Fitness Performance Research Articles

Assessing the Impact of Multicultural Curriculum on Student Performance in Beijing High Schools

Considering increasing diversity and the need for culturally responsive education, this study examines the integration of technology within multicultural curricula in Chinese high schools. This study focuses on the interplay between task characteristics and technological capabilities and their impact on student performance. Grounded in the TaskTechnology Fit theory, the analysis employs structural equation modelling (SEM) to assess the relationships among these variables. This study aims to identify how aligning educational tasks with technological resources can enhance student outcomes in multicultural learning environments. The research design involved collecting data from a purposive sample of teachers and students in Beijing, and the analysis revealed significant relationships among task characteristics (TaC), technology characteristics (TeC), tasktechnology fit (TTF), and student performance (SP). The findings highlight that optimal alignment between educational tasks and technological tools is crucial for enhancing academic performance and fostering deeper engagement with the multicultural aspects of the curriculum. These results emphasise the critical need for strategic selection and integration of technology in educational settings. This study underscores the importance of developing strategies that consider both the pedagogical aspects of the curriculum and the technological tools used for its delivery. This research provides empirical insights into the effective use of technology in multicultural education and offers valuable guidance for educators and policymakers. Furthermore, it plays a role in attaining sustainable development goal 4 and 10. The findings contribute to the ongoing discourse on educational technology and multicultural education, with practical implications for enhancing teaching and learning in diverse educational contexts.

Neural mechanisms of the relationship between aerobic fitness and working memory in older adults: An fNIRS study

Abstract A growing number of studies have revealed that higher aerobic fitness is associated with better working memory (WM) performance in older adults. However, the underlying functional neural mechanisms of this association remain under debate. It has been reported that aging increases recruitment of the prefrontal cortex (PFC) during cognitive tasks, and that this is associated with task performance in a compensatory manner. Therefore, this study aimed to clarify the prefrontal activation pattern that is associated with the relationship between aerobic fitness and WM performance in older adults, focusing on age-related extended prefrontal recruitment. Forty-seven older adults (65–74 years, 29 females) and 49 younger adults (18–24 years, 23 female) performed verbal and spatial n-back tasks, which included 0-, 1-, and 2-back conditions. Reaction time (RT) and accuracy (ACC) were assessed as indices of task performance. Prefrontal activation during the experimental tasks was monitored using functional near-infrared spectroscopy (fNIRS) and analyzed using an adaptive GLM method. We compared task performance and prefrontal activation between age groups to find age-related prefrontal activation patterns. Only older adults underwent a graded exercise test (GXT) to determine their ventilation thresholds (VT) as a measure of aerobic fitness, and, subsequently, the relationships among aerobic fitness, n-back task performance, and prefrontal activation in older adults were examined using correlation analysis and mediation analysis controlling for possible covariates. A comparison of task performance between groups revealed that older adults had slower RT and lower ACC than did younger adults, especially in the higher WM load 2-back condition. Group comparisons of prefrontal activation showed that older adults exhibited additional or greater activation than younger adults mainly in the ventrolateral PFC (VLPFC) and front polar area (FPA) in both the verbal and spatial 2-back conditions. Correlation analysis showed a relationship between higher VT, shorter RT for the verbal 2-back condition, and greater prefrontal activation of the bilateral FPA and right VLPFC during verbal 2-back conditions in older adults. In addition, mediation analyses indicated the possibility of a mediation effect of the prefrontal activation on the relationship between VT and RT for the verbal 2-back condition. These results suggest that older adults with higher aerobic fitness levels recruited more extended PFC regions, possibly for compensatory activation, to enhance their performance of the verbal n-back task. This study sheds light on the neural mechanisms underpinning the relationship between aerobic fitness and cognitive function in older adults.

A novel intervention effect-based quadratic time-varying nonlinear discrete grey model for forecasting carbon emissions intensity

In the context of severe global warming, accurately exploring the trend of carbon emissions intensity (CEI) changes is of great significance for mitigating climate change issues. The implementation of China's Carbon Emissions Trading Scheme (ETS) in 2013 is a policy intervention aimed at influencing CEI. The impact of intervention events makes forecasting a complex problem, which poses significant challenges to the construction of forecasting models. We first develop a quadratic time-varying nonlinear discrete grey model (QDNDGM(1,1)) to assess the intervention effect of the ETS policy. Then, a novel intervention effect-based quadratic time-varying nonlinear discrete grey model (IE-QDNDGM(1,1)) is developed to conduct the prediction under intervention effect, including an intervention term. The Whale Optimization Algorithm (WOA) is used to calculate a nonlinear parameter. We assess the intervention effect of the ETS policy in China and find that it can indeed reduce CEI. We verify the IE-QDNDGM(1,1) model’s superiority by comparing its predictive performance with that of three grey models, one statistical technique, and one artificial intelligence model. The comparative study shows the proposed model’s excellent fitting and prediction performance. An ablation experiment is conducted to validate the design of the IE-QDNDGM(1,1) model. Policy implications of the ETS intervention effect are discussed.

The effect of landing zone toricity on scleral lens fitting characteristics and optics.

The fit and optical performance of a scleral lens is affected by the alignment of the landing zone with the underlying ocular surface. The aim of this research was to quantify the effect of landing zone toricity upon scleral lens fitting characteristics (rotation and decentration) and optics (lens flexure) during short-term wear. Scleral lenses with nominal landing zone toricities of 0, 100, 150 and 200 μm were worn in a randomised order by 10 young healthy participants (mean [SD] 24 [7] years) for 30 min, with other lens parameters held constant. Scleral toricity was quantified using a corneo-scleral profilometer, and lens flexure, rotation, and decentration were quantified using over-topography during lens wear. Repeated measures analyses were conducted as a function of landing zone toricity and residual scleral toricity (the difference between scleral and lens toricity) for eyes with 'low' magnitude scleral toricity (mean: 96 μm) and 'high' magnitude scleral toricity (mean: 319 μm). Toric landing zones significantly reduced lens flexure (by 0.37 [0.21] D, p < 0.05) and lens rotation (by 20 [24]°, p < 0.05) compared with a spherical landing zone. Horizontal and vertical lens decentration did not vary significantly with landing zone toricity. These trends for flexure, rotation, and decentration were also observed for eyes with 'low' and 'high' magnitude scleral toricity as a function of residual scleral toricity. Landing zones with 100-200 μm toricity significantly reduced lens flexure (by ~62%) and rotation (by ~77%) but not horizontal or vertical lens decentration, compared with a spherical landing zone, when controlling for other confounding variables. The incorporation of a toric landing zone, even for eyes with lower magnitude scleral toricity (~100 μm), may be beneficial, particularly for front surface optical designs.

Mapping the landscape of small-sided games in team sports: A bibliometric analysis and literature review

Background Problems: Small-sided games (SSGs) have become a significant focus of research in the context of team sports, and a thorough review and analysis of the existing research landscape is needed to identify trends, gaps, and emerging themes. Research Objectives: This study aims to map the research landscape on small-sided games (SSGs) in the context of team sports through a bibliometric approach and a literature review. Methods: Literature was drawn from established research sources in the Scopus and Web of Science databases from 2014 to 2023. The final data used for this study consisted of 1012 articles, which were then analysed using a bibliometric approach, and the literature review was limited to 237 articles after grouping ten types of team sports using the ScientoPy tool. Findings and Results: The results showed a consistent increase in interest in SSGs, with journals such as “Journal of Strength and Conditioning Research” and “Biology of Sport” playing an important role in expanding understanding of the topic. The analysis also revealed that authors such as Clemente F.M. and Praca G.M. had a significant impact on developing knowledge about SSGs. Although football dominated the related literature, the results also highlighted other sports such as basketball, futsal, and handball. However, there is still a lack of SSG research for sports such as cricket and dodgeball. The benefits of SSGs in team sports include improved technical skills, physical fitness, tactical understanding, and physical performance. Conclusion: This study provides insight into the important role of SSGs in athlete development and team sports training and highlights the need for further research to optimise the full potential of this training method.

A nomogram to predict the risk of venous thromboembolism in patients with colon cancer in China.

To create a nomogram for predicting the likelihood of venous thromboembolism (VTE) in colon cancer patients from China. The data of colon cancer patients from Chongqing University Cancer Hospital between 2019 and 2022 were analyzed. Patients were divided into training set and internal validation set by random split-sample method in a split ratio of 7:3. The univariable and multivariable logistic analysis gradually identified the independent risk factors for VTE. A nomogram was created using all the variables that had a significance level of p < 0.05 in the multivariable logistic analysis and those with clinical significance. Calibration curves and clinical decision curve analysis (DCA) were used to assess model's fitting performance and clinical value. Harrell's C-index (concordance statistic) and the area under the receiver operating characteristic curves (AUC) were used to evaluate the predictive effectiveness of models. A total of 1996 patients were ultimately included. There were 1398 patients in the training set and 598 patients in the internal validation set. The nomogram included age, chemotherapy, targeted therapy, hypertension, activated partial thromboplastin time, prothrombin time, platelet, absolute lymphocyte count, and D-dimer. The C-index of nomogram and Khorana score were 0.754 (95% CI 0.711-0.798), 0.520 (95% CI 0.477-0.563) in the training cohort and 0.713 (95% CI 0.643-0.784), 0.542 (95% CI 0.473-0.612) in the internal validation cohort. We have established and validated a nomogram to predict the VTE risk of colon cancer patients in China, which encompasses a diverse age range, a significant population size, and various clinical factors. It facilitates the identification of high-risk groups and may enable the implementation of targeted preventive measures.

Relationship between Physical Fitness and Match-Play Performance among Youth Netball Players

Relationship between Physical Fitness and Match-Play Performance among Youth Netball Players

A novel framework for predicting non-stationary production time series of shale gas based on BiLSTM-RF-MPA deep fusion model

Shale gas, as an environmentally friendly fossil energy resource, has gained significant commercial development and shows immense potential. However, accurately predicting shale gas production faces substantial challenges due to the complex law of decline, nonlinear and non-stationary features in production data, which greatly repair the robustness of current models in predicting shale gas production time series. To address these challenges and improve accuracy in production forecasting, this paper introduces a novel and innovative approach: a hybrid proxy model that combines the bi-directional long short-term memory (BiLSTM) neural network and random forest (RF) through deep learning. The BiLSTM neural network is adept at capturing long-term dependencies, making it suitable for understanding the intricate relationships between input and output variables in shale gas production. On the other hand, RF serves a dual purpose: reducing model variance and addressing the concept drift problem that arises in non-stationary time series predictions made by BiLSTM. By integrating these two models, the hybrid approach effectively captures the inherent dependencies present in long and nonstationary production time series, thereby reducing model uncertainty. Furthermore, the combination of BiLSTM and RF is optimized using the recently-proposed marine predators algorithm (MPA) to fine-tune hyperparameters and enhance the overall performance of the proxy model. The results demonstrate that the proposed BiLSTM-RF-MPA model achieves higher prediction accuracy and demonstrates stronger generalization capabilities by effectively handling the complex nonlinear and non-stationary characteristics of shale gas production time series. Compared to other models such as LSTM, BiLSTM, and RF, the proposed model exhibits superior fitting and prediction performance, with an average improvement in performance indicators exceeding 20%. This innovative framework provides valuable insights for forecasting the complex production performance of unconventional oil and gas reservoirs, which sheds light on the development of data-driven proxy models in the field of subsurface energy utilization.

Fitting in a workgroup in unique ways: A latent profile analysis of perceived person-group fit characteristics.

Research has identified seven characteristics-value congruence, shared interests, perceived demographic similarity, needs-supplies match, goal similarity, common workstyle, and complementary attributes-on which group members simultaneously evaluate their perceived person-group (PG) fit. Most of extant research has focused on how each characteristic or them as a composite predicts outcomes. However, these variable-centered approaches fail to address how there may be subpopulations of members who differentially combine the PG fit characteristics and how such conjunctive effects differentially relate to various work outcomes. To address these issues, we adopt a profile-based approach using latent profile analysis to understand how group members are similar to and different from each other on more holistic configurations of perceived PG fit experiences. With two widely different samples of employees working in group settings, we found seven unique profiles of PG fit: perfect fits, comfortable fits, surface-level misfits, out of syncs, social misfits, lone wolves, and total misfits. We also found in Sample 2 that these profiles differentially predicted group member outcomes commonly studied in the PG fit literature, including attitudes (satisfaction and cohesion), performance behaviors (task performance and citizenship behaviors of helping and voice), and withdrawal (social loafing and turnover). Complementing research that used variable-centered approaches, our profile-based results reveal new theoretical and practical insights of perceived PG fit, suggesting that different group members have distinct configurations of PG fit, and that higher levels of PG fit are not universally positive, and neither is every type of misfit universally negative. (PsycInfo Database Record (c) 2024 APA, all rights reserved).

The impact of training status on firefighter task performance after acute exercise

Background: Though on-shift exercise provides firefighters structured time to maintain fitness levels needs for their job; many are concerned on-shift exercise may impair their readiness. It also remains unclear if their current fitness level is protective of potential exercise-induced decrements. Therefore, we aimed to determine if aerobic fitness and body composition impact occupational performance and physiological stress after acute exercise. Methods: 32 participants (17M/15F; 25.2 ± 4.1 yrs; 173.8 ± 9.8 cm; 75.6 ± 13.2 kg; 23.3 ± 7.4% body fat) completed 2 visits assessing fitness (VO2peak and %body fat (%BF)), and 3 experimental trials: resistance exercise (RE), aerobic exercise (AE), and rested control (CON). Each trial was followed by an occupational task assessment (OTA; 4 rounds of 10 deadlifts (set to 85 or 135lbs) and a 0.15-mile 40lb-sandbag carry) in an environmental chamber (35°C; 50% humidity). Time to complete (TTC), blood lactate concentrations (BLC), and core temperature (CT) were assessed. Participants were split into tertiles based on high, moderate, and low VO2peak and %BF. Two-way ANOVAs were used to compare fitness level by exercise condition. RESULTS: There was a main effect of VO2peak on TTC (High VO2: 1094 ± 235; Moderate VO2: 1120 ± 200; Low VO2: 1365 ± 263 sec; p &lt; 0.01). High VO2peak participants had a shorter TTC compared to moderate or low VO2max regardless of exercise prior (ps &lt; 0.01). There was a main effect of VO2peak on CT (High VO2: 38.1 ± 0.6; Moderate VO2: 38.2 ± 0.6; Low VO2: 38.4 ± 0.5°C; p &lt; 0.01). High VO2peak participants had a lower CT during the OTA compared to low VO2peak regardless of exercise prior (p &lt; 0.01). There was a main effect of %BF on TTC (High %BF: 1029 ± 150; Moderate %BF: 1098 ± 210; Low %BF: 1399 ± 259 sec; p &lt; 0.001). High %BF participants had a longer TTC compared to average or low %BF regardless of exercise prior (ps &lt; 0.01). There was a significant main effect of %BF on BLC (High %BF: 6.6 ± 2.8; Moderate %BF: 7.6 ± 3.0; Low %BF: 9.9 ± 3.8 mMol/L; p &lt; 0.01). Low %BF participants had lower BLC after the OTA compared to high %BF regardless of exercise prior (p &lt; 0.01). CT were significantly higher following AE compared to RE and CON (AE: 38.7 ± 0.3; RE: 38.5 ± 0.5; CON: 37.6 ± 0.4°C; p &lt; 0.01). All other interaction and main effects of TTC or physiological stress variables were not significantly different based on fitness (VO2peak and %BF; ps &gt; 0.18). CONCLUSIONS: Our findings suggest fitness (VO2peak and %BF) impacts time to complete and physiological stress during occupational tasks. However, fitness did not influence the response to occupational tasks differently based on if acute exercise (aerobic or resistance) was performed beforehand. These findings contribute to our understanding of the impact of acute on-shift exercise methods and provide relevant metrics of fitness related to firefighter occupational performance. FUNDING DISCLOSURE: There are no funding sources to disclose. This is the full abstract presented at the American Physiology Summit 2024 meeting and is only available in HTML format. There are no additional versions or additional content available for this abstract. Physiology was not involved in the peer review process.

Modeling and nonlinear predictive control of solar thermal systems in district heating

This study presents data-driven modeling and nonlinear model predictive control of solar thermal plants in district heating, for the purpose of operation optimization. The study considers the efficient operation of a solar thermal plant in Hillerød, Denmark. A dynamic model is estimated as a system of stochastic differential equations using grey-box modeling and real-world data. The presented nonlinear model predictive controller design is based on repeated trajectory linearization and the dynamic model. Several objective designs are considered, e.g., maximizing energy or temperature. The study provides simulations for analyzing the model fitness and controller performances. The model is shown to fit the daytime operation of the plant. The designed controllers are shown to improve efficiency, increasing the transported energy up to 28%.

Influence of Kinanthopometric Profile of National Level Male Judo Players of Tamil Nadu on Performance

Introduction: Anthropometric Aspects and Body composition are the crucial factors in explaining human physical status. Anthropometric advantage, a non-modifiable factor, significantly contributed to athletes' success in sports. The present study was studied to find out the relationship between the Kinanthopometric profiles and performance level of National-level male Judo players from Tamil Nadu, India. Method: 30 male Judo players were measured for their anthropometric parameters. Height, weight, circumferences, and skinfold thickness at various sites were measured. Body density was calculated, and body fat % was determined using Siri's Equation. A special Judo fitness test involved throwing partners using the ippon-seoi-nage technique in three periods with heart rate checks, and an index was calculated based on total throws and heart rate values. Results: An average height of 158.1 (± 8.5) cm was observed in the present study whereas that of weight was 63.7 (± 7.3) kg. Judo players were categorized into Endomorphic (30%), Mesomorphic (36.7 %) and Ectomorphic (33.4 %) body type. Mesomorphic players showed good performance, Ectomorphic players showed average performance, and Endomorphic players displayed poor performance when performances were analyzed using the Special Judo Fitness Test. This underscored a potential link between body type and Judo performance. Conclusion: The study concluded that body composition has a high impact on physical fitness and sports performance.

PHYSICAL FITNESS AND BALANCE PERFORMANCE IN TABLE TENNIS PLAYERS WITH INTELLECTUAL DISABILITIES: A COMPARISON BETWEEN THE YEARS 2016 AND 2022

Training routine is beneficial for people with intellectual disabilities, increasing their physical fitness, improving the balance and consequently reducing the risk of falls. However, during the COVID-19 breakdown, training restrictions could impair these benefits. This is a two-fold study aimed to compare the physical fitness performance and balance in table tennis athletes with intellectual disability (ID) during 2016 and 2022; and to analyse the association between the fitness and balance parameters. Table tennis athletes with ID, from both sexes and members of the Special Olympics Czech Republic, were assessed in 2016 (n=33) or 2022 (n=32). The FUNfitness protocol was used to measure the physical fitness parameters and balance (e.g., functional shoulder rotation, timed sit-to-stand test, partial sit-up test, handgrip test, single leg stance eyes open, single leg stance eyes closed). Comparison and correlation statistical tests were used (p&lt;0.05). The physical fitness parameters and balance presented no statistically significant between the years (2016 vs 2022) or better results in 2022 (single leg stance, partial sit-up test). The correlation of physical fitness parameters and balance demonstrated that balance is positively related to flexibility (single leg stance eyes closed: r=0.32 and 0.58 for males and females, respectively) and lower limb strength (single leg stance eyes open: r=-0.31 and -0.52 for males and females, respectively). In conclusion, the development of flexibility and low limb strength in ID population can help improve the balance, and consequently minimize the risk of falls.

Associations of physical fitness with cortical inhibition and excitation in adolescents and young adults.

We investigated the longitudinal associations of cumulative motor fitness, muscular strength, and cardiorespiratory fitness (CRF) from childhood to adolescence with cortical excitability and inhibition in adolescence. The other objective was to determine cross-sectional associations of motor fitness and muscular strength with brain function in adolescence. In 45 healthy adolescents (25 girls and 20 boys) aged 16-19 years, we assessed cortical excitability and inhibition by navigated transcranial magnetic stimulation (nTMS), and motor fitness by 50-m shuttle run test and Box and block test, and muscular strength by standing long jump test. These measures of physical fitness and CRF by maximal exercise were assessed also at the ages 7-9, 9-11, and 15-17 years. Cumulative measures of physical measures were computed by summing up sample-specific z-scores at ages 7-9, 9-11, and 15-17 years. Higher cumulative motor fitness performance from childhood to adolescence was associated with lower right hemisphere resting motor threshold (rMT), lower silent period threshold (SPt), and lower motor evoked potential (MEP) amplitude in boys. Better childhood-to-adolescence cumulative CRF was also associated with longer silent period (SP) duration in boys and higher MEP amplitude in girls. Cross-sectionally in adolescence, better motor fitness and better muscular strength were associated with lower left and right rMT among boys and better motor fitness was associated with higher MEP amplitude and better muscular strength with lower SPt among girls. Physical fitness from childhood to adolescence modifies cortical excitability and inhibition in adolescence. Motor fitness and muscular strength were associated with motor cortical excitability and inhibition. The associations were selective for specific TMS indices and findings were sex-dependent.

Dental Age Estimation in Northern Chinese Han Children and Adolescents Using Demirjian's Method Combined with Machine Learning Algorithms.

To investigate the application value of combining the Demirjian's method with machine learning algorithms for dental age estimation in northern Chinese Han children and adolescents. Oral panoramic images of 10 256 Han individuals aged 5 to 24 years in northern China were collected. The development of eight permanent teeth in the left mandibular was classified into different stages using the Demirjian's method. Various machine learning algorithms, including support vector regression (SVR), gradient boosting regression (GBR), linear regression (LR), random forest regression (RFR), and decision tree regression (DTR) were employed. Age estimation models were constructed based on total, female, and male samples respectively using these algorithms. The fitting performance of different machine learning algorithms in these three groups was evaluated. SVR demonstrated superior estimation efficiency among all machine learning models in both total and female samples, while GBR showed the best performance in male samples. The mean absolute error (MAE) of the optimal age estimation model was 1.246 3, 1.281 8 and 1.153 8 years in the total, female and male samples, respectively. The optimal age estimation model exhibited varying levels of accuracy across different age ranges, which provided relatively accurate age estimations in individuals under 18 years old. The machine learning model developed in this study exhibits good age estimation efficiency in northern Chinese Han children and adolescents. However, its performance is not ideal when applied to adult population. To improve the accuracy in age estimation, the other variables can be considered.

A 12-Week Ballroom Dance Training Improves Physical Fitness Performance More Than Walking Training in Older Women, Regardless of Vitamin B12 or D Adequacy.

This study aimed to compare the effects of Ballroom Dancing (BD) versus Walking Training (WT) on the physical fitness performance in physically independent older women with adequate or inadequate levels of vitamins B12 and D. Forty-three sedentary women aged 68.5 ± 6.5 years, were allocated to the BD (n = 23) or WT (n = 20) groups. They took part in a 12-week intervention, performed 3 times a week, for about 50 minutes with moderate effort intensity. Data were collected through Short Physical Performance Battery (SPPB), 6 minutes Walk Test (6MWT), Hand Grip Test (HGT), Isokinetic tests for lower limbs and blood tests to detect serum levels of vitamins B12 and D. The BD group performed better after the intervention in relation to the WT in the Sit and Stand Test (SST) (BD pre = 3.1 score vs post = 3.8 score; WT pre = 2.8 score vs post = 3.4 score; P = .02) and in the Peak Torque 180° extension (PKTOQ 180° extension) (BD pre = 56.7 Nm vs post = 61.2 Nm, WT pre = 56.7 Nm vs post = 56.1; P < .01). A time effect was observed in all other variables, with the exception of HGT. Both interventions improved physical fitness performance, regardless of the adequacy of vitamins B12 and D, but the older women from BD obtained significant improvements in more variables than the WT.

Batch Specular Plane Flatness Measurements Based on Phase Measuring Deflectometry.

Flatness is a critical parameter in the manufacturing industry, directly impacting the fit and overall product performance. As the efficiency of manufacturing continues to advance, there is an increasing demand for more accurate and efficient measurement techniques. Existing methods often struggle to strike a balance between precision and efficiency. In response, this article introduces a novel approach that is capable of achieving high-precision and rapid measurements concerning multiple surfaces. By enhancing the traditional phase measuring deflectometry (PMD) method, employing a matching technique based on polar lines and normal vector constraints to address discrete surface measurement challenges, and implementing a plane pre-positioning method to tackle low efficiency in binocular matching and solving, we successfully performed swift and synchronized measurements for a large batch of specular surfaces and obtained the three-dimensional surface profile of each measured surface. Through experimental validation, the method proposed in this paper can perform the batch measurement of specular planes while maintaining high measurement accuracy.

Study on the Impact of Urban Morphologies on Urban Canopy Heat Islands Based on Relocated Meteorological Stations

This study addresses a crucial gap in understanding the impact of urban morphologies on the canopy urban heat islands (CUHI) effect. The selection of reference stations lacks a unified standard, and their surface air temperature (SAT) sequences are also inevitably influenced by urbanization. However, synchronous observational data from relocated meteorological stations could provide high-quality sample data for studying CUHI. Utilizing remote sensing techniques, the findings of this paper revealed that the observation environment of stations after relocation exhibited remarkable representativeness, with their observation sequences accurately reflecting the local climatic background. The differences in synchronized observation sequences could characterize the CUHI intensity (CUHII). Among the various factors, land use parameters and landscape parameters played particularly significant roles. Furthermore, the fitting performance of the random forest (RF) model for both training and testing data was significantly superior to that of the linear model and support vector regression (SVR) model. Additionally, the influence of local circulation on CUHI could not be overlooked. The mechanisms by which urban morphologies affect CUHII under different circulation backgrounds deserve further investigation.

Resistance training and cardiometabolic risk in women with metabolically healthy and unhealthy obesity.

Despite some reported benefits, there is a low quality of evidence for resistance training (RT) improving metabolic health of individuals with overweight or obesity. We evaluated the impact of RT on body composition, cardiorespiratory fitness (CRF) and physical performance, lipid-lipoprotein profile, inflammation, and glucose-insulin homeostasis in 51 postmenopausal women versus 29 controls matched for age, obesity, and physical activity. Exercised women were further subdivided for comparison of RT effects into those presenting metabolically healthy obesity (MHO) and those with metabolically unhealthy obesity (MUHO) classified according to Karelis and Rabasa-Lhoret or an approach based on adipose tissue secretory dysfunction using the plasma adiponectin(A)/leptin (L) ratio. Participants followed a 4-month weekly RT program targeting major muscle groups (3 × 10 repetitions at 80% one repetition maximum (1-RM)). Percent fat marginally decreased and lean body mass increased (0.01<p<0.05) while CRF and muscular strength improved in all women, after RT (effect size (ES): 0.11-1.21 (trivial to large effects), p ˂ 0.01). Fasting plasma triacylglycerol and high-density lipoprotein-cholesterol levels slightly increased and decreased, respectively, in participants with MHO using the A/L ratio approach (ES: -0.47 to 1.07 (small to large effects), p ˂ 0.05). Circulating interleukin-6 soluble receptor decreased in both groups and soluble tumor necrosis factor receptor-1/soluble tumor necrosis factor receptor-2 in women with MUHO only, irrespective of definition (ES: -0.42 to-0.84 (small to large effects), p ˂ 0.05). Glucose-insulin homeostasis was unchanged regardless of group or definition. RT improved physical performance and body composition but had a lesser impact on cardiometabolic risk in women with obesity, irrespective of their metabolic phenotype.

A novel method for studying the wind speed probability distribution and estimating the average wind energy density

Traditional distribution models generally have large fitting errors at low wind speeds and poor fitting effects at multi-peak wind speed distributions. In this paper, a novel approach is proposed to fit different wind speed distributions, introducing a Gumbel distribution into common hybrid distribution models. The model parameters are solved by a combination of snake optimizer and nonlinear least squares (SO-NLS), using the optimal values obtained by the nonlinear least squares method as a set of initial input vectors for the snake optimizer. Simulation experiments were conducted using multi-peak wind speed distribution datasets with varying characteristics, comparing the fitting performance of the improved hybrid models against the conventional Weibull, Normal, and Rayleigh hybrid models. The results show that the proposed approach improved the model fit effects, particularly at low wind speeds, in all five experimental datasets. In most cases, the overall fitting effects were also improved. Furthermore, the validity and superiority of the improved hybrid models were further verified by comparing the estimated average wind energy density. Meanwhile, the experimental results also verified that SO-NLS not only yielded better optimization results but also accelerated the convergence speed than the snake optimizer. The improvements presented in this study effectively address the problem of large fitting errors at the low wind speed sections of the distribution, providing a theoretical basis for wind farm planning and design.