Higher Fitness Research Articles

Preaching to the choir: Do green influencers make a difference?

Green influencers are crucial in promoting sustainable lifestyles, but their impact may vary depending on their audience. Their behavioral effect might be limited if they were mainly “preaching to the choir” of like-minded green followers who likely already consume sustainably. The purpose of this research is to (1) investigate the communication practices of green influencers and their followers as an indicator of their green attitudes (and a potential influencer-follower attitude fit) and (2) understand the effect of consumer green attitudes on their response to green influencer messages. Field data reveal the high influencer-follower fit in green attitudes in that they mostly talk about green topics. Two experiments with real and fictional influencers show the negative implications of this attitude fit: the effect of green influencer posts on consumers’ usage intention is weaker when these consumers have similar green attitudes as the influencers (fit), whereas the effect is stronger when these posts reach a non-green audience (non-fit). This implies that green influencers should focus on reaching a non-green audience when promoting green consumption; alternatively, green behavioral intentions might increase through sustainable messages from non-green (e.g., beauty) influencers.

Clustered Cardiometabolic Risk and the "Fat but Fit Paradox" in Adolescents: Cross-Sectional Study.

The "fat but fit paradox" states that people who are fit have a lower cluster cardiometabolic risk (CCMR), even if they are overweight or obese. Therefore, the objective was to investigate the CCMR between four categories based on the "fat but fit paradox" variable, in different fitness categories-cardiorespiratory fitness, muscular fitness, and physical fitness-in adolescents. Body composition, cardiorespiratory fitness, muscle fitness, blood samples, and blood pressure were assessed in 230 adolescents, and cardiometabolic risk and three different "fat but fit paradox" variables were calculated. Participants with a higher CRF exhibited a lower CCMR within their body mass index (BMI) category (p < 0.05). Participants with a high BMI and high muscular fitness showed a lower CCMR than participants with a low muscular fitness and a similar BMI, or low BMI and low muscular fitness (p < 0.05). When both variables, CRF and muscular fitness, were combined, their effectabove CCMR increased (p < 0.05). Across all fitness categories, the fat and unfit group, whether considered individually or combined, exhibited the highest risk of CCMR (p < 0.05). This study confirms the "fat but fit paradox" in different physical fitness categories, showing the importance of both CRF and muscular fitness as predictors of CCMR, with the combination of both variables showing a greater agreement.

Forecasting first-year student mobility using explainable machine learning techniques

In the context of regional sciences and migration studies, gravity and radiation models are typically used to estimate human spatial mobility of all kinds. These formal models are incorporated as part of regression models along with co-variates, to better represent regional specific aspects. Often, the correlations between dependent and independent variables are of non-linear type and follow complex spatial interactions and multicollinearity. To address some of the model-related obstacles and to arrive at better predictions, we introduce machine learning algorithm class XGBoost to the estimation of spatial interactions and provide useful statistics and visual representations for the model evaluation and the evaluation and interpretation of the independent variables. The methods suggested are used to study the case of the spatial mobility of high-school graduates to the enrolment in higher education institutions in Germany at the county-level. We show that machine learning techniques can deliver explainable results that compare to traditional regression modeling. In addition to typically high model fits, variable-based indicators such as the Shapley Additive Explanations value (SHAP) provide significant additional information on the differentiated and non-linear effect of the variable values. For instance, we provide evidence that the initial study location choice is not related to the quality of local labor-markets in general, as there are both, strong positive and strong negative effects of the local academic employment rates on the migration decision. When controlling for about 28 co-variates, the attractiveness of the study location itself is the most important single factor of influence, followed by the classical distance-related variables travel time (gravitation) and regional opportunities (radiation). We show that machine learning methods can be transparent, interpretable, and explainable, when employed with adequate domain-knowledge and flanked by additional calculations and visualizations related to the model evaluation.

Microbiome features associated with performance measures in athletic and non-athletic individuals: A case-control study.

The influence of human gut microbiota on health and disease is now commonly appreciated. Therefore, it is not surprising that microbiome research has found interest in the sports community, hoping to improve health and optimize performance. Comparative studies found new species or pathways that were more enriched in elites than sedentary controls. In addition, sport-specific and performance-level-specific microbiome features have been identified. However, the results remain inconclusive and indicate the need for further assessment. In this case-control study, we tested two athletic populations (i.e. strength athletes, endurance athletes) and a non-athletic, but physically active, control group across two acute exercise bouts, separated by a 2-week period, that measured explosive and high intensity fitness level (repeated 30-s all-out Wingate test (WT)) and cardiorespiratory fitness level (Bruce Treadmill Test). While we did not identify any group differences in alpha and beta diversity or significant differential abundance of microbiome components at baseline, one-third of the species identified were unique to each group. Longitudinal sample (pre- and post-exercise) analysis revealed an abundance of Alistipes communis in the strength group during the WT and 88 species with notable between-group differences during the Bruce Test. SparCC recognized Bifidobacterium longum and Bifidobacterium adolescentis, short-chain fatty acid producers with probiotic properties, species strongly associated with VO2max. Ultimately, we identified several taxa with different baseline abundances and longitudinal changes when comparing individuals based on their VO2max, average power, and maximal power parameters. Our results confirmed that the health status of individuals are consistent with assumptions about microbiome health. Furthermore, our findings indicate that microbiome features are associated with better performance previously identified in elite athletes.

Insilico screening to identify novel inhibitors targeting 30S ribosomal protein S12 in meningitis-causing organism ‘Elizabethkingia meningoseptica’

The current trend in biomedical research is on prioritizing infections based on multidrug resistance. Elizabethkingia meningoseptica, a nosocomial infection-causing organism emerging from Neonatal Intensive Care Units (NICUs), leads to neonatal meningitis and sepsis resulting in severe illness, and, in some cases, fatal. Finding a solution remains challenging due to limited prior work. Translational S12 ribosomal proteins play a crucial role in decoding the codon-anticodon helix, which is essential for the survival of E. meningoseptica. These proteins do not exhibit significant similarity with humans, making them potential drug targets. An in silico study aims to identify specific inhibitors for E. meningoseptica ribosomal proteins among known bioactive compounds targeting prokaryotic 30S ribosomal protein. A 3D model of the 7JIL_h protein from Flavobacterium johnsoniae, showing 90% sequence similarity with the target protein was generated using SWISS-MODEL software. The model was validated through Molprobity v4.4, VERIFY 3D, Errata, and ProSA analysis, confirming conserved residues of the target protein. Insilico screening of known bioactive compounds and their analogs identified potential ligands for the target protein. Molecular Docking and post-docking analysis assessed the stability of the protein-ligand complexes among the shortlisted compounds. The top two compounds with high Gold fitness scores and low predicted binding energy underwent MD simulation and further estimation of free binding energy using the MM_PBSA module. These computationally shortlisted compounds, namely chEMBL 1323619 and chEMBL 312490 may be considered for future in-vivo studies as potential inhibitors against the modeled 30S ribosomal protein S12 of E. meningoseptica. Communicated by Ramaswamy H. Sarma

Assessing The General Sentiment of Malaysian Manufacturers in Adopting Blockchain Technology to Improve Supply Chain Processes

The popularity of blockchain technology has seen a dramatic increase throughout the year of 2021 and 2022, with numerous use cases in various global industries. This includes the use of cryptocurrencies, non-fungible tokens, decentralized autonomous organizations, and others. In Malaysia, there is also an apparent increase in general awareness towards the technology, with Bank Negara Malaysia and Securities Commission actively participating in the Committee for Blockchain and Distributed Ledger Technology. This study specifically examined at the Malaysian manufacturing industry, attempting to assess their general sentiments towards blockchain technology and how it could help to improve various supply chain processes. The quantitative study received 114 valid responses from management level employees with Malaysian manufacturers, enabling the researchers to assess the task technology fit, perceived ease of use (PEOU), perceived usefulness (PUSE), and technology readiness index (TRI). The analysis indicated that there is indeed a high task technology fit in the Malaysian manufacturing sector. In addition, this industry showed a positive sentiment towards blockchain technology, with high scores in PEOU, PUSE, and TRI. The high TRI numbers also had a positive significant relationship on the decision to adopt this technology within the manufacturing companies to improve various supply chain processes.

Vascular Responses to Acute Induced Inflammation With Aging: Does Fitness Matter?

Acute inflammation impairs vascular function in an age-dependent manner and affects cardiovascular event risk. Regular aerobic exercise preserves vascular function with aging and potentially modifies how acute inflammation affects the vasculature. We hypothesize high cardiorespiratory fitness may accompany greater arterial responsiveness post-acute inflammation in older adults.

TDFPI: A Three-Dimensional and Full Parameter Inversion Model and Its Application for Building Damage Assessment in Guotun Coal Mining Areas, Shandong, China

Subsidence prediction is essential for preventing and controlling geohazards in coal mining areas. However, the Interferometric Synthetic Aperture Radar (InSAR) technique is limited in deriving the goaf displacements with a large gradient and fast deformation rates, hindering its application for potential risk evaluation over the mining areas. In this study, we proposed a three-dimensional and full parameter inversion (TDFPI) model to derive the large-gradient subsidence and then investigate its application for building damage assessment over coal mining areas. By taking the Guotun coal mine as the case study, the TDFPI model was demonstrated to have effectively predicted the large-gradient deformation of the mining areas and successfully evaluated the house damage in Chelou village, which agrees well with our field investigations. Specifically, the predicted subsidence results were validated with high fitting accuracy against field measurements, with RMSE of 0.083 m and 0.102 m, respectively, on observation line A and line F. In addition, the classified damage levels are highly consistent with in situ field surveys for the house cracks in Chelou village, presenting its practicality and effectiveness for building damage evaluation, and thus can provide a useful tool for potential risk assessment and prevention over the mining areas.

Comparison study between as-synthesized ZnO and ZnO derived from ZiF-8 metalorganic framework in removing methylene blue

The nanoporous materials derived from the ZiF-8 metalorganic framework are highly interesting because of their features like good thermal stability, catalytic performance, and electric conductivity. Therefore, ZiF-derived metal oxide was utilized in several applications, for instance, energy storage and environmental remediations. Herin, we compare the as-prepared ZnO, ZiF-8, and ZnO derived from ZiF-8 in terms of structural, thermal, and morphological properties. Thus, the photocatalytic performance of all compositions. The preparation process is an effective parameter in the morphology of the sample surface. The as-synthesized ZnO had nanoparticle morphology with a size of 50–80[Formula: see text]nm. However, the ZiF-8 has cubic morphology with a long rod. The decomposition of ZiF-8 converted the sample to ZnO coated by carbon, indicating a high surface and fit morphology for photocatalysis. These results mean that ZnO derivative from ZiF-8 nanoparticles has the best efficiency for water desalination with 95%. ZnO drives from ZiF-8 demonstrated the smallest charge transfer resistance compared to that of as-prepared ZnO, and ZIF-8, coinciding with the conclusion of the EIS results. The transient photoresponses and electrochemical impedance spectra of the compounds were used further to explore the separation efficiency of the photogenerated charge carriers.

Modeling the behavior of monoclonal antibodies on hydrophobic interaction chromatography resins

Monoclonal antibodies (mAbs) require a high level of purity for regulatory approval and safe administration. High-molecular weight (HMW) species are a common impurity associated with mAb therapies. Hydrophobic interaction chromatography (HIC) resins are often used to remove these HMW impurities. Determination of a suitable HIC resin can be a time and resource-intensive process. In this study, we modeled the chromatographic behavior of seven mAbs across 13 HIC resins using measurements of surface hydrophobicity, surface charge, and thermal stability for mAbs, and hydrophobicity and zeta-potential for HIC resins with high fit quality (adjusted R2 > 0.80). We identified zeta-potential as a novel key modeling parameter. When using these models to select a HIC resin for HMW clearance of a test mAb, we were able to achieve 60% HMW clearance and 89% recovery. These models can be used to expedite the downstream process development for mAbs in an industry setting.Graphical

Study on the Influence and Optimization Design of Viscous Damper Parameters on the Damping Efficiency of Frame Shear Wall Structure

This study investigates the influence of viscous damper parameters on the damping efficiency of frame shear wall structures. Taking a specific frame shear wall structure as the background, a three-dimensional finite element model is established using a nonlinear dynamic time–history analysis method. The damping ratio, reduction in vertex displacement, reduction in base shear, and inter-story drift utilization rate are selected as the damping performance indicators. Firstly, a sensitivity analysis is conducted to study the influence of different viscous damper parameters on these indicators. Then, the relationship models between the viscous damper parameters and the indicators are fitted using the response surface method, and the fitting effect is evaluated through an F-test and determination coefficient R2. Finally, an objective function based on key damping performance indicators is established to solve for the optimal parameters. The results show that the traditional sensitivity analysis method is unable to comprehensively consider the combined effects of different damping efficiency indicators. The response surface method has high fitting accuracy and good predictability and can serve as an optimization model. Considering the stiffness of supporting components matched with the viscous damper parameters, the feasibility of the optimal damping parameters is demonstrated from an engineering application perspective. A simple and easy-to-operate damping design flowchart is proposed, providing important guidance and reference for designers in frame shear wall structure damping design in the future.

Evaluation of physical fitness and health of young children aged between 3 and 6 based on cluster and factor analyses

BackgroundAs life improves and sedentary time increases, young children's physical fitness gradually declines.MethodsMulti-stage stratified whole cluster sampling was utilized to sample 5584 preschoolers. Young infants' morphology, function, and quality were revealed using cluster and factor analysis.ResultsThe cluster analysis separated 3–6-year-olds into two genders: 1,551 men in group A "high physical fitness" 1,499 men in group B "low physical fitness"; 1,213 women in group A and 1,321 women in group B. Young children's fitness was measured by standing long jump(1.00), weight(1.00), and height(1.00). A cluster analysis of 3–4-year-olds classified them into three groups: 272 “muscular strength,” 75 “average physical fitness,” and 250 “low agility.” Young children's health depends on weight (1.00), height (0.57), and chest circumference (0.54). A cluster analysis of the 4–5-year-olds classified them into two groups: 1070 “balance” and 806 “muscular strength.” Young children’s health depends on weight (1.00), height (0.74), and chest circumference (0.71). A cluster analysis of the 5–6-year-olds divided them into three groups: 1762 “high physical fitness,” 384 “obese,” and 105 “low physical fitness.” Young children’s physical health depends on BMI (1.00), weight (1.00), and chest circumference (1.00). Factor analysis demonstrated that muscle strength, body shape, cardiovascular variables, and physical fitness composite components affected young children's health.ConclusionWomen should focus on motor function and strength, while men on flexibility. Male group B “low physical fitness” should focus on strength, motor function, and balance, whereas male group A “high physical fitness” should focus on flexibility. Then, female group A “high physical fitness” should emphasize variety.2) For 3–4-year-olds, group A “muscular strength” should focus on flexibility, and group C “low agility” on motor function. 3) For 4–5-year-olds, group A “balanced” should focus on strength and motor function; 4) For 5–6-year-olds, group B “obese” should emphasize weight loss, and group C “low fitness” should emphasize strength, motor function, and flexibility; 5) Young children’s physical fitness depends on muscle strength, body shape, cardiovascular factors, and physical fitness composite.

Volleyball training and practice: vertical jump and agility tests

This study aimed to research peculiarities of improving high jump techniques training in modern volleyball and developing agility amongst sportsmen in the context of attributive components, that influence the success of team competition plays. The methodological basis of this study consists of methods of systematic analysis of key components in distributing training loads amongst volleyball team members on a stage of pre-season training with complex research of various aspects of jumping ability and agility of sportsmen tests employment and their practical use on the stage of preparing sportsmen in the competitive period as well. The results of the study showed the high practical importance of testing the real jumping and agility characteristics of volleyball players and show prospects for the practical use of the test system for these characteristics at the training level of athletes in order to achieve high fitness of sports commands and successful games in monthly competitions. The practical benefit of these results obtained during the research lies in the ability to use them in the training process of modern volleyball teams to develop the agility and jumping abilities of athletes, which has prospects for higher results of sports teams in competitions.

Compensatory mutations are associated with increased in vitro growth in resistant clinical samples of Mycobacterium tuberculosis.

Mutations in Mycobacterium tuberculosis associated with resistance to antibiotics often come with a fitness cost for the bacteria. Resistance to the first-line drug rifampicin leads to lower competitive fitness of M. tuberculosis populations when compared to susceptible populations. This fitness cost, introduced by resistance mutations in the RNA polymerase, can be alleviated by compensatory mutations (CMs) in other regions of the affected protein. CMs are of particular interest clinically since they could lock in resistance mutations, encouraging the spread of resistant strains worldwide. Here, we report the statistical inference of a comprehensive set of CMs in the RNA polymerase of M. tuberculosis, using over 70 000 M. tuberculosis genomes that were collated as part of the CRyPTIC project. The unprecedented size of this data set gave the statistical tests more power to investigate the association of putative CMs with resistance-conferring mutations. Overall, we propose 51 high-confidence CMs by means of statistical association testing and suggest hypotheses for how they exert their compensatory mechanism by mapping them onto the protein structure. In addition, we were able to show an association of CMs with higher in vitro growth densities, and hence presumably with higher fitness, in resistant samples in the more virulent M. tuberculosis lineage 2. Our results suggest the association of CM presence with significantly higher in vitro growth than for wild-type samples, although this association is confounded with lineage and sub-lineage affiliation. Our findings emphasize the integral role of CMs and lineage affiliation in resistance spread and increases the urgency of antibiotic stewardship, which implies accurate, cheap and widely accessible diagnostics for M. tuberculosis infections to not only improve patient outcomes but also prevent the spread of resistant strains.

Waveform design through the trade-off relationship between the MI criterion and the SINR criterion

Aiming at the diversity requirements of cognitive radar monitoring tasks, a joint optimization design criterion that comprehensively considers the mutual information (MI) and signal-to-interference-to-noise ratio (SINR) between the target and the echo is proposed. In view of the challenges brought by the traditional water-filling algorithm, this paper further studies how to effectively solve the new optimization criteria to improve the overall performance of the system. Specifically, this paper proposes a PCMA-ES algorithm that combines an adaptive penalty function with the Covariance Matrix Adaptive Evolutionary Strategy (CMA-ES) algorithm. The penalty function aims to prioritize feasible solutions by assigning them the highest fitness. For infeasible solutions with lower constraint violations, the fitness is slightly lower, allowing for better utilization of information from infeasible solutions. The simulation results show that the PCMA-ES algorithm has lower time complexity or better performance than the traditional water-filling algorithm, and can solve more complex transmission waveforms. In addition, the waveform designed with a joint optimization criterion outperforms that based on a single optimization criterion. The radar detection focus can be adjusted to meet the specific requirements of diverse detection tasks.

Anisotropic dynamic permeability model for porous media

Based on the tortuous capillary network model, the relationship between anisotropic permeability and rock normal strain, namely the anisotropic dynamic permeability model (ADPM), was derived and established. The model was verified using pore-scale flow simulation. The uniaxial strain process was calculated and the main factors affecting permeability changes in different directions in the deformation process were analyzed. In the process of uniaxial strain during the exploitation of layered oil and gas reservoirs, the effect of effective surface porosity on the permeability in all directions is consistent. With the decrease of effective surface porosity, the sensitivity of permeability to strain increases. The sensitivity of the permeability perpendicular to the direction of compression to the strain decreases with the increase of the tortuosity, while the sensitivity of the permeability in the direction of compression to the strain increases with the increase of the tortuosity. For layered reservoirs with the same initial tortuosity in all directions, the tortuosity plays a decisive role in the relative relationship between the variations of permeability in all directions during pressure drop. When the tortuosity is less than 1.6, the decrease rate of horizontal permeability is higher than that of vertical permeability, while the opposite is true when the tortuosity is greater than 1.6. This phenomenon cannot be represented by traditional dynamic permeability model. After the verification by experimental data of pore-scale simulation, the new model has high fitting accuracy and can effectively characterize the effects of deformation in different directions on the permeability in all directions.

Offspring sex ratio increases with paternal reproductive success in a colony of southern elephant seals

AbstractIn polygynous species, male reproductive success is often determined by their ability to dominate female harems. Harem‐holders sire a disproportionate number of offspring. Male dominance tends to correlate with age, but intense competition and early male mortality limit most males from achieving high social status. To maximize reproductive success despite low rank, offspring sex ratio adjustment may have evolved, favoring the sex with higher fitness. If traits influencing dominance are heritable and confer reproductive advantages, we expect fathers with higher reproductive success to produce more sons, as they are more likely to become dominant. In contrast, subordinate males with lower success may benefit from siring more daughters. We tested this hypothesis on a colony of southern elephant seals breeding on the Kerguelen Archipelago. We used genetic markers to link the paternity of 540 pups to 58 breeding males. We found that the probability of siring a son increases from 43% to 54% with paternal relative reproductive success, supporting the offspring sex ratio adjustment hypothesis. Given that various factors influence sex ratio in a population, future studies should tease apart these ecological processes (e.g., paternal dominance, maternal condition, local density, or adult sex ratio) and investigate how they interact with each other.

Research On Emotion Management Based on Speech Analysis for Nursing Homes

Speech emotion recognition is an important research area in artificial intelligence aimed at identifying the emotional states of speakers. This paper provides an overview of the current state and key algorithms of speech emotion recognition, focusing particularly on the emotional well-being of elderly residents in nursing homes. Firstly, the paper introduces the background and application areas of speech emotion recognition, emphasizing its significance in human-computer interaction, psychological health monitoring, and emotional intelligent systems. The paper extensively discusses methods of extracting emotional features, it adopts the K-Nearest Neighbors (KNN) classification algorithm, which serves as a simple yet effective classification method with potential in emotional speech recognition. This study aims to explore the design, implementation, and future directions of an emotion recognition system based on KNN. This algorithm exhibits a high fitting performance for emotional speech data, presenting advantages such as ease of implementation and alignment with the distribution characteristics of emotional speech data. Through multiple comparative experiments, the optimal K value for recognition has been determined, achieving a straightforward and convenient emotional recognition simulation. Finally, the paper summarizes the current state of speech emotion recognition, emphasizing its potential and significance in practical applications.

Organizational Justice and Work Motivation: The Perspective of Civil Servant Groups in Banten and Central Java Provinces

The purpose of this study was to uncover the effect of organizational justice (procedural justice, distributive justice, and interactional justice) on the work motivation of BAPENDA employees in Banten and Central Java Provinces. The data were collected using a survey with a purposive sampling technique. The sample size of the study was 587 persons, which involved 200 civil servants at Central Java Province BAPENDA and 387 civil servants at Banten Province BAPENDA. The data were analyzed using multigroup Structural Equation Modeling (SEM). The results of the study showed that organizational justice (distributive, procedural, and interactional justices) can explain and predict the work motivation in the cultural group background of Banten and Central Java. The involvement of the two different cultural groups in this study indicated that the analysis supports the high model fit. In other words, the psychometric properties of the model measurements was not disturbed by the group presence, aka disturbed by the deceptive response.

Optimization Of Replenishment and Sales Strategies of Vegetable Commodities Based on Multiple Linear Regression and Non-Linear Programming

Automated pricing of vegetable items generates suggested selling prices for the items based on the provided item data, considering the structure of the data. The aim of this paper is to better help superstores plan future replenishment volumes and pricing strategies by analysing sales data for each category and individual item in the vegetable category. Specifically, this paper takes the category as a unit, uses the processed cost-plus pricing, sales volume, purchase price, and loss rate data of each vegetable category to establish a linear regression model, and based on the least squares method, the relationship between cost-plus pricing and total sales volume of each vegetable category is fitted to solve the problem, and the multivariate linear relationship expression with high fit is obtained; based on the cost-plus pricing rule, the future replenishment volume and pricing decision is established based on each category. Based on the cost-plus pricing rule, a nonlinear programming model is established based on the total daily replenishment and pricing decision for each category in the coming week, with the revenue of the superstore as the optimization objective, and considering the constraint of the transport loss rate, the total daily replenishment in the coming week under the maximal revenue is solved by using MATLAB to get the corresponding replenishment strategy, and then the total daily replenishment is substituted into the multivariate linear expression to calculate the corresponding pricing strategy.