Structural Hierarchy Research Articles

Central Autonomic Network and Heart Rate Variability in Premature Neonates

Introduction: The Central Autonomic Network (CAN) is a hierarchy of brain structures that collectively influence cardiac autonomic input, mediating the majority of brain-heart interactions, but has never been studied in premature neonates. In this study, we use heart rate variability (HRV), which has been described as the “primary output” of the CAN, and resting-state functional MRI (rsfMRI) to characterize brain-heart relationships in premature neonates. Methods: We studied premature neonates who underwent rsfMRI at term (37-week postmenstrual age or above) and had HRV data recorded during the same week of their MRI. HRV was derived from continuous electrocardiogram data during the week of the rsfMRI scan. For rsfMRI, a seed-based approach was used to define regions of interest (ROIs) pertinent to the CAN, and blood oxygen level-dependent signal was correlated between each ROI as a measure of functional connectivity. HRV was correlated with CAN connectivity (CANconn) for each region, and subgroup analysis was performed based on sex and clinical comorbidities. Results: Forty-seven premature neonates were included in this study, with a mean gestational age at birth of 28.1 +/− 2.6 weeks. Term CANconn was found to be significantly correlated with HRV in approximately one-fifth of CAN connections. Two distinct patterns emerged among these HRV-CANconn relationships. In the first, increased HRV was associated with stronger CANconn of limbic regions. In the second pattern, stronger CANconn at the precuneus was associated with impaired HRV maturation. These patterns were especially pronounced in male premature neonates. Conclusion: We report for the first time evidence of brain-heart relationships in premature neonates and an emerging CAN, most striking in male neonates, suggesting that the brain-heart axis may be more vulnerable in male premature neonates. Signatures in the heart rate may eventually become an important noninvasive tool to identify premature males at highest risk for neurodevelopmental impairment.

Modeling Poverty Data in Indonesia with Spatial Hierarchy Structure Using HLM, GWR, and HGWR Methods

Poverty causes the majority of the Indonesian population to face challenges in fulfilling basic needs such as clothing, food, and shelter. The factors that play a role in determining the poverty rate in Indonesia tend to vary in each province; this is due to the diverse conditions resulting from spatial heterogeneity. However, poverty in Indonesia is not only influenced by factors from various regions but also by the conditions of the districts/cities within them. Districts/cities within a province form a spatial hierarchy structure. Therefore, in this study, the Hierarchical Linear Model (HLM), Geographically Weighted Regression (GWR), and Hierarchical and Geographically Weighted Regression (HGWR) methods were applied to determine the best model among the three methods in analyzing the factors affecting the poverty rate in Indonesia with a spatial hierarchy structure. The results of the analysis show that the HGWR method is the best model compared to HLM and GWR, as evidenced by the higher R-squared value of 0.8004 compared to HLM and GWR. Based on the HGWR model, most of the local estimators for population dependency ratio (G1), adjusted per capita expenditure (G2), and economic growth rate (G3) showed significance in provinces located in eastern Indonesia. In addition, the fixed effects and random effects estimators, namely the percentage of households without access to electricity (X1), the ratio of per capita normative consumption to net product (X2), and the percentage of households without access to clean water (X3), also have a significant influence on the poverty rate in Indonesia.

Insights in the Structural Hierarchy of Statically Crystallized Palm Oil

Palm oil (PO) is still widely used for the production of all types of food products. Due to its triacylglycerol (TG) composition, PO is semisolid at ambient temperature, offering possibilities for many applications. In order to tailor the fat crystal network for certain applications, it remains imperative to understand the structural build-up of the fat crystal network at the full-length scale and to understand the effect of processing conditions. In this study, PO was crystallized under four temperature protocols (fast (FC) or slow (SC) cooling to 20 °C or 25 °C) and was followed for one hour of isothermal time. A broad toolbox was used to fundamentally unravel the structural build-up of the fat crystal network at different length scales. Wide-angle and small-angle X-ray scattering (WAXS and SAXS) showed transitions from α-2L to β’-2L over time. Despite the presence of the same polymorphic form (β’), chain length structure (2L), and domain size, ultra-small-angle X-ray scattering (USAXS) showed clear differences in the mesoscale. For all samples, the lamellar organization was confirmed. Both cooling speed and isothermal temperature were found to affect the size of the crystal nanoplatelets (CNPs), where the highest cooling speed and lowest isothermal temperature (FC and 20 °C) created the smallest CNPs. The microstructure was visualized with polarized light microscopy (PLM) and cryo-scanning electron microscopy (cryo-SEM), showing clear differences in crystallite size, clustering, and network morphology. Raman spectroscopy was applied to confirm differences in triglyceride distribution in the fat crystal network. This study shows that both cooling rate and isothermal temperature affect the fat crystal network formed, especially at the meso- and microscale.

A Health Status Assessment Method of Fluid Loop System Based on Hierarchical Multi-Information Fusion

As a kind of thermal control device, fluid loop systems must operate with the demands of high safety, high reliability, and long life. In order to accurately assess the health status of fluid loop systems, a hierarchical multi-information fusion (HMIF) method is proposed in this paper. Considering that fluid loop systems generally have distinct structural hierarchies, the health evaluation process in this method is divided into three levels, which are the indicator level, the component level, and the system level. In the evaluation process, the health indices are, respectively, constructed to quantify the health status at the three levels. At the indicator level, One-Class support vector machine algorithm is used to obtain the distribution space of each state monitoring indicator under a normal state. The indicator-level health indices are evaluated by calculating the ratio of the data located in the distribution space to the overall data. At the component level, a fuzzy theory is used to calculate the health indices of the component level. Health indices at the indicator level are first converted to membership degree by membership degree function. Then, the evaluation fusion strategy is used to deduce the membership degree of the component level. The health indices at the component level are obtained from the mapping relationship between the membership degree and the health index. At the system level, an adaptive weight adjustment strategy is proposed to fuze all component-level health indices. Taking a practical fluid loop system as an example, health indices at the three levels evaluated by the HMIF method are compared with the actual status. The results indicate that the proposed method can correctly judge the health state of the system and provide a reference for the maintenance and fault diagnosis of fluid loop systems.

Mechanisms underlying the relationship between the intersectionality of multiple social identities with depression among US adults: A population-based study on the mediating roles of lifestyle behaviors

BackgroundDespite substantial efforts to investigate the inequalities in mental health among people with marginalized identities, most research has concentrated on single social identities rather than complete persons. The current study aimed to explore the mechanisms underlying the relationship between the intersectionality of multiple social identities with depression among US adults. MethodsData for this study came from the National Health and Nutrition Examination Survey waves between 2015 and 2018, including a total of 11,268 US adults. A latent class analysis (LCA) was conducted to identify latent subpopulations based on sex, race/ethnicity, immigrant status, disability, household income, employment status, and education level. Interclass differences in lifestyle factors and depression were examined. Multiple mediation analysis was used to examine the mediating roles of lifestyle behaviors. ResultsLCA identified four potential subpopulations: “least marginalized”, “immigrant minorities”, “disabled, less-educated non-workers”, and “low-income minorities” groups. There was enormous heterogeneity in mental health among immigrant minority individuals. The “disabled, less-educated non-workers” group had the highest rates of depression; in contrast, the “immigrant minorities” group had the best mental health, even better than that of the “least marginalized” group. Distributions of lifestyle factors followed a similar pattern. In addition, lifestyle behaviors significantly mediated the relationship between intersectional social identities and depression. LimitationsThe cross-sectional design prevented establishment of the causality of relationships. ConclusionsThis study suggests that applying a person-centered approach is important when examining intersectional inequalities in mental health and highlights the effects of structural social hierarchies on individuals' health behaviors and mental health.

Unfamiliarity generates costly aggression in interspecific avian dominance hierarchies

Dominance hierarchies often form between species, especially at common feeding locations. Yet, relative to work focused on the factors that maintain stable dominance hierarchies within species, large-scale analyses of interspecific dominance hierarchies have been comparatively rare. Given that interspecific behavioral interference mediates access to resources, these dominance hierarchies likely play an important and understudied role in community assembly and behavioral evolution. To test alternative hypotheses about the formation and maintenance of interspecific dominance hierarchies, we employ an large, participatory science generated dataset of displacements observed at feeders in North America in the non-breeding season. Consistent with the hypothesis that agonistic interference can be an adaptive response to exploitative competition, we find that species with similar niches are more likely to engage in costly aggression over resources. Among interacting species, we find broad support for the hypothesis that familiarity (measured as fine-scale habitat overlap) predicts adherence to the structure of the dominance hierarchy and reduces aggression between species. Our findings suggest that the previously documented agonistic hierarchy in North American birds emerges from species-level adaptations and learned behaviors that result in the avoidance of costly aggression.

Certified Registered Nurse Anaesthetists’ and Critical Care Registered Nurses’ perception of knowledge/power in teamwork with Anaesthesiologists in Sweden: a mixed-method study

Efficient teamwork is crucial to provide optimal health care. This paper focuses on teamwork between Anaesthesiologists (ANES), Certified Registered Nurse Anaesthetists’ (CRNA) and Critical Care Registered Nurses (CCRN) working in challenging environments such as the intensive care unit (ICU) and the operating room (OR). Conflicts are common between physicians and nurses, negatively impacting teamwork. Social hierarchies based on professional status and power inequalities between nurses and physicians plays a vital role in influencing teamwork. Foucault was a famous thinker especially known for his reasoning regarding power/knowledge. A Foucauldian perspective was therefore incorporated into this paper and the overall aim was to explore CCRN/CRNA perception of knowledge/power in teamwork with ANES.Methods A mixed-method approach was applied in this study. Data was collected using a web-based questionnaire containing both closed-end and open-ended questions. A total of 289 CCRNs and CRNAs completed the questionnaire. Data analysis was then conducted through five stages as outlined by Onwuebugzie and Teddlie; analysing quantitative data in SPSS 27.0 and qualitative data with a directed content analysis, finally merging data together in ATLAS.ti v.23.Results The result reveals a dissonance between quantitative and qualitative data; quantitative data indicates a well-functioning interdisciplinary teamwork between CCRN/CRNA and ANES - qualitative data highlights that there are several barriers and inequalities between the two groups. Medicine was perceived as superior to nursing, which was reinforced by both social and organisational structures at the ICU and OR.Conclusion Unconscious rules underlying current power structures in the ICU and OR works in favour of the ANES and biomedical paradigm, supporting medical knowledge. To achieve a more equal power distribution between CCRN/CRNAs and ANES, the structural hierarchies between nursing and medicine needs to be addressed. A more equal power balance between the two disciplines can improve teamwork and thereby reduce patient mortality and improve patient outcomes.

The Interconnections of Music Structure, Harmony, Melody, Rhythm, and Predictivity

Repetition and structure have a significant place in music theory, but the structure hierarchy and its influences are often ignored in both music analysis and music generation. In this article, we first describe novel algorithms based on repetition to extract music structure hierarchy from a MIDI data set of popular music and show its effectiveness through evaluation. Then, we introduce new data-driven approaches to estimate and validate structural influences in music. Results show that the automatically detected hierarchical repetition structures reveal significant interactions between structure and harmony, melody, rhythm, and predictivity. Different levels of hierarchy interact differently, providing evidence that structural hierarchy plays an important role in our popular music data set beyond simple notions of repetition or similarity. We further study how musical structure has evolved over decades of popular music writing. Finally, we discuss the importance of this work in highlighting roles that structure can play in music analysis, music similarity, music generation, music evaluation, and other music information retrieval tasks.

Capital and Cohesion: A new perspective on the analysis of mortality differentials

Abstract: Social characteristics related to mortality, such as hierarchy or family structure, are generally represented as a unidimensional scale. This is inconsistent with much of sociological theory, which represents hierarchy as a multidimensional, or partially ordered, scale. We utilize Bourdieu’s tripartite concept of Capital – Economic, Cultural and Social – to conceptualize social hierarchy and construct appropriate scales. We combine these with measures of Household Structure to investigate their relation to male and female aggregate mortality. Using data for Australian small statistical areas (SA2) from the census of 2011, we regressed male and female standardized mortality (SMR) on the scales for Capital and Household Structure, with controls for State/Territory, Remoteness, and Indigeneity of the SA2. We find that Economic and Cultural Capital significantly reduce mortality, while Social Capital has a smaller effect, significant only for males; Family Structure is at least as important as Capital in explaining levels of mortality; Geographic location, namely State/Territory and degree of Remoteness, are significant determinants of mortality risk and Indigenous areas are at a heavy disadvantage, even when we account for all other social and situational characteristics. We conclude that social space, as measured by scales of social hierarchy and family structure, is multidimensional. To understand fully why mortality is higher in some areas than in others, we need to bring together theory and data.

Nickel-based superalloy architectures with surface mechanical attrition treatment: Compressive properties and collapse behaviour

Surface modifications can introduce natural gradients or structural hierarchy into human-made microlattices, making them simultaneously strong and tough. Herein, we describe our investigations of the mechanical properties and the underlying mechanisms of additively manufactured nickel–chromium superalloy (IN625) microlattices after surface mechanical attrition treatment (SMAT). Our results demonstrated that SMAT increased the yielding strength of these microlattices by more than 64.71% and also triggered a transition in their mechanical behaviour. Two primary failure modes were distinguished: weak global deformation, and layer-by-layer collapse, with the latter enhanced by SMAT. The significantly improved mechanical performance was attributable to the ultrafine and hard graded-nanograin layer induced by SMAT, which effectively leveraged the material and structural effects. These results were further validated by finite element analysis. This work provides insight into collapse behaviour and should facilitate the design of ultralight yet buckling-resistant cellular materials.

Structural, optical and magnetic properties of a new metal-organic CoII-based complex.

A mononuclear cobalt(ii) complex [C5H8N3]2[CoCl4(C5H7N3)2] (I) was synthesized and structurally characterized. Single crystal X-ray diffraction analysis indicates that monometallic Co(ii) ions acted as coordination nodes in a distorted octahedral geometry, giving rise to a supramolecular architecture. The latter is made up of a ½ unit form composed of an anionic element [Co0.5Cl2(C5H7N3)]- and one 2-amino-4-methylpyrimidinium cation [C5H8N3]+. The crystalline arrangement of this compound adopts the sandwich form where inorganic parts are sandwiched between the organic sheets following the [100] direction. More information regarding the structure hierarchy has been supplied based on Hirshfeld surface analysis; the X⋯H (X = N, Cl) interactions play a crucial role in stabilizing the self-assembly process of I, complemented by the intervention of π⋯π electrostatic interaction created between organic entities. Thermal analyses were carried out to study the thermal behavior process. Static magnetic measurements and ab initio calculations of compound I revealed the easy-axis anisotropy character of the central Co(ii) ion. Two-channel field-induced slow-magnetic relaxation was observed; the high-frequency channel is characterized by underbarrier relaxation with U eff = 16.5 cm-1, and the low-frequency channel involves a direct relaxation process affected by the phonon-bottleneck effect.

РАЗВИТИЕ ФИНАНСОВОГО МЕНЕДЖМЕНТА ОРГАНИЗАЦИЙ В КОНТЕКСТЕ ИНТЕГРАЦИИ ФАКТОРОВ ЭНДОГЕННОЙ И ЭКЗОГЕННОЙ СРЕДЫ

The scientific article proposes a scientific idea of the development of financial management of organizations under the mutual influence of endogenous and exogenous factors. The endogenous environment that influences the development of corporate finance is interpreted as the prevailing internal organizational, economic and financial conditions for the functioning of organizations associated with a certain state of objects of financial and non-financial management, the hierarchy of the business structure, as well as the peculiarities of interaction with management entities of different levels. Factors of the exogenous environment that affect the development of financial conditions for the activities of organizations are considered in the context of factors of direct and indirect impact.

Progressive brain structural abnormality in cerebral small vessel disease assessed with MR imaging by using causal network analysis

AimsCerebral small vessel disease (CSVD) is a complex condition characterized by a combination of microcirculation disorders and neurodegenerative processes, CSVD is associated with structural abnormalities in multiple brain regions. However, the progressive pattern of structural changes remains unknown. MethodsIn order to detail the progressive structural changes in CSVD patients according to the degree of cognitive impairment, we recruited 121 CSVD patients and 104 healthy controls (HCs). Voxel-based morphometry was employed to measure the gray matter volume (GMV) of each participant. According to the VICCCS-2 diagnostic criteria, patients were initially divided into three stage groups, then we investigated the GMV changes in each stage and their causal relationships using causal structure covariance network (CaSCN) analysis. ResultsOverall, patients with CSVD presented stage-specific GMV alterations compared with HCs. With the worsening of cognitive impairment, the decrease in gray matter volume starts from the right hippocampus and gradually spreads to the cortical-subcortical brain regions. Importantly, the right hippocampus in CSVD patients plays a driving role in the directional network and forms both positive and negative causal effect networks with cortical-subcortical brain regions. ConclusionsThis study reveals the significance of the right hippocampus as an early pathological area in CSVD patients and its causal impact on brain GMV changes with disease progression, shedding light on structural brain damage hierarchy and compensatory mechanisms.

Enzyme-mimic catalytic activities and biomedical applications of noble metal nanoclusters.

Noble metal (e.g., Au and Ag) nanoclusters (NCs), which exhibit structural complexity and hierarchy comparable to those of natural proteins, have been increasingly pursued in artificial enzyme research. The protein-like structure of metal NCs not only ensures enzyme-mimic catalytic activity, including peroxidase-, catalase-, and superoxide dismutase-mimic activities, but also affords an unprecedented opportunity to correlate the catalytic performance with the cluster structure at the molecular or atomic levels. In this review, we aim to summarize the recent progress in programming and demystify the enzyme-mimic catalytic activity of metal NCs, presenting the state-of-the-art understandings of the structure-property relationship of metal NC-based artificial enzymes. By leveraging on a concise anatomy of the hierarchical structure of noble metal NCs, we manage to unravel the structural origin of the catalytic performance of metal NCs. Noteworthily, it has been proven that the surface ligands and metal-ligand interface of metal NCs are instrumental in influencing enzyme-mimic catalytic activities. In addition to the structure-property correlation, we also discuss the synthetic methodologies feasible to tailoring the cluster structure at the atomic level. Prior to the closure of this review with our perspectives in noble metal NC-based artificial enzymes, we also exemplify the biomedical applications based on the enzyme-mimic catalysis of metal NCs with the theranostics of kidney injury, brain inflammation, and tumors. The fundamental and methodological advancements delineated in this review would be conducive to further development of metal NCs as an alternative family of artificial enzymes.

Decoding startup failures in Indian startups: Insights from Interpretive Structural Modeling and Cross-Impact Matrix Multiplication Applied to Classification

PURPOSE: Start-ups are widely acknowledged as crucial catalysts for innovation and drivers of economic progress. However, their vulnerability to failure continues to pose a persistent and significant obstacle. In light of this, the study intends to ascertain the various elements responsible for the elevated incidence of start-up failures and examine their contextual associations. It further aims to establish the hierarchical structure and identify the crucial factors of start-up failure. METHODOLOGY: The paper uses the Interpretive Structural Modeling (ISM) approach to determine the structural hierarchy and interconnections among the causes of start-up failures identified through the comprehensive analysis of existing literature and experts’ opinions. MICMAC (Cross-Impact Matrix Multiplication Applied to Classification) analysis is also being utilized to categorize these identified failure causes into autonomous, independent, dependent, and linking factors by their driving and dependency powers. FINDINGS: A structural framework depicting the interrelationships among the factors has been derived, showing the failure factor, ‘poor market positioning’ factor at the highest level, and the ‘lack of entrepreneurial efficiency’ at the lowest level of the model. The results also revealed that lack of entrepreneurial efficiency, poor management, and external environmental issues are the most significant independent factors upon which all other failure factors rely. It also categorizes ‘poor market positioning’ as the dependent factor, signifying its passive role in the failure of start-ups. IMPLICATIONS: As previous literature has discussed the various factors responsible for the failure of start-ups in isolation, the current study fills out the gap in the literature by establishing linkages among those factors. The study’s insights emphasize the value of effective management teams and entrepreneurial skills in averting start-up failures. It highlights the importance of skill development and mentorship to enhance the capabilities of entrepreneurs and their teams. Furthermore, the research indicates that policymakers and support groups can create focus initiatives addressing issues like market validation, team dynamics, and financial management to enhance the start-up environment. These initiatives may encompass entrepreneurship training, financial assistance, and mentorship through the ‘Start-up India’ Program, Bharat Fund platform, etc. ORIGINALITY AND VALUE: Previous studies on entrepreneurial failure are based on AHP (Analytical Hierarchical Process), content analysis, and quality management methodologies. This is potentially the first study using the ISM-MICMAC approach that explores the complex world of start-up failures in India and illustrates the relative influence and interdependence of various failure factors of start-ups through a hierarchical model.

Research on the Hierarchical Structure of User Feature Factors Affecting the Accurate Recommendation of Digital Educational Resources

In order to analyze the user characteristic factors that affect the accurate recommendation of digital educational resources, optimize the user model and improve the accuracy of resource recommendation. In this paper, the interpretive structure Model (ISM) technology is used to clarify the logical relations among 13 key feature factors and establish the interpretive structure hierarchy model. Then the MICMAC model is used to analyze the dependence and driving force of each characteristic factor to determine its influence degree. The results show that user resource needs, resource preference and learning style are the direct factors affecting digital educational resource recommendation service. Individual attribute, learning motivation, social attribute, interactive preference, learning emotion and learning attitude are the core factors that affect the recommendation of digital educational resources. Information literacy, knowledge level, cognitive structure and learning input are the source factors that affect the recommendation of digital educational resources. Combining ISM analysis and MICMAC classification, the stability and driving force of the model level are gradually enhanced from the surface layer to the deep layer, and the dependence is gradually weakened.

An improved multi-stage framework for large-scale hierarchical text classification problems using a modified feature hashing and bi-filtering strategy

The classification of large-scale textual dataset is associated with a huge number of instances and millions of features which must be discriminated between large numbers of categories. The task requires the utilization of a defined hierarchy structure and tools that automatically classify instances within the hierarchy known as Large Scale Hierarchical Text Classification (LSHTC). Predicting the labels of instances by the employed classifiers is challenging due to the high number of features. Furthermore, the existing Dimensional Reduction (DR) approaches in cooperation with the LSHTC framework are still quite inefficient. In such a problem, an effective Hierarchical Dimensional Reduction approach can be advantageous in improving the performance of the LSHTC. Therefore, in this paper, we enhance the performance of LSHTC by proposing a Multi-stage Hierarchical Dimensional Reduction (MHDR) approach based on Modified Feature Hashing (MFH) and Hierarchical Bi-Filtering (HBF) method. In addition to alleviating bad collision and result discrepancy, experimental results show that the proposed approach has achieve the best performance in terms of micro-f1 and macro-f1 by recording average scores of 58.47% and 54.77% using TD-SVM, and average scores of 51.14% and 48.70% using TD-LR, respectively. The method also achieved 11% speed-up than the approaches compared.

A Novel Similarity-Based Link Prediction Approach for Transaction Networks

A network consists of nodes and links, which represent components of a system and interactions between them, respectively. An example of networks is transaction networks, in which nodes and links represent firms and transactions, respectively. Link prediction in transaction networks is an important problem, which aims to estimate the likelihood of a transaction between two firms. It can be used to predict missing link information between firms to gain fuller knowledge as transaction information is not easily accessible between firms. In addition, firms can use it to predict future transactions as transactions evolve over time for various reasons such as a shift in customer demand, resource availability, etc. Many link prediction methods have been proposed for networks. However, to the best of our knowledge, there is no existing link prediction method for transaction networks. Existing methods are not suitable for transaction networks as they assume homophily, the tendency of individuals to associate with similar others, which may not be true in transaction networks. In addition, they do not consider the hierarchy structure exhibited in transaction networks. In this article, we propose a new similarity score for transaction networks that account for multiple, temporal, and directed transactions. We then propose a link prediction procedure based on the proposed similarity score to predict new transactions in transaction networks, which avoids the homophily assumption and exploits the hierarchical structure of transaction networks. The proposed method is tested on real-world transaction networks and yields better area under the receiver operating characteristic curve compared to existing methods.

Micro-nano-engineered slippery liquid-infused porous surface coating with highly sustainable superhydrophobicity and omniphobicity

Omniphobic coatings designed to repel liquid contamination off the surface can be used in various industries such as automotive, constructions, oil-water separation, anti-fouling marine devices, etc. Various methods have been investigated in the past decade to achieve omniphobicity in which majority of them have proven to be either complex, expensive, or impractical for large scale production. This study has employed a simple three-step, micro-nano-structuring fabrication technique to create physico-chemically and thermo-mechanically robust slippery liquid-infused porous surfaces (SLIPS). Polydimethylsiloxane (PDMS) was used as a base resin engineered with chemical bonding to ZnO micro particles and further physical trapping to hydrophobized TiO2 nano particles to create a hierarchy structure able to repel water and create omniphobicity. With very low contact angle hysteresis (CAH) of <1° and a low tilting angle of <5° for various liquid test subjects, the final coating displays excellent hydrophobicity and omniphobicity in spite of going through harsh mechanical tests such as the cross-hatch and scotch tape tests, pressure tests up to 100 N (500 kPa), thermal stability tests by exposure to low and high temperatures (−18 °C to 120 °C), and chemical pH tests from 1 to 13. We believe that the simple and low-cost micro-nano-engineering approach can be easily scaled up and is suitable for a variety of applications for self-cleaning and hydrophobic purposes.

Tennis performance priorities for all-court player using the Analytic Hierarchy Process method

Purpose: Above all, a wealth of research into the game of tennis has failed to provide an answer to the question of how to measure the overall performance of a top tennis player under competitive conditions. The aim of this study was to investigate the priorities for all-court tennis players on offence and defence. Methods: The relative importance coefficients related to the style of all-court tennis players were determined for eighteen criteria for evaluating overall performance based on the expertise conducted by seven tennis experts. In this study, the priorities for “all-court” style were analysed using the Analytic Hierarchy Process method. From each given matrix, the vectors of importance coefficients defined by each of the experts were calculated using the geometric mean method and used to form a matrix of importance coefficients for an all-court player. The vectors of the arithmetic mean and standard deviations were calculated. Results: The top of the defensive hierarchical structure indicates that the quality of movement/defensive tasks and the quality of the first serve-return have a high importance; the quality of performance in long rallies has a medium to high importance. The top of the offensive hierarchy structure indicates that playing with multiple styles and the quality of the first serve have a high importance; the quality of taking the initiative in rallies and the quality of the offensive forehand have a medium to high importance. Conclusions: The results of this study can be used by coaches to select the optimal technical and tactical solutions.