in cell cycle processes, immune dysregulation and histone modifications. Antioxidant compounds such as hesperitin were predominantly linked to the hub genes of the DEGs. Sex-specific analyses highlighted disparities in DEGs and altered pathways in male and female PCC patients, revealing a difference in the expression of ribosomal proteins. PCC in men was mostly linked to neuro-cardiovascular disorders, while women exhibited more diverse disorders, with a high index of respiratory conditions. ConclusionOur study reveals the intricate molecular processes underlying PCC, highlighting that the differences in molecular dynamics between males and females could be key to understanding and effectively managing the varied symptomatology of this condition.

The English Sublexical Toolkit: Methods for indexing sound–spelling consistency

This work introduces the English Sublexical Toolkit, a suite of tools that utilizes an experience-dependent learning framework of sublexical knowledge to extract regularities from the English lexicon. The Toolkit quantifies the empirical regularity of sublexical units in both the reading and spelling directions (i.e., grapheme-to-phoneme and phoneme-to-grapheme) and at multiple grain sizes (i.e., phoneme/grapheme and onset/rime unit size). It can extract multiple experience-dependent regularity indices for words or pseudowords, including both frequency indices (e.g., grapheme frequency) and conditional probability indices (e.g., grapheme-to-phoneme probability). These tools provide (1) superior estimates of the regularities that better reflect the complexity of the sublexical system relative to previously published indices and (2) completely novel indices of sublexical units such as phonographeme frequency (i.e., combined units of individual phonemes and graphemes that are independent of processing direction). We demonstrate that measures from the toolkit explain significant amounts of variance in empirical data (naming of real words and lexical decision), and either outperform or are comparable to the best available consistency measures. The flexibility of the toolkit is further demonstrated by its ability to readily index the probability of different pseudowords pronunciations, and we report that the measures account for the majority of variance in these empirically observed probabilities. Overall, this work provides a framework and resources that can be flexibly used to identify optimal corpus-based consistency measures that help explain reading/spelling behaviors for real and pseudowords.

Indices for ecological condition of native vegetation: A review, and introducing the HH2.0 method

SummaryThe concept of using a single index to encapsulate the ‘ecological condition’ or ‘quality’ of native vegetation is reviewed and critiqued, along with the history of the adoption of such indices by governments at the State and Federal levels in Australia. Despite the value judgements inherent in such a concept, these indices provide a currency that is useful for comparing sites for purposes such as to provide a nexus between vegetation proposed to be removed under permit and other vegetation that will be protected or improved as an ‘offset’. The oldest of the reviewed indices – Habitat Hectares, from the Victorian Government – has been the subject of more scrutiny than others but there has been negligible change to it in response to recommendations made. A recommendation of particular significance is for values of the index to respond smoothly to changes in continuously variable observables like foliage cover, rather than discretising the data into broad class intervals. The step‐wise jumps in index values that result from discretisation can have the effects of creating false indications of difference between sites or over time and masking actual differences. The index's value for monitoring change is thereby impaired. The Biodiversity Assessment Method of New South Wales is the only index reviewed here that avoids discretisation. However, many of the other recommendations that have been made for changes to Habitat Hectares were adopted in 2003 for a simplified variant of that method – the ‘Land manager self‐assessment method’. Therefore, this variant has been used as the basis for a new index – HH2.0 – that retains the simplifications but does not discretise continuous variables or allocate scores in a step‐wise manner. The process of translating field observations into scores is automated by a Microsoft Excel workbook, saving time and reducing human error. HH2.0 will suit landowners, land managers, local government and regional authorities, for non‐regulatory purposes such as determining what planning protection to apply to an area or prioritising vegetation management among different areas. It requires significantly less gathering of field data than Habitat Hectares, but like that method, it places weight on vegetation's similarity to a long‐undisturbed state. No restriction is made on whether it is applied to plots of specified size or shape. A pre‐existing set of observational data has been used to retrospectively determine scores under HH2.0 and compare them with Habitat Hectares scores. The data came from 37 sites spanning a range of vegetation types and ecological condition. The root‐mean‐square difference between the two methods' scores is 5.1 points (out of a theoretical maximum of 100). For reference, Tolsma and Newell (2003) concluded that a difference of 20 points between two Habitat Hectares assessments cannot be relied upon to indicate a genuine difference in ecological condition. A larger data set would be desirable but even if there were no comparison at all between indices (as normal for a new index), that would not invalidate the use of HH2.0 for its intended non‐regulatory purposes. HH2.0 is designed for use in Victoria but the changes that have been made compared with Habitat Hectares could be applied to create faster, simpler variants of indices used in other jurisdictions.

Cost-optimization based target reliabilities for design of structures exposed to fire

Adequacy of structural fire design in uncommon structures is conceptually ensured through cost-benefit analysis where the future costs are balanced against the benefits of safety investment. Cost-benefit analyses, however, are complicated and computationally challenging, and hence impractical for application to individual projects. To address this issue, design guidance proposes target reliability indices for normal design conditions, but no target reliability indices are defined for structural fire design. We revisit the background of the cost-optimization based approach underlying normal design target reliability indices then we extend this approach for the case of fire design of structures. We also propose a modified objective function for cost-optimization which simplifies the evaluation of target reliability indices and reduces the number of assumptions. The optimum safety level is expressed as a function of a new dimensionless variable named “Damage-to-investment indicator” (DII). The cost optimization approach is validated for the target reliability indices for normal design condition. The method is then applied for evaluating DII and the associated optimum reliability indices for fire-exposed structures. Two case studies are presented: (i) a one-way loaded reinforced concrete slab and (ii) a steel column under axial loading. This study thus provides a framework for deriving optimum (target) reliability index for structural fire design which can support the development of rational provisions in codes and standards.

Tracking Weekly State-Level Economic Conditions

AbstractThis paper develops a novel dataset of weekly economic conditions indices for the 50 U.S. states going back to 1987 based on mixed-frequency dynamic factor models with weekly, monthly, and quarterly variables that cover multiple dimensions of state economies. We find considerable cross-state heterogeneity in the length, depth, and timing of business cycles. We illustrate the usefulness of these state-level indices for quantifying the main contributors to the economic collapse caused by the COVID-19 pandemic and for evaluating the effectiveness of the Paycheck Protection Program. We also propose an aggregate indicator that gauges the overall weakness of the U.S. economy.

Nonlinear deformations of size-dependent porous functionally graded plates in a temperature field

In this paper, the Variational Iteration Method (VIM) or Extended Kantorovich Method (EKM) is formulated for the first time for flexible porous functionally graded (PFGM) plates with different boundary conditions and geometric nonlinearity according to the theory of Theodore von Karman. Its accuracy and efficiency are demonstrated. The plate is subjected to a uniform transversal load and temperature field. The displacement field of the plate is approximated based on the classical plate theory (CTP) or Kirchhoff's plate theory. The governing equations are derived using Hamilton's principle. Modified Coupled Stress Theory (MCST) is used to account for size-dependent effects. The material properties vary with thickness and are temperature dependent. Four porosity distribution patterns are considered in this study. Several examples are solved to demonstrate the proposed algorithm. The results obtained are compared with solutions obtained by the Bubnov-Galerkin Method (BGM) in higher approximations, the Finite Difference Method (FDM) of second order accuracy, as well as with results obtained by the Finite Element Method (FEM) of other authors. The results include an analysis of the effect of size dependent parameters, porosity type pattern, porosity index, functionally graded index, temperature field and different types of boundary conditions on the stress–strain state and bending deflection of plates.

A Kinematics-Based Optimization Design for the Leg Mechanism of a Novel Earth Rover

Abstract This paper proposes a general kinematic-based design method for optimizing the side-mounted leg mechanism of BJTUBOT, a novel multi-mission quadrupedal Earth rover. The focus issue lies in designing structural improvements that not only enhance its kinematic performance but also prevent singularity, all while meeting the demands for miniaturization and lightweight without deviating from the original leg design concept. To solve this issue, a novel 3-UPRU&PPRR mechanism is envisaged based on the original configuration. Around the unique structural features of this mechanism, its inverse kinematic solution and Jacobian matrix are calculated, and a coupled motion relation between a key limb and its moving platform (MP) is presented. In order to achieve singularity avoidance, some typical singularity configurations based on line geometry analysis are given. In accordance with this result, an initial configuration for multi-objective dimensional optimization is presented. To further enhance its kinematic performance, we introduce the use of the GCI (global conditional index) performance at extreme positions as one of the optimization criteria based on the NSGA-II (Non-dominated Sorting Genetic Algorithm) algorithm, and directly measuring the crowding distance using the position vector of the U (universal) joints on the moving platform. This optimized mechanism prototype is demonstrated in a single-leg Adams simulation, which exhibits good velocity mapping effects and displacement accuracy. Finally, a new BJTUBOT prototype was constructed based on the optimized leg, and its flexibility was tested with various classical forms of motions. The workflow in this paper significantly improves the leg performance under the current design needs.

Forecasting Consumer Price Index with Federal Open Market Committee Sentiment Index

AbstractThe Federal Open Market Committee (FOMC) is a component of the Federal Reserve System responsible for overseeing open market operations. The FOMC meets roughly eight or more times per year to assess the economy of the United States. After each meeting, the FOMC releases a statement to the press outlining its assessment of the US economy and its monetary policy stance. The sentiment of these statements may have an influence on the US economy and financial markets. Using sentiment and correlational analyses, this research examines how the sentiment of these statements affects the US economy and financial markets by analyzing how FOMC statement sentiment is correlated with the Consumer Price Index (CPI), the National Financial Conditions Index (NFCI), and the Adjusted National Financial Conditions Index (ANFCI). We find evidence to suggest that there is a moderate negative correlation between an FOMC statement's sentiment and the US City Average CPI value associated with the month before and the month after the statement's release. We also find that there is no evidence to suggest there exists a correlation between an FOMC statement's sentiment and the NFCI value associated with the week before or the week after the statement's release. However, we do find evidence to suggest that there is a moderate negative correlation between an FOMC statement's sentiment and the ANFCI value associated with the week before and the week after the statement's release. We also found that out of the three models we tested (linear regression, vine copula regression, and Gaussian copula regression), the Gaussian copula regression model performs the best when forecasting the CPI and the ANFCI. Additionally, we find that when forecasting CPI values, the models that include FOMC statement sentiment are more accurate than the models that exclude FOMC statement sentiment.

Finansal Koşullar Endeksi ile Ekonomik Büyüme ve Net Sermaye Girişi Arasındaki İlişkinin İncelenmesi: Türkiye İçin Nedensellik Analizi

The global economic crisis, which had a devastating effect worldwide with the bankruptcy of Lehman Brother's investment bank in the United States of America in 2008, revealed that countries should indicate risks and take precautions accordingly. In this sense, the importance of the Financial Conditions Index (FCI), which was created by considering the monetary policy instruments of the country's central banks and various macroeconomic and financial variables as a whole, has also increased. FCI, the index that has taken place in the literature since the 1990s, with the growth of countries, has been investigated in empirical studies conducted after the global economic crisis. In this study, the related index is calculated using Principal Component Analysis (PCA), and the data generated is analyzed with growth and net capital inflow items. According to the results of the related analysis, while there is bidirectional causality between FCI and real gross domestic product (GDP) growth, one-way causality was found between FCI and net capital inflow. The index created accordingly is the Granger cause of the net capital inflow.

Adaptive properties and ecological plasticity of promising lines of spring soft wheat in the conditions of the Central Non-Black Earth Regions of Russia

In the conditions of the Ryazan region, adaptive properties and ecological plasticity of 8 breeding lines of spring soft wheat (standard Agatha variety) were studied in 2020–2023 according to the "yield" trait using diverse statistical methods. Ecological plasticity (bi), stability (σ2d ), index of environmental conditions (Ij) were determined by S. A. Eberhart, W. A. Russell, variation – by B. A. Dospekhov, stress resistance and genetic flexibility – by A. A. Rossielle, J. Hemblin, index of ecological plasticity – by A. A. Gryaznov, indicator the level of the variety yield stability – according to E. D. Nettevich, homeostaticity (Hom ), breeding value (Sc) – according to V. V. Hangildin, relative stability, stability criterion – according to N. A. Sobolev, multiplicativity (KM) – according to V. A. Dragavtsev. It has been established that the yield level is mostly influenced by the factor "environment" – 51.5 %, the contribution of the factor "genotype" is 11.5 %. The average yield in the experiment was 5.19 t/ha, favorable conditions in 2022 (Ij = 0.50) allowed the yield to be 17.9 % higher than in 2021 (Ij = -0.52). Lines 2-17 (6.24 t/ha) and 21-17 (6.05 t/ha) had high yields, their genetic flexibility index was 5.58 and 5.41 t/ha, respectively. Lines 1-17, 465-17 and 531-17 had the highest resistance to stress (-0.63, -1.00), they are characterized by high yield stability (σ2d = 0.27–0.65) and the least decrease in it under adverse conditions. Strong responsiveness to changes in environmental conditions was noted for lines 290-17, 2-17 and 21-17: bi – 1.25–1.29, KM – 2.22–2.27. According to the degree of homeostaticity, line 1-17 was distinguished (Sc – 4.63, PUSS – 212, Hom – 18.3), these indicators exceeded the average values by 10.2 %, 81.0 abs. %, 98.9 %, respectively. According to the results of the integrated assessment by the ranking method, breeding lines 1-17; 465-17 and 21-17 showed varying degrees of adaptability, environmental stability, homeostaticity and had a high rating. The lowest rating in terms of the sum of the ranks of adaptability and plasticity parameters was obtained by the standard Agate variety.

Adaptability of buckwheat varieties in the forest-steppe of the Krasnoyarsk Territory

The article presents the results of the study of eight buckwheat varieties (Dikul (standard), Dasha, Diana, Design, Zhdanka, Zemlyachka, Natasha, Yashlek) for adaptability in the conditions of the Krasnoyarsk forest-steppe. For this purpose, the following indices were determined for each variety: plasticity coefficient, stability measure, intensity, stability index, variety stability level index, homeostaticity, breeding value of the variety, and adaptability coefficient. The hydrothermal coefficient showed that in 2021 the period from the second ten-day period of May to the first ten-day period of September was dry, in 2020 and 2022 – excessively humidified. The index of environmental conditions was with the sign "–" only in 2022 (–2.942). It was with a "+" sign in 2020 and 2021 (+2.821 and +0.121, respectively). This made it possible to determine the ability of the studied buckwheat varieties to adapt under contrasting conditions. Based on the plasticity coefficient in combination with the measure of stability, the conclusion was made about the ability of the varieties Zemlyachka, Diana, Design, Dikul, Yashlek to give high yield in favorable conditions. But Diana, unlike the other three varieties, in unfavorable conditions can noticeably reduce the yield. The varieties Design, Dikul and Yashlek showed slight yield reduction in the same situation. Dasha and Natasha varieties can produce higher yields under the worst conditions. At the same time variety Dasha is able to give both high and low yields in any conditions, which indicates its high meteorological dependence, especially since the value of the yield of this variety varies little from year to year. At the same time, the Zhdanka variety can sharply reduce yields in some years. The adaptability coefficient showed that Yashlek, Design and Diana have a very high degree of adaptability. Dikul can also be attributed to the varieties with a high ability to adapt to stressful conditions. According to the results of the study, Yashlek is the most adaptive variety (sum of the ranks amounted to 22). Diana, Design, Dasha and Dikul were slightly behind (sum of the ranks was 24–33).

A prediction index of the volatile organic compounds pollution conditions in a chemical industrial park based on atmospheric stability

Volatile organic compounds (VOCs), as common precursors of ozone (O3) and fine particulate matter (PM2.5), are a focus of air pollution prevention and control. Furthermore, with the rapid development of industry, industrial sources have become the largest source of anthropogenic VOCs emissions, leading to economic development while causing great harm to the environment. It is becoming meaningful to efficiently predict the future total volatile organic compounds (TVOC) pollution conditions in chemical industrial parks (CIPs), which can assist managers in carrying out corporate emission management in advance. In this study, TVOC monitoring data and meteorological data from January 1, 2022, to December 31, 2022, were used to innovatively construct the TVOC pollution index. This index comprehensively considers the atmospheric stability and localized horizontal diffusion conditions and can quickly and accurately predict the variations in the TVOC in a CIP in the next 7 days. In addition, we used synoptic weather patterns and backward trajectory analysis to explore the mechanism of VOCs pollution formation in a CIP. The results show that the combined influences of a westerly wind pattern, temperatures above 30 °C, a subtropical high pressure system, more upwind pollutants, and the horizontal and vertical diffusion conditions in the CIP were unfavorable, leading to VOCs pollution.

Macroeconomic and financial mixed frequency factors in a big data environment

Abstract In this paper, we evaluate the predictive content of 3 new business condition indexes and uncertainty measures that are estimated using high-frequency financial and low-frequency macroeconomic time series data. More specifically, our measures are defined as latent factors that are extracted from a state space model that includes multiple different frequencies of non-parametrically estimated components of quadratic variation, as well as mixed frequency macroeconomic variables. When forecasting growth rates of various monthly financial and macroeconomic variables, use of our new mixed frequency factors is shown to result in significant improvement in predictive performance, relative to a number of benchmark models. Additionally, when used to forecast corporate yields, predictive gains associated with the use of our measures are shown to be monotonically increasing, as one moves from predicting higher to lower rated bonds. This is consistent with the existence of a natural pricing channel wherein financial risk (as measured using our volatility factors) contains more predictive information for lower grade bonds. We also find that a variety of extant risk factors including the Aruoba et al. [(2009a). Real-time measurement of business conditions. Journal of Business & Economic Statistics, 27(4), 417427] business conditions index also contain marginal predictive content for the variables that we examine, although their inclusion does not reduce the usefulness of our measures.

Nonlinear causal network learning via Granger causality based on extreme support vector regression.

For complex networked systems, based on the consideration of nonlinearity and causality, a novel general method of nonlinear causal network learning, termed extreme support vector regression Granger causality (ESVRGC), is proposed. The nonuniform time-delayed influence of the driving nodes on the target node is particularly considered. Then, the restricted model and the unrestricted model of Granger causality are, respectively, formulated based on extreme support vector regression, which uses the selected time-delayed components of system variables as the inputs of kernel functions. The nonlinear conditional Granger causality index is finally calculated to confirm the strength of a causal interaction. Generally, based on the simulation of a nonlinear vector autoregressive model and nonlinear discrete time-delayed dynamic systems, ESVRGC demonstrates better performance than other popular methods. Also, the validity and robustness of ESVRGC are also verified by the different cases of network types, sample sizes, noise intensities, and coupling strengths. Finally, the superiority of ESVRGC is successful verified by the experimental study on real benchmark datasets.

2012 The long-term clinical outcomes of older adults with frailty following acute hospital admission in Tanzania

Abstract Background Non-communicable disease, multimorbidity and frailty are posing considerable challenges as global populations age. Healthcare systems in Low- and Middle-Income Countries are having to rapidly adapt services to meet the needs of older people. Objective This study, the first of its kind in sub-Saharan Africa, aimed to establish whether screening older people for frailty on admission to hospital could be used to identify those at greatest risk of adverse outcomes. Methods At baseline assessment, 308 participants aged ≥60 years, admitted to medical wards at four hospitals in the Kilimanjaro Region of Tanzania, were screened for frailty using the Clinical Frailty Scale (CFS). After 10-12 months, participants, and their informants, were contacted by telephone to establish clinical outcomes. The primary outcome was all-cause mortality. Cox regression was used to estimate hazard ratios ( 95% confidence interval) for mortality, with dichotomised CFS frailty status (frail if ≥5) as the independent variable. Results Primary outcome data were obtained for 194 (63.0%) of the original participants after a mean follow-up period of 10.8 ( 0.9) months. Mean age was 75.1 years and 99 (51%) of respondents were female. A total of 100 (51.5%) respondents were deceased and hazard ratios for all-cause mortality demonstrate that those with frailty were at significantly greater risk of mortality (HR 2.27 [CI 1.39 – 3.69], p<0.01), an effect that persisted even after adjustment for age, baseline Barthel Index, education and number of chronic conditions. Conclusion For older people living in Tanzania, unplanned admission to hospital is associated with high mortality and frailty is a strong independent predicator. In accurately identifying which older people are at the greatest risk, frailty screening using the CFS could provide a starting point for the development of targeted care pathways and interventions.

Noise-Tolerant Discrimination Indexes for Fuzzy ɣ Covering and Feature Subset Selection.

Fuzzy β covering (FBC) has attracted considerable attention in recent years. Nevertheless, as the basic information granularity of FBC, fuzzy β neighborhood does not satisfy reflexivity, which may lead to instability in classification learning and decision-making. Although a few studies have involved reflexive fuzzy β neighborhoods, they only focus on a single fuzzy covering and cannot effectively deal with the information representation and information fusion of multiple fuzzy coverings. Moreover, there is a lack of investigation on noise-tolerant uncertainty measures for FBC, as well as their application in feature selection. Motivated by these issues, we investigate a noise-tolerant variable precision discrimination index (VPDI) by means of a new reflexive fuzzy covering neighborhood. To this end, fuzzy ɣ neighborhood with reflexivity is introduced to characterize the information fusion of a fuzzy covering family. An uncertainty measure called fuzzy ɣ neighborhood discrimination index is then presented to reflect the discriminatory power of fuzzy covering families. Some variants of the uncertainty measure, such as variable precision joint discrimination index, variable precision conditional discrimination index, and variable precision mutual discrimination index, are then put forth by means of fuzzy decision. These VPDIs can be used as an evaluation metric for a family of fuzzy coverings. Finally, the knowledge reduction of fuzzy covering decision systems is addressed from the point of keeping the discriminatory power, and a heuristic feature selection algorithm is designed by means of the variable precision conditional discrimination index. The experiments on 16 public datasets exhibit that the proposed algorithm can effectively reduce redundant features and achieve competitive results compared with six state-of-the-art feature selection algorithms. Moreover, it demonstrates strong robustness to the interference of random noise.

Weak convergence of the conditional single index $ U $-statistics for locally stationary functional time series

<abstract><p>In recent years, there has been a notable shift in focus towards the analysis of non-stationary time series, driven largely by the complexities associated with delineating significant asymptotic behaviors inherent to such processes. The genesis of the theory of locally stationary processes arises from the quest for asymptotic inference grounded in nonparametric statistics. This paper endeavors to formulate a comprehensive framework for conducting inference within the realm of locally stationary functional time series by harnessing the conditional $ U $-statistics methodology as propounded by W. Stute in 1991. The proposed methodology extends the Nadaraya-Watson regression function estimations. Within this context, a novel estimator was introduced for the single index conditional $ U $-statistics operator, adept at accommodating the non-stationary attributes inherent to the data-generating process. The primary objective of this paper was to establish the weak convergence of conditional $ U $-processes within the domain of locally stationary functional mixing data. Specifically, the investigation delved into scenarios of weak convergence involving functional explanatory variables, considering both bounded and unbounded sets of functions while adhering to specific moment requirements. The derived findings emanate from broad structural specifications applicable to the class of functions and models under scrutiny. The theoretical insights expounded in this study constitute pivotal tools for advancing the domain of functional data analysis.</p></abstract>

A Conditional Mediation Analysis of Ad Value Moderated by Time Spent on YouTube

Social media usage has increased significantly in the last few years due to its easy access and low cost. Besides other social media forums, YouTube usage worldwide has also increased profoundly. A recent decline in the number of advertisements on YouTube has led to YouTube skepticism about its full-fledged operations in Pakistan. Also, many firms in Pakistan have reduced their advertisement budgets on YouTube. Given its importance, the study has examined the factors affecting YouTube ad avoidance. The study also examined the mediating role of ad value and the moderating role of time spent on YouTube. In addition, the study has also examined the conditional indirect effect of time spent on YouTube. We collected data based on an e-questionnaire, in which 457 participants completed the questionnaires. Our study found that perceived ad irritation (PAIR) and perceived ad goal impediment (PAGI) positively affect perceived ad avoidance (PAAV). The study also documents that perceived ad information (PAIN) and perceived Ad entertainment (PAEN) insignificantly affect perceived ad avoidance (PAAV). The study proposed four mediating hypotheses, and we found support for only one hypothesis, stating that perceived ad avoidance (PAAV) mediates perceived ad goal impediment (PAGI) and perceived ad avoidance (PAAV). However, the study found time spent on YouTube (TSOY) moderates perceived ad value (PAVL) and perceived ad avoidance (PAAV). We also found that the time spent on YouTube’s conditional mediation Index (CoMe Index) is statistically significant.