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Using UK data supplied by universities, this paper confirms that women academics earn less than men, even after controlling for a range of covariates. Despite narrowing after 2004/05, the observed (unconditional) pay gap was still −0.089 in 2019/20, while the conditional pay gap was relatively unchanged remaining at around −0.050 in 2019/20. The results are consistent with the literature on why pay gaps might occur, with the key disparity occurring when women face a higher cost of investment and statistical discrimination, linked to bias, to achieve promotion. That is, the results presented here suggest that earnings gaps are significantly reduced when grade-balanced gender sub-groups are compared, suggesting conditional wage differences are more likely due to bias rather than any inherent differences in (research) productivity. [ABSTRACT FROM AUTHOR]

Background/Objectives: Patellofemoral Pain Syndrome (PFPS) is prevalent among physically active individuals, highlighting the need for innovative treatment strategies beyond conventional physiotherapy. This study investigates the effectiveness of integrating flossing band therapy with standard physiotherapy, anticipating improved outcomes in pain reduction, functional ability, and patient satisfaction. Methods: A double-blinded randomized controlled trial involved 50 PFPS-diagnosed participants. They were divided into two groups: Standard Physiotherapy Group (SPG) and Flossing Band and Physiotherapy Group (FBPG), each undergoing an 8-week intervention focusing on resistance training supplemented by respective therapies. Assessment metrics included pain (VAS), strength (Dynamometry), lower limb function (LEFS), and PFPS function (AKPS) measured before and after the intervention. Results: Significant enhancements in all outcome measures were noted for both groups, yet the FBPG exhibited notably superior improvements in pain, knee functionality, muscle strength, and lower extremity function. The FBPG demonstrated statistically significant greater efficacy in pain alleviation and strength enhancement. Conclusions: The addition of flossing band therapy to conventional physiotherapy presents a more effective treatment modality for PFPS, suggesting its potential to redefine therapeutic standards. Future studies should delve into the long-term impacts and mechanistic underpinnings of floss band therapy in PFPS management. [ABSTRACT FROM AUTHOR]

(1) Background: Trainers and athletes have always sought to reduce the failure of muscle function during long endurance events. However, nowadays, it is a topic that is generating much debate in the scientific field. Currently, deep-sea water (DSW) intake seems to be a suitable hydration alternative for this type of endurance event. Therefore, the aim of this study was to determine whether DSW consumption during a triathlon event could preserve muscle function after exercise. (2) Methods: Nineteen trained male triathletes (age = 39.0 ± 4.25 years; BMI = 23.67 ± 1.81 kg/m2) randomly performed three triathlons, one of them consuming DSW (Totum SPORT 30 AB, Laboratories Quinton International, S.L., Spain), the other consuming isotonic placebo and the last with tap water-hydration. A vertical jump test with countermovement and an isometric muscle strength test were conducted before and after the triathlon test. (3) Results: There was a significant difference between treatment × time during the isometric muscle strength test. Based on the Tukey post hoc analysis, the peak net force decreased statistically in the placebo (p = 0.045) and control conditions (p = 0.026), but not in the experimental condition (p = 0.121). In addition, all of the conditions studied obtained similar results in the countermovement vertical jump after exercise. (4) Conclusions: As a result, consumption of DSW seems to delay the failure of muscle function specifically in isometric exercises but does not improve performance in sports. Thus, DSW does not alter muscle capacity in a negative way; therefore, its consumption may be recommended. [ABSTRACT FROM AUTHOR]

Physical rehabilitation plays a crucial role in restoring motor function following injuries or surgeries. However, the challenge of overcrowded waiting lists often hampers doctors' ability to monitor patients' recovery progress in person. Deep Learning methods offer a solution by enabling doctors to optimize their time with each patient and distinguish between those requiring specific attention and those making positive progress. Doctors use the flexion angle of limbs as a cue to assess a patient's mobility level during rehabilitation. From a Computer Vision perspective, this task can be framed as automatically estimating the pose of the target body limbs in an image. The objectives of this study can be summarized as follows: (i) evaluating and comparing multiple pose estimation methods; (ii) analyzing how the subject's position and camera viewpoint impact the estimation; and (iii) determining whether 3D estimation methods are necessary or if 2D estimation suffices for this purpose. To conduct this technical study, and due to the limited availability of public datasets related to physical rehabilitation exercises, we introduced a new dataset featuring 27 individuals performing eight diverse physical rehabilitation exercises focusing on various limbs and body positions. Each exercise was recorded using five RGB cameras capturing different viewpoints of the person. An infrared tracking system named OptiTrack was utilized to establish the ground truth positions of the joints in the limbs under study. The results, supported by statistical tests, show that not all state-of-the-art pose estimators perform equally in the presented situations (e.g., patient lying on the stretcher vs. standing). Statistical differences exist between camera viewpoints, with the frontal view being the most convenient. Additionally, the study concludes that 2D pose estimators are adequate for estimating joint angles given the selected camera viewpoints. [ABSTRACT FROM AUTHOR]

The existing literature has examined the impact of the coronavirus disease (Covid-19) pandemic on business failure, highlighting firms' internal characteristics. However, information on changes in firms' external environments remains scarce. This study examines whether Covid-19 has also influenced the spatial co-locational patterns of business failure processes. Therefore, the authors apply symbolisation techniques to identify different business failure processes during pre- and post-covid periods. In addition, the authors compute the Q(m) spatial dependence test for categorical variables to identify differences in the colocation patterns before and after covid. The studie find significant differences in the spatial association of business failure processes, which are further understood when the authors examine sectoral differences. [ABSTRACT FROM AUTHOR]

In the media, a prevalent narrative is that the incumbent United States President Donald J. Trump lost the 2020 elections because of the way he handled the COVID-19 pandemic. Quantitative evidence to support this narrative is, however, limited. We put forward a spatial, information-theoretic approach to critically examine the link between voting behavior and COVID-19 incidence in the 2020 presidential elections. The approach overcomes classical limitations of traditional regression analysis, where it does not require an underlying mathematical model and it can capture nonlinear interactions. From the analysis of county-level data, we uncovered a robust association between voting behavior and prevalence of COVID-19 cases. Surprisingly, such an association points in the opposite direction from the accepted narrative: in counties that experienced less COVID-19 cases, the incumbent President lost more ground to his opponent, now President Joseph R. Biden Jr. A tenable explanation of this observation is the different attitude of liberal and conservative voters toward the pandemic, which led to more COVID-19 spreading in counties with a larger share of republican voters. [ABSTRACT FROM AUTHOR]

Background: Sustained engagement is essential for the success of telerehabilitation programs. However, patients' lack of motivation and adherence could undermine these goals. To overcome this challenge, physical exercises have often been gamified. Building on the advantages of serious games, we propose a citizen science--based approach in which patients perform scientific tasks by using interactive interfaces and help advance scientific causes of their choice. This approach capitalizes on human intellect and benevolence while promoting learning. To further enhance engagement, we propose performing citizen science activities in immersive media, such as virtual reality (VR). Objective: This study aims to present a novel methodology to facilitate the remote identification and classification of human movements for the automatic assessment of motor performance in telerehabilitation. The data-driven approach is presented in the context of a citizen science software dedicated to bimanual training in VR. Specifically, users interact with the interface and make contributions to an environmental citizen science project while moving both arms in concert. Methods: In all, 9 healthy individuals interacted with the citizen science software by using a commercial VR gaming device. The software included a calibration phase to evaluate the users' range of motion along the 3 anatomical planes of motion and to adapt the sensitivity of the software's response to their movements. During calibration, the time series of the users' movements were recorded by the sensors embedded in the device. We performed principal component analysis to identify salient features of movements and then applied a bagged trees ensemble classifier to classify the movements. Results: The classification achieved high performance, reaching 99.9% accuracy. Among the movements, elbow flexion was the most accurately classified movement (99.2%), and horizontal shoulder abduction to the right side of the body was the most misclassified movement (98.8%). Conclusions: Coordinated bimanual movements in VR can be classified with high accuracy. Our findings lay the foundation for the development of motion analysis algorithms in VR-mediated telerehabilitation. [ABSTRACT FROM AUTHOR]

Sea level rise (SLR) could have catastrophic consequences worldwide. More than 600 million people currently living in coastal areas may see their livelihood at risk and choose to migrate in the near future. Predicting when, how, and where people could migrate under environmental change is critical to devise effective policy initiatives and improve our preparedness. Here, we propose a modeling framework to predict the effect of SLR on migration patterns from easily accessible geographic and demographic data. The framework adapts the radiation model to capture unwillingness or inability to migrate of affected residents, as well as return migration and cascading effects in migration patterns. We apply the mathematical model to study internal migration in Bangladesh, where we predict a complex and counterintuitive landscape of migration patterns between districts. Our predictions indicate that the impact of SLR on 816,000 people by 2050 will trigger cascading effects in migration patterns throughout the entire country. The population of each of the 64 districts will change, leading to a total variation of 1.3 million people. Migration from inundated regions in the center will trigger non‐trivial patterns, including a reduction in the population of the district of the capital Dhaka. Plain Language Summary: Droughts, desertification, floods, earthquakes, and wildfire threaten livelihood worldwide, triggering more and more often human unrest. From wealthy to developing economies, every country is vulnerable to environmental change. Mathematical models can assist in providing reliable predictions of environmental migration, which are critical for devising effective policy initiatives and improving our preparedness for future migration patterns. Here, we establish a modeling framework to predict environmental migration from knowledge of the number of individuals who will be placed on the move due to environmental change. We specialize the model to the study of the catastrophic consequences of sea level rise (SLR) in Bangladesh. Our results indicate that the geography and demographics of Bangladesh could magnify the threat posed by SLR on internal migration. Although SLR will affect only a fraction of the districts, these districts have a critical role in migration patterns and local inundations therein will ultimately trigger human unrest in the entire country. Key Points: We establish a mathematical model that captures unwillingness or inability to migrate, return migrations, and cascading effectsFor the case study of Bangladesh, the model predicts population changes of each of the 64 districts, impacting 1.3 million people by 2050Migration from inundated central regions will trigger country‐wide, non‐trivial patterns, such as the reduction in the population of Dhaka [ABSTRACT FROM AUTHOR]

From epidemiology to economics, there is a fundamental need of statistically principled approaches to unveil spatial patterns and identify their underpinning mechanisms. Grounded in network and information theory, we establish a non-parametric scheme to study spatial associations from limited measurements of a spatial process. Through the lens of network theory, we relate spatial patterning in the dataset to the topology of a network on which the process unfolds. From the available observations of the spatial process and a candidate network topology, we compute a mutual information statistic that measures the extent to which the measurement at a node is explained by observations at neighbouring nodes. For a class of networks and linear autoregressive processes, we establish closed-form expressions for the mutual information statistic in terms of network topological features. We demonstrate the feasibility of the approach on synthetic datasets comprising 25–100 measurements, generated by linear or nonlinear autoregressive processes. Upon validation on synthetic processes, we examine datasets of human migration under climate change in Bangladesh and motor vehicle deaths in the United States of America. For both these real datasets, our approach is successful in identifying meaningful spatial patterns, begetting statistically-principled insight into the mechanisms of important socioeconomic problems. [ABSTRACT FROM AUTHOR]

BACKGROUND: Centrate treatment using microalgal–bacterial processes might be limited by the hydraulic retention time (HRT) required to achieve satisfactory chemical oxygen demand (COD) and nutrients removal. Moreover, the poor settling of microalgal biomass still limits the technical and economic performance of microalgal–bacterial processes. In this work, the performance of microalgal–bacterial aggregates (MABAs) supplied with flue gas was investigated as an effective strategy to improve the treatment of centrate from anaerobic digestion of winery wastewater. RESULTS: MABAs supplied with flue gas achieved maximum soluble COD, N‐NO3−, P‐PO43− and N‐NH4+ removal efficiencies of 95%, 94%, 100%, and 100%, respectively, in five‐fold centrate dilution within 7 days of operation. Centrate turbidity or its components did not hinder the performance of the MABAs under the conditions tested and no aggregates were formed in controls without MABAs inoculation. The mean diameter of the MABAs after centrate treatment was the same or even larger than that of the aggregates of the inoculum. Scanning electron microscopy analyses showed that the liquid medium composition influenced the structure and the type of microalgae cells established in the MABAs. CONCLUSION: MABAs‐based centrate treatment supported by flue gas is a promising technology for improving COD and nutrients removal from centrate as well as further biomass harvesting. © 2019 Society of Chemical Industry [ABSTRACT FROM AUTHOR]

Recurrence plots and recurrence quantification analysis are powerful tools to study the behavior of dynamical systems. What we learn through these tools is typically determined by the choice of a distance threshold in the phase space, which introduces arbitrariness in the definition of recurrence. Not only does symbolic recurrence overcome this difficulty, but also it offers a richer representation that book-keeps the recurrent portions of the phase space. Using symbolic recurrences, we can construct recurrence plots, perform quantification analysis, and examine causal links between dynamical systems from their time-series. Although previous efforts have demonstrated the feasibility of such a symbolic framework on synthetic data, the study of real time-series remains elusive. Here, we seek to bridge this gap by systematically examining a wide range of experimental datasets, from firearm prevalence and media coverage in the United States to the effect of sex on the interaction of swimming fish. This work offers a compelling demonstration of the potential of symbolic recurrence in the study of real-world applications across different research fields while providing a computer code for researchers to perform their own time-series explorations. [ABSTRACT FROM AUTHOR]

We propose a statistical procedure to determine if a spatial structure observed in the data is generated by a deterministic (even chaotic) spatial process rather than by a stochastic process. This procedure can be used as a specification test. It is robust against non-linearity and non-stationarity and can complete the toolbox for testing the diagnosis as well. The advantages of the presented methods are high power, simplicity, and ease and ample applicability for tests to be conducted, provided that weak conditions are required. Herein, we conduct several simulations to evaluate the performance of our procedure on well-known spatial processes and in situations where standard tests for spatial autocorrelation fail to detect spatial dependence. Guidelines for using the technique are also provided. [ABSTRACT FROM AUTHOR]

Recurrence quantification analysis offers a powerful framework to investigate complexity in dynamical systems. While several studies have demonstrated the possibility of multivariate recurrence quantification analysis, information-theoretic tools for the discovery of causal links remain elusive. Particularly enticing is to formulate information-theoretic tools on symbolic recurrence plots, which alleviate some of the methodological challenges of traditional recurrence plots and offer a richer representation of recurrences. Toward this aim, we establish a probability space in which we ground a theory of information that encodes information in the recurrences of the symbols. We introduce transfer entropy on symbolic recurrences as a tool to guide the inference of the strength and direction of the interaction between dynamical systems. We demonstrate statistically reliable discovery of causal links on synthetic and experimental time series, from only two time series or a larger dataset with multiple realizations. The proposed approach brings together recurrence plots, information theory, and symbolic dynamics to empower researchers and practitioners with effective means to visualize and quantify interactions in dynamical systems. [ABSTRACT FROM AUTHOR]

This paper aims to introduce the concept of symbolic correlation integral SC that is extensively used in many scientific fields. The new correlation integral SC avoids the noisy parameter 𝜀 of the classical correlation integral, defined by Grassberger and Procaccia (1983) and extensively used for constructing correlation-integral-based statistics, as in the BDS test. Once the free parameter 𝜀 disappears, it is possible to construct a nonparametric powerful test for independence that can also be used as a diagnostic tool for model selection. The symbolic correlation integral is also extended to deal with multivariate models, and a test for causality is proposed as an example of the theoretical power of the new concept. With extensive Monte Carlo simulations, the paper shows the good size and power performance of symbolic correlation-integral-based tests. [ABSTRACT FROM AUTHOR]

Inferring network topologies from the time series of individual units is of paramount importance in the study of biological and social networks. Despite considerable progress, our success in network inference is largely limited to static networks and autonomous node dynamics, which are often inadequate to describe complex systems. Here, we explore the possibility of reconstructing time-varying weighted topologies through the information-theoretic notion of transfer entropy. We focus on a Boolean network model in which the weight of the links and the spontaneous activity periodically vary in time. For slowly-varying dynamics, we establish closed-form expressions for the stationary periodic distribution and transfer entropy between each pair of nodes. Our results indicate that the instantaneous weight of each link is mapped into a corresponding transfer entropy value, thereby affording the possibility of pinpointing the dominant weights at each time. However, comparing transfer entropy readings at different times may provide erroneous estimates of the strength of the links in time, due to a counterintuitive modulation of the information flow by the non-autonomous dynamics. In fact, this time variation should be used to scale transfer entropy values toward the correct inference of the time evolution of the network weights. This study constitutes a necessary step toward a mathematically-principled use of transfer entropy to reconstruct time-varying networks. [ABSTRACT FROM AUTHOR]

This paper, based on the concept of symbolic correlation integral, introduces a set of symbolic recurrence plots and associated invariant measures, which are independent of the distance parameter, serving as a tool for quantifying the dynamic structure. These new measures allow the study of transient behavior, coexistence of attractors, bifurcations, and structural change. The final user does not have to choose the free distance parameter. An empirical application to electrocardiography data illustrates the use of symbolic recurrence measures. [ABSTRACT FROM AUTHOR]

Copyright of Cirugía y Cirujanos is the property of Publicidad Permanyer SLU and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Background: With the goal of achieving immune system reset, autologous hematopoietic stem cell transplantations have been performed in patients with multiple sclerosis (MS). Material and Methods: Two hundred and eighty-six consecutive patients with MS were autografted in a single center using non-frozen peripheral blood stem cells (PBSCs), on an outpatient basis and conditioning with cyclophosphamide and rituximab. The protocol was registered in Clinical- Trials.gov identifier NCT02674217. Results: One hundred and ninety-four females and 92 males were included; the median age was 47. All procedures were started on an outpatient basis and only 8 persons needed to be admitted to the hospital during the procedure. In order to obtain at least 1 × 106/kg viable CD34 cells, 1-4 aphereses were performed (median 1). The total number of viable CD34+ cells infused ranged between 1 and 19.2 × 106/kg (median 4.6). Patients recovered above 0.5 × 109/L absolute granulocytes on median day 8 (range 0-12). Two individuals needed red blood cells but none needed platelet transfusions. There were no transplant-related deaths and the 128-month overall survival of the patients is 100%. In 82 persons followed up for 3 or more months, the Expanded Disability Status Scale diminished from a mean of 5.2-4.9, the best results being obtained in relapsing-remitting and primary progressive MS. Conclusions: It is possible to conduct autotransplants for patients with MS employing non-frozen PBSCs and outpatient conduction. Additional information is needed to assess the efficacy of these procedures in the treatment of patients with MS. [ABSTRACT FROM AUTHOR]

Copyright of Revista de Investigación en Educación is the property of Universidad de Vigo, Facultad de Ciencias de la Educacion y del Deporte and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

The BDS test is the best-known correlation integral-based test, and it is now an important part of most standard econometric data analysis software packages. This test depends on the proximity ( $$\varepsilon )$$ and the embedding dimension ( $$m)$$ parameters both of which are chosen by the researcher. Although different studies (e.g., Kanzler in Very fast and correctly sized estimation of the BDS statistic. Department of Economics, Oxford University, Oxford, ) have been carried out to provide an adequate selection of the proximity parameter, no relevant research has yet been done on $$m$$. In practice, researchers usually compute the BDS statistic for different values of $$m$$, but sometimes these results are contradictory because some of them accept the null and others reject it. This paper aims to fill this gap. To that end, we propose a new simple, yet powerful, aggregate test for independence, based on BDS outputs from a given data set, that allows the consideration of all of the information contained in several embedding dimensions without the ambiguity of the well-known BDS tests. [ABSTRACT FROM AUTHOR]

Copyright of Retos: Nuevas Perspectivas de Educación Física, Deporte y Recreación is the property of Federacion Espanola de Asociaciones de Docentes de Educacion Fisica and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

The paper is a parametric and nonparametric analysis of past precipitation data. We analyze 90 years of daily precipitation data at the Sooke Reservoir in British Columbia, Canada, for evidence of climate change. We fit probability density functions (PDFs) to the data and find evidence of a structural break in the data generating process by using the Andrews-Ploberger test. To confirm this finding, we analyze higher moments of the distribution of precipitation and show that the PDFs have become more leptokurtic. The results show how the pattern of precipitation has changed. [ABSTRACT FROM AUTHOR]

In this article, we have proposed a simple diagnostic statistical procedure for testing the order of dependence of a spatial process. The proposed test is of nonparametric nature, and it is able to deal with potential nonlinear spatial dependencies. An added value is that from a methodological point of view, the new test is based on symbolic dynamics and hence on information theory. We characterized the behavior of a symbolic entropy measure in the presence of spatial dependencies of order higher than 1. The good power performance of the new method in detecting higher order spatial lags is notable and gives rise to an expectation that it may form a suitable basis for constructive specification searches. [ABSTRACT FROM AUTHOR]

This article addresses the question of improving the detection of nonlinear dependence by means of recently developed nonparametric tests. To this end a generalized version of BDS test and a new test based on symbolic dynamics are used on realizations from a well-known artificial market for which the dynamic equation governing the market is known. Comparisons with other tests for detecting nonlinearity are also provided. We show that the test based on symbolic dynamics outperforms other tests with the advantage that it depends only on one free parameter, namely the embedding dimension. This does not hold for other tests for nonlinearity. [ABSTRACT FROM AUTHOR]

The article explores the possible role of entropy-based measures for detecting spatial structure. The possible role is partially based on the superior performance of dependence entropy measures over traditional measures and partially on the idea of symbolic entropy. A nonparametric statistical procedure for selecting the order of the spatial lag is provided, along with a proposed symbolization procedure.

In this article we introduce a test for independence between two processes { X t} and { Y t}. To this end we rely on symbolic dynamics and permutation entropy as a measure of dependence. As a result, a nonparametric (model-free) test for either linear or nonlinear processes is presented. The test is consistent for a broad range of dependent alternatives. Empirical simulations indicate and highlight the general utility of the test for time-series analysts. [ABSTRACT FROM AUTHOR]

Background: The etiology of complex diseases is due to the combination of genetic and environmental factors, usually many of them, and each with a small effect. The identification of these small-effect contributing factors is still a demanding task. Clearly, there is a need for more powerful tests of genetic association, and especially for the identification of rare effects Results: We introduce a new genetic association test based on symbolic dynamics and symbolic entropy. Using a freely available software, we have applied this entropy test, and a conventional test, to simulated and real datasets, to illustrate the method and estimate type I error and power. We have also compared this new entropy test to the Fisher exact test for assessment of association with low-frequency SNPs. The entropy test is generally more powerful than the conventional test, and can be significantly more powerful when the genotypic test is applied to low allele-frequency markers. We have also shown that both the Fisher and Entropy methods are optimal to test for association with lowfrequency SNPs (MAF around 1-5%), and both are conservative for very rare SNPs (MAF<1%) Conclusions: We have developed a new, simple, consistent and powerful test to detect genetic association of biallelic/ SNP markers in case-control data, by using symbolic dynamics and symbolic entropy as a measure of gene dependence. We also provide a standard asymptotic distribution of this test statistic. Given that the test is based on entropy measures, it avoids smoothed nonparametric estimation. The entropy test is generally as good or even more powerful than the conventional and Fisher tests. Furthermore, the entropy test is more computationally efficient than the Fisher's Exact test, especially for large number of markers. Therefore, this entropy-based test has the advantage of being optimal for most SNPs, regardless of their allele frequency (Minor Allele Frequency (MAF) between 1-50%). This property is quite beneficial, since many researchers tend to discard low allele-frequency SNPs from their analysis. Now they can apply the same statistical test of association to all SNPs in a single analysis., which can be especially helpful to detect rare effects. [ABSTRACT FROM AUTHOR]

Let R be a commutative ring, G a group and RG its group ring. Let φ : RG → RG denote the R-linear extension of an involution φ defined on G. An element x in RG is said to be φ-antisymmetric if φ (x) = -x. A characterization is given of when the φ-antisymmetric elements of RG commute. This is a completion of earlier work. [ABSTRACT FROM AUTHOR]

Let R be a commutative ring, G a group and RG its group ring. Let φ: RG → RG denote the R-linear extension of an involution φ defined on G. An element x in RG is said to be antisymmetric if φ(x) = -x. A characterization is given of when the antisymmetric elements $(RG)^{-}_{\varphi}$ of RG commute except when Char(R) = 3. [ABSTRACT FROM AUTHOR]

Student satisfaction is a crucial aspect in the quality assessment of higher education. The aim of the present study was to assess the degree of satisfaction among students in the Faculties of physiotherapy throughout Spain concerning online teaching during the State of Emergency due to the COVID-19 pandemic. This was a quantitative study with a cross-sectional observational design. The online questionnaire DISFISCOVID was distributed to 24 physiotherapy faculties across Spain. A sample of 348 physiotherapy students from 14 Spanish universities completed the questionnaire. It showed high reliability evidence, achieving Cronbach's alpha indices higher than 0.870, alongside a McDonald's ωH of 0.876. On the whole, students were not satisfied with online learning during the State of Emergency, considering it unsuitable for their learning in either the theoretical or practical field of subjects in the degree of physiotherapy. In conclusion, the perception of physiotherapy students concerning the teaching they received, as far as practical contents and assessment methods are concerned, was not very satisfactory in those Faculties in which online learning platforms were not being used beforehand, and was more satisfactory when teaching was carried out in-person in the classroom. [ABSTRACT FROM AUTHOR]

Metabolism and physiology frequently follow non-linear rhythmic patterns which are reflected in concepts of homeostasis and circadian rhythms, yet few biomarkers are studied as dynamical systems. For instance, healthy human development depends on the assimilation and metabolism of essential elements, often accompanied by exposures to non-essential elements which may be toxic. In this study, we applied laser ablation-inductively coupled plasma-mass spectrometry (LA-ICP-MS) to reconstruct longitudinal exposure profiles of essential and non-essential elements throughout prenatal and early post-natal development. We applied cross-recurrence quantification analysis (CRQA) to characterize dynamics involved in elemental integration, and to construct a graph-theory based analysis of elemental metabolism. Our findings show how exposure to lead, a well-characterized toxicant, perturbs the metabolism of essential elements. In particular, our findings indicate that high levels of lead exposure dysregulate global aspects of metabolic network connectivity. For example, the magnitude of each element's degree was increased in children exposed to high lead levels. Similarly, high lead exposure yielded discrete effects on specific essential elements, particularly zinc and magnesium, which showed reduced network metrics compared to other elements. In sum, this approach presents a new, systems-based perspective on the dynamics involved in elemental metabolism during critical periods of human development. [ABSTRACT FROM AUTHOR]

This Special Issue (SI) brings together contributions from researchers working in symbolic analysis, complex dynamics, and information theory, from both theoretical and applied perspectives. References 1 Bandt C. Entropy Ratio and Entropy Concentration Coefficient, with Application to the COVID-19 Pandemic. C. Bandt [[1]] puts forward an entropy-based concentration coefficient C that avoids the limitations of other classical concentration measures such as Gini's index. [Extracted from the article]

Typical, definite forms of the reflex sympathetic dystrophy syndrome present no diagnostic problems, but the diagnosis of localized or very localized forms is very difficult. In the absence of characteristic roentgenographic evidence of acute, patchy, bony demineralization in the affected extremity, scintigraphy has proven to be a valuable examination. A retrospective analysis of 6 patients with a partial form of reflex sympathetic dystrophy with negative roentgenogram results who were evaluated by bone scintigraphy is presented. In the initial clinical stages, the predominant scintigraphic pattern was a very localized and intense hyperactivity in the internal femoral condyle and/or tibial plate of the affected joint on both blood pool and static images. The increased periarticular activity showed a marked decrease in association with remission of the clinical symptoms. In conclusion, bone scintigraphy was found to be a useful tool in the diagnosis and assessment of the therapeutic response. [ABSTRACT FROM AUTHOR]

A novel general method for constructing nonparametric hypotheses tests based on the field of symbolic analysis is introduced in this paper. Several existing tests based on symbolic entropy that have been used for testing central hypotheses in several branches of science (particularly in economics and statistics) are particular cases of this general approach. This family of symbolic tests uses few assumptions, which increases the general applicability of any symbolic-based test. Additionally, as a theoretical application of this method, we construct and put forward four new statistics to test for the null hypothesis of spatiotemporal independence. There are very few tests in the specialized literature in this regard. The new tests were evaluated with the mean of several Monte Carlo experiments. The results highlight the outstanding performance of the proposed test. [ABSTRACT FROM AUTHOR]

Total knee replacement (TKR) surgery ameliorates knee function and the quality of life of patients, although 20% still experience dissatisfaction due to pain limiting their function. Radiofrequency Diathermy (MDR) has shown improvements in knee osteoarthritis and patellofemoral pain syndrome. As such, this study aims to assess the effects of MDR in the postoperative treatment of TKR patients. Forty-two participants were allocated to an experimental, placebo, or control group. For two weeks, subjects performed daily knee exercises and MDR, knee exercises and placebo MDR, or only knee exercises. Data from the Visual Analogue Scale (VAS), Timed Up-and-Go (TUG) test, Five Times Sit-to-Stand Test (FSST), Western Ontario and McMaster Universities Arthritis Index (WOMAC), physical component summary (PCS), and the mental component summary (MCS) of the SF-12 questionnaire were collected. Group-by-time interaction was significant, with favorable results in the MDR group for VAS (p = 0.009) and WOMAC (p = 0.021). No significant differences were found for TUG, FSST, PCS, or MCS (p > 0.05). In conclusion, the addition of MDR to therapeutic knee exercises obtained better results for knee pain than exercise alone in patients who had recently undergone TKR surgery. [ABSTRACT FROM AUTHOR]