Number Of Repetitions Research Articles

Narrowband Internet of Things via Low Earth Orbit Satellite Networks: An Efficient Coverage Enhancement Mechanism Based on Stochastic Geometry Approach.

With the development of IoT technology and 5G massive machine-type communication, the 3GPP standardization body considered as viable the integration of Narrowband Internet of Things (NB-IoT) in low Earth orbit (LEO) satellite-based architectures. However, the presence of the LEO satellite channel comes up with new challenges for the NB-IoT random access procedures and coverage enhancement mechanism. In this paper, an Adaptive Coverage Enhancement (ACE) method is proposed to meet the requirement of random access parameter configurations for diverse applications. Based on stochastic geometry theory, an expression of random access channel (RACH) success probability is derived for LEO satellite-based NB-IoT networks. On the basis of a power consumption model of the NB-IoT terminal, a multi-objective optimization problem is formulated to trade-off RACH success probability and power consumption. To solve this multi-objective optimization problem, we employ the Non-dominated Sorting Genetic Algorithms-II (NSGA-II) method to obtain the Pareto-front solution set. According to different application requirements, we also design a random access parameter configuration method to minimize the power consumption under the constraints of RACH success probability requirements. Simulation results show that the maximum number of repetitions and back-off window size have a great influence on the system performance and their value ranges should be set within [4, 18] and [0, 2048]. The power consumption of coverage enhancement with ACE is about 58% lower than that of the 3GPP proposed model. All this research together provides good reference for the scale deployment of NB-IoT in LEO satellite networks.

Resistance Exercise Minimal Dose Strategies for Increasing Muscle Strength in the General Population: an Overview.

Many individuals do not participate in resistance exercise, with perceived lack of time being a key barrier. Minimal dose strategies, which generally reduce weekly exercise volumes to less than recommended guidelines, might improve muscle strength with minimal time investment. However, minimal dose strategies and their effects on muscle strength are still unclear. Here our aims are to define and characterize minimal dose resistance exercise strategies and summarize their effects on muscle strength in individuals who are not currently engaged in resistance exercise. The minimal dosestrategies overviewed were: "Weekend Warrior," single-set resistance exercise, resistance exercise "snacking," practicing the strength test, and eccentric minimal doses. "Weekend Warrior," which minimizes training frequency, is resistance exercise performed in one weekly session. Single-set resistance exercise, which minimizes set number and session duration, is one set of multiple exercises performed multiple times per week. "Snacks," which minimize exercise number and session duration, are brief bouts (few minutes) of resistance exercise performed once or more daily. Practicing the strength test, which minimizes repetition number and session duration, is one maximal repetition performed in one or more sets, multiple days per week. Eccentric minimal doses, which eliminate or minimize concentric phase muscle actions, are low weekly volumes of submaximal or maximal eccentric-only repetitions. All approaches increase muscle strength, and some approaches improve other outcomes of health and fitness. "Weekend Warrior" and single-set resistance exercise are the approaches most strongly supported by current research, while snacking and eccentric minimal doses are emerging concepts with promising results. Public health programs can promote small volumes of resistance exercise as being better for muscle strength than no resistance exercise at all.

Distinct adaptations of muscle endurance but not strength or hypertrophy to low-load resistance training with and without blood flow restriction.

Low-load resistance training promotes muscle strength and hypertrophic adaptations when combined with blood flow restriction (BFR). However, the effect of BFR on muscle endurance remains unclear. The aim of this study was to clarify the effects of BFR on muscle performance and adaptation, with special reference to local muscle endurance. In experiment1, eight healthy men performed unilateral elbow flexion exercise to failure at 30% of one-repetition maximum with BFR (at 40% of estimated arterial occlusion pressure) and free blood flow (FBF). During the exercise, muscle activity and tissue oxygenation were measured from the biceps brachii. In experiment 2, another eight healthy men completed 6weeks of elbow flexion training with BFR and FBF. The number of repetitions to failure at submaximal load (Rmax), the estimated time for peak torque output to decay by 50% during repetitive maximum voluntary contractions (half-time), one-repetition maximum, isometric strength and muscle thickness of elbow flexors were measured pre- and post-training. Blood flow restriction resulted in fewer repetitions and lower muscle tissue oxygenation at the end of exercise than FBF, while the muscle activity increased similarly to repetition failure. Blood flow restriction also resulted in a smaller post-training Rmax, which was strongly correlated with the total exercise volume over the 6week period. Despite the smaller exercise volume, BFR resulted in similar improvements in half-time, muscle strength and thickness compared with FBF. These results suggest that the application of BFR can attenuate muscle endurance adaptations to low-load resistance training by decreasing the number of repetitions during exercise, both acutely and chronically.

RELATIONSHIP BETWEEN BURPEE TEST AND BODY COMPOSITION: A SAMPLE OF FEMALE UNIVERSITY STUDENTS

This study aimed to examine the relationship between the 30-second Burpee test and body composition and certain physical parameters in female students studying at the Social Sciences Vocational School, Special Security and Protection Program, Çankırı Karatekin University. Fifty volunteer students participated in the study, with an average age of 19.24±0.89 years, an average height of 164.66±5.94 cm, an average body weight of 60.94±4.44 kg, an average body fat percentage of 22.93±1.82%, and an average BMI of 25.92±2.68 kg/m2. The statistical analyses were performed using SPSS 23 (SPSS Inc., Chicago, IL, USA) software package. The variance homogeneity of the data was tested using Levene's Test, and the normal distribution analyses were conducted using the Shapiro-Wilk Test. Pearson Correlation Analysis was used for all parameter correlations. The significance level was set at p < 0.05. According to the findings of this study, which examined the relationship between the 30-second Burpee test and certain physiological parameters, the following results were obtained: a positive and statistically significant relationship was found between body mass and Burpee, height (cm) and standing long jump, body fat percentage (%) and sit-up test, as well as the number of exercise repetitions (p < 0.05). However, no statistically significant relationship was found between body composition and Flamingo balance, Assisted pull-up, and 10x5m tests (p > 0.05). In conclusion, the 30-second Burpee test can be beneficial for assessing strength endurance among physiological parameters, especially regarding body mass. Considering body composition, the statistically significant negative relationship between body weight and the 30-second Burpee (frequency) should be taken into consideration as a determining factor during test trials.

Dynamics of physical status indicators under the influence of healthy swimming classes, constructed taking into assount the age characterristics of women's development

The work examines the dynamics of indicators of the physical condition of women aged 20-25 under the influence of recreational swimming classes, built taking into account the age characteristics of women. The purpose of the study was to investigate the dynamics of indicators of physical development, functional state and physical fitness of women aged 20-25 under the influence of recreational swimming classes according to a program that takes into account their age characteristics. The research was carried out in the physical culture and health groups of the "Pioneer" swimming pool in Kharkiv (KZ MSDYUSSHOR on water sports of Yana Klochkova). The study lasted for 10 months. The study involved 30 women aged 20 to 25, randomly selected from among those who attend recreational swimming groups. Two groups were formed for the study: experimental and control. Initially, both groups used the same exercises on the water (low-intensity exercises for familiarization with water, breathing, special exercises, for relaxation; the technique of the chosen method of swimming was consolidated and improved). Later, swimming took on the character of training, and the exercises offered to individuals of both groups began to differ. This difference consisted in the fact that in the experimental group, the duration, number of exercises and repetitions, dose of load, complexity of exercises and amplitude of their movements were proposed according to the age characteristics of the state of the female body. Classes in both groups were held three times a week - the duration of one class was 60 minutes. The following methods were used during data collection: analysis of literary sources; test method; pedagogical experiment; pedagogical observation; method of mathematical processing of research data. The work presents materials that show that the research conducted to study the effectiveness of the program developed and applied by us for women 20-25 years old has confirmed data and a reliable advantage in comparison with the generally accepted program. Eleven out of twelve indicators of physical development and physical fitness improved statistically significantly in the subjects of the experimental group (p <0.01). \The developed health swimming program for women aged 20-25 can be recommended in the health groups of the city's swimming pools.

DNA Profiling, Bioinformatics and Databases in Forensics: Human Identification Purposes

Individual identification is an essential element in establishing truth the forensic investigation process, be it in criminal, medico-legal, or mass disasters case. When ante-mortem data are not available, the only thing that can be used is identification trough DNA. Alec Jeffrey, a geneticist (1984), found that certain regions of DNA contain repetitive sequences and the number of repetitions in individuals differs from one another. This identification method known as DNA profiling. DNA profiling is described as an important and significant discovery in forensics and has been considered as the standard in modern human identification. Until now, the recommended DNA profiling method must be based on the PCR to analyze degraded DNA and short-sized DNA (Short Tandem Repeats) through PCR amplification. In profiling, the result DNA amplification are analyzed through genetic markers (DNA Markers) and then matched into the database or compared with the collected reference samples. In the human genome, the genetic markers most frequently used in forensics are autosomal STR, Y-STR, mtDNA, X-STR, SNPs, and Amelogenin. Accomplishment of proving the suitability of DNA profiles, an application of bioinformatics in forensics is carried out. Bioinformatics is a combination of molecular biology and computer informatics that aims to manage and analyze data and store biological (genetic) information. DNA database is an application of bioinformatics in the form of special software which has become an important tool for biologists and forensics. This genetic database will predict the similarities between one DNA profile and another.

The Effect of Oral Adenosine Triphosphate (ATP) Supplementation on Anaerobic Exercise in Healthy Resistance-Trained Individuals: A Systematic Review and Meta-Analysis.

Adenosine triphosphate (ATP) is an energy and signaling molecule. It is synthesized endogenously and can be taken as an oral supplement. This review aimed to identify the effects of oral ATP supplementation on anaerobic exercise in healthy resistance-trained adults. A systematic review and meta-analysis were performed based on the Preferred Reporting Items of Systematic Reviews and Meta-Analysis (PRISMA) criteria. The inclusion criteria were articles published from 2000 to 2022, with anaerobic variables (maximal strength, maximum repetitions, and maximum anaerobic power) measurable in healthy adults with experience in resistance training, only randomized placebo-controlled clinical trials (RCTs), and with the acute (a single dose 30 min to 24 h before the tests) and/or chronic (>1 day) oral supplementation of ATP. A total of five RCTs with 121 adult men were included. The oral ATP supplementation achieved significantly greater gains in maximal strength compared with the placebo (PL) (MD = 8.13 kg, 95%CI [3.36-12.90], p < 0.001). Still, no differences were observed in the maximum number of repetitions or the maximum anaerobic power. Furthermore, 400 mg of ATP showed improvement in anaerobic exercise regardless of the duration of the supplementation protocol. In conclusion, supplementation with 400 mg of ATP doses can improve maximal muscle strength in resistance-trained men.

Would it be possible to reduce the number of repetitions in the evaluation of clones in a single tree plot?

Would it be possible to reduce the number of repetitions in the evaluation of clones in a single tree plot?

Identification of leaf diseases in field crops based on improved ShuffleNetV2.

Rapid and accurate identification and timely protection of crop disease is of great importance for ensuring crop yields. Aiming at the problems of large model parameters of existing crop disease recognition methods and low recognition accuracy in the complex background of the field, we propose a lightweight crop leaf disease recognition model based on improved ShuffleNetV2. First, the repetition number and the number of output channels of the basic module of the ShuffleNetV2 model are redesigned to reduce the model parameters to make the model more lightweight while ensuring the accuracy of the model. Second, the residual structure is introduced in the basic feature extraction module to solve the gradient vanishing problem and enable the model to learn more complex feature representations. Then, parallel paths were added to the mechanism of the efficient channel attention (ECA) module, and the weights of different paths were adaptively updated by learnable parameters, and then the efficient dual channel attention (EDCA) module was proposed, which was embedded into the ShuffleNetV2 to improve the cross-channel interaction capability of the model. Finally, a multi-scale shallow feature extraction module and a multi-scale deep feature extraction module were introduced to improve the model's ability to extract lesions at different scales. Based on the above improvements, a lightweight crop leaf disease recognition model REM-ShuffleNetV2 was proposed. Experiments results show that the accuracy and F1 score of the REM-ShuffleNetV2 model on the self-constructed field crop leaf disease dataset are 96.72% and 96.62%, which are 3.88% and 4.37% higher than that of the ShuffleNetV2 model; and the number of model parameters is 4.40M, which is 9.65% less than that of the original model. Compared with classic networks such as DenseNet121, EfficientNet, and MobileNetV3, the REM-ShuffleNetV2 model not only has higher recognition accuracy but also has fewer model parameters. The REM-ShuffleNetV2 model proposed in this study can achieve accurate identification of crop leaf disease in complex field backgrounds, and the model is small, which is convenient to deploy to the mobile end, and provides a reference for intelligent diagnosis of crop leaf disease.

Benchmarks for Pirá 2.0, a Reading Comprehension Dataset about the Ocean, the Brazilian Coast, and Climate Change

ABSTRACT Pirá is a reading comprehension dataset focused on the ocean, the Brazilian coast, and climate change, built from a collection of scientific abstracts and reports on these topics. This dataset represents a versatile language resource, particularly useful for testing the ability of current machine learning models to acquire expert scientific knowledge. Despite its potential, a detailed set of baselines has not yet been developed for Pirá. By creating these baselines, researchers can more easily utilize Pirá as a resource for testing machine learning models across a wide range of question answering tasks. In this paper, we define six benchmarks over the Pirá dataset, covering closed generative question answering, machine reading comprehension, information retrieval, open question answering, answer triggering, and multiple choice question answering. As part of this effort, we have also produced a curated version of the original dataset, where we fixed a number of grammar issues, repetitions, and other shortcomings. Furthermore, the dataset has been extended in several new directions, so as to face the aforementioned benchmarks: translation of supporting texts from English into Portuguese, classification labels for answerability, automatic paraphrases of questions and answers, and multiple choice candidates. The results described in this paper provide several points of reference for researchers interested in exploring the challenges provided by the Pirá dataset.

New formulation for predicting total dissolved gas supersaturation in dam reservoir: application of hybrid artificial intelligence models based on multiple signal decomposition

Total dissolved gas (TDG) concentration plays an important role in the control of the aquatic life. Elevated TDG can cause gas-bubble trauma in fish (GBT). Therefore, controlling TDG fluctuation has become of great importance for different disciplines of surface water environmental engineering.. Nowadays, direct estimation of TDG is expensive and time-consuming. Hence, this work proposes a new modelling framework for predicting TDG based on the integration of machine learning (ML) models and multiresolution signal decomposition. The proposed ML models were trained and validated using hourly data obtained from four stations at the United States Geological Survey. The dataset are composed from: (i) water temperature (Tw), (ii) barometric pressure (BP), and (iii) discharge (Q), which were used as the input variables for TDG prediction. The modelling strategy is conducted based on two different steps. First, six singles ML model namely: (i) multilayer perceptron neural network, (ii) Gaussian process regression, (iii) random forest regression, (iv) random vector functional link, (v) adaptive boosting, and (vi) Bootstrap aggregating (Bagging), were developed for predicting TDG using Tw, BP, and Q, and their performances were compared. Second, a new framework was introduced based on the combination of empirical mode decomposition (EMD), the variational mode decomposition (VMD), and the empirical wavelet transform (EWT) preprocessing signal decomposition algorithms with ML models for building new hybrid ML models. Hence, the Tw, BP, and Q signals were decomposed to extract the intrinsic mode functions (IMFs) by using the EMD and VMD methods and the multiresolution analysis (MRA) components by using the EWT method. Then after, the IMFs and MRA components were selected and regraded as new input variables for the ML models and used as an integral part thereof. The single and hybrid prediction models were compared using several statistical metrics namely, root mean square error, mean absolute error, coefficient of determination (R2), and Nash–Sutcliffe efficiency (NSE). The single and hybrid models were trained several times with high number of repetitions, depending on the kind of modeling process. The obtained results using single models gave good agreement between the predicted TDG and the situ measured dataset. Overall, the Bagging model performed better than the other five models with R2 and NSE values of 0.906 and 0.902, respectively. However, the extracted IMFs and MRA components using the EMD, VMD and the EWT have contributed to an improvement of the hybrid models’ performances, for which the R2 and NSE were significantly increased reaching the values of 0.996 and 0.995. Experimental results showed the superiority of hybrid models and more importantly the importance of signal decomposition in improving the predictive accuracy of TDG.Graphical abstract

A novel approach to accelerate training in artificial neural network to detect three-phase flows using a gamma source and a detector

Measuring the percentage of mass fraction of multiphase flow in pipelines is one of the most significant issues in the petrochemical oil industry. In this research, The mass fraction in three-phase flow has been determined with artificial neural network. There are various ways to accelerate the neural network that in this study, changing the activation function and normalizing the data has been considered. There are 5 activation functions and 5 normalization methods for the input data which combine to create 25 states. Then, by using the normalizing method for the input data to the network to (12tanh(1100Xi−avg(Xi)std(Xi))), while changing the hidden network activator function to (f(X)=tanh(X)), the average number of repetitions for network training reached 171 repetitions and the average number of errors was 0.002. While without applying the mentioned cases, i.e., without normalizing the data and using the activation function (f(X)=X), the average value of errors is 5.63 and the average value of repetition is 417.

A new sports garment with elastomeric technology optimizes physiological, mechanical, and psychological acute responses to pushing upper-limb resistance exercises.

This study aimed to compare the mechanical (lifting velocity and maximum number of repetitions), physiological (muscular activation, lactate, heart rate, and blood pressure), and psychological (rating of perceived exertion) responses to upper-body pushing exercises performed wearing a sports elastomeric garment or a placebo garment. Nineteen physically active young adults randomly completed two training sessions that differed only in the sports garment used (elastomeric technology or placebo). In each session, subjects performed one set of seated shoulder presses and another set of push-ups until muscular failure. The dependent variables were measured immediately after finishing the set of each exercise. Compared to the placebo garment, the elastomeric garment allowed participants to obtain greater muscular activation in the pectoralis major (push-ups: p = 0.04, d = 0.49; seated shoulder press: p < 0.01, d = 0.64), triceps brachialis (push-ups, p < 0.01, d = 0.77; seated shoulder press: p < 0.01, d = 0.65), and anterior deltoid (push-ups: p < 0.01, d = 0.72; seated shoulder press: p < 0.01, d = 0.83) muscles. Similarly, participants performed more repetitions (push-ups: p < 0.01; d = 0.94; seated shoulder press: p = 0.03, d = 0.23), with higher movement velocity (all p ≤ 0.04, all d ≥ 0.47), and lower perceived exertion in the first repetition (push-ups: p < 0.01, d = 0.61; seated shoulder press: p = 0.05; d = 0.76) wearing the elastomeric garment compared to placebo. There were no between-garment differences in most cardiovascular variables (all p ≥ 0.10). Higher diastolic blood pressure was only found after the seated shoulder press wearing the elastomeric garment compared to the placebo (p = 0.04; d = 0.49). Finally, significantly lower blood lactate levels were achieved in the push-ups performed wearing the elastomeric garment (p < 0.01; d = 0.91), but no significant differences were observed in the seated shoulder press (p = 0.08). Overall, the findings of this study suggest that elastomeric technology integrated into a sports garment provides an ergogenic effect on mechanical, physiological, and psychological variables during the execution of pushing upper-limb resistance exercises.

Mental fatigue impairs the number of repetitions to muscular failure in the half back‐squat exercise for low‐ and mid‐ but not high‐intensity resistance exercise

AbstractThe study aimed to analyze the acute effect of mental fatigue on the maximum number of repetitions in a resistance exercise session with different intensities in resistance‐trained adults. Eighteen young men aged between 18 and 25 years old (age, 22.1 ± 2.0 years; body weight, 82.5 ± 6.6 kg; height, 177.4 ± 5.2 cm; half‐back squat 1‐RM, 106.7 ± 21.9 kg) were recruited for the study. Each participant performed two trials (i.e., control and mental fatigue) in a random and balanced order for 30‐min. The participants performed three sets of half back‐squat exercise to failure, with intensities of 50%, 70%, and 90% of 1RM with a passive recovery interval of 5‐min between sets. The intensity was randomized and counterbalanced, and the order was maintained in both conditions for the same subject. We assessed resistance training using the number of repetitions to failure and perceived effort and checked the mental fatigue subjectively and objectively. The participants in the mental fatigue condition presented a significantly increased perception of mental fatigue (p &lt; 0.05) and reduced pupil diameter (p &lt; 0.05). The number of repetitions were significantly lower for the 50 (p &lt; 0.05) and 75% 1RM (p &lt; 0.05) in the mental fatigue condition, but the 90% 1RM remained similar (p &gt; 0.05). Also, the perceived effort showed significantly higher results for the 50 (p &lt; 0.05) and 75% 1RM (p &lt; 0.05) in the mental fatigue condition, but the 90% 1RM remained similar (p &gt; 0.05). Then, this study showed that mental fatigue reduced resistance exercise performance for low‐ and mid‐intensity but not for high‐intensity.

Sex differences in fatiguability during single-joint resistance exercise in a resistance-trained population.

The primary aim of this study was to compare the fatiguability and acute recovery from a session of unilateral biceps curl exercise between habitually resistance-trained males and females. Twenty participants performed biceps curl one repetition maximum (1RM) and maximal voluntary contraction (MVC) testing of the elbow flexors. The exercise protocol consisted of four sets of unilateral biceps curls at 50% of the measured 1RM to volitional failure. MVC force and EMG activity of the elbow flexors were assessed following each set and during the recovery phase. The outcome variables compare the repetition volume, MVC strength loss, and EMG responses between males and females during the exercise and recovery phases. Across the exercise protocol, females completed significantly more repetitions compared to males (p < 0.01, d = 1.7) and lost significantly less strength across time (76% versus 69%, d = 0.48, p = 0.046). Additionally, females maintained a higher level of normalized EMG amplitude compared to males across the protocol (p = 0.035; d = 0.510). However, there were no sex differences in the strength (p = 0.562) or EMG (p = 0.607) responses during the recovery phase. In a resistance-trained population, females were able to perform a greater number of repetitions during the biceps curl exercise compared to their male counterparts at the same relative intensity of exercise. The ability of females to maintain a greater proportion of their maximal elbow flexor strength and muscle excitation during exercise suggests females can tolerate greater upper-body resistance exercise volume than males.

Design and utilisation of a novel, high-fidelity, low-cost, hybrid-tissue simulation model to facilitate training in robot-assisted partial nephrectomy

Robot-assisted partial nephrectomy (RAPN) has rapidly evolved as the standard of care for appropriately selected renal tumours, offering key patient benefits over radical nephrectomy or open surgical approaches. Accordingly, RAPN is a key competency that urology trainees wishing to treat kidney cancer must master. Training in robotic surgery is subject to numerous challenges, and simulation has been established as valuable step in the robotic learning curve. However, simulation models are often both expensive and suboptimal in fidelity. This means that the number of practice repetitions for a trainee may limited by cost restraints, and that trainees may struggle to reconcile the skills obtained in the simulation laboratory with real-world practice in the operating room. We have developed a high-fidelity, low-cost, customizable model for RAPN simulation based on porcine tissue. The model has been utilised in teaching courses at our institution, confirming both feasibility of use and high user acceptability. We share the design of our model in this proof-of-concept report.

Uniform Homeostatic Stress Through Individualized Interval Training Facilitates Homogeneous Adaptations Across Rowers With Different Profiles.

This study compared the effects of individualizing supramaximal interval rowing interventions using anaerobic power reserve (APR [high-intensity interval training (HIIT) prescribed according to individual APR (HIITAPR)]) and power associated with maximal oxygen uptake (WV˙O2max [HIIT prescribed based on the individual WV˙O2max (HIITW)]) on the homogeneity of physiological and performance adaptations. Twenty-four well-trained rowers (age 24.8 [4.3]y, stature 182.5 [3]cm, body mass 86.1 [4.3]) were randomized into interventions consisting of 4 × 30-second intervals at 130%APR (WV˙O2max + 0.3 × maximal sprint power) with weekly progression by increasing the number of repetitions per set (5, 6, 7, 8, 9, and 10, from first to sixth session) and the same sets and repetitions with the intensity described as 130% WV˙O2max. The work-to-recovery ratio was 1:1 for repetitions and 3minutes between sets. Responses of aerobic fitness indices, power output, cardiac hemodynamics, locomotor abilities, and time-trial performance were examined. Both HIITAPR and HIITW interventions significantly improved V˙O2max, lactate threshold, cardiac hemodynamics, and 2000-m performance, with no between-groups difference in changes over time. However, HIITAPR resulted in a lower interindividual variability in adaptations in V˙O2max and related physiological parameters, but this is not the case for athletic performance, which can depend on a multitude of factors beyond physiological parameters. Results demonstrated that expressing supramaximal interval intensity as a proportion of APR facilitates imposing the same degrees of homeostatic stress and leads to more homogeneous physiological adaptations in maximal variables when compared to prescribing a supramaximal HIIT intervention using WV˙O2max. However, lower interindividual variability would be seen in submaximal variables if HIIT interventions were prescribed using WV˙O2max.

Combination of Training to Failure and Non-Failure: An Unexplored Approach of Number of Repetitions, Rate of Perceived Exertion, Time Under Tension, Volume-Load, and Muscle Swelling in Trained Individuals

Combination of Training to Failure and Non-Failure: An Unexplored Approach of Number of Repetitions, Rate of Perceived Exertion, Time Under Tension, Volume-Load, and Muscle Swelling in Trained Individuals

Inter-Set Foam Rolling of the Latissimus Dorsi Acutely Increases Repetitions in Lat Pull-Down Exercise without Affecting RPE.

Foam rolling is widely used in fitness, sports, rehabilitation, and injury prevention. However, there are limited data available on the effect of foam rolling techniques on the upper limbs. The aim of this investigation is to assess the effects of foam rolling the latissimus dorsi area during the rest period between two consecutive lat pull-down exercise (LPDE) sets. Seventeen resistance training experienced volunteer male subjects (25.8 ± 3.4 years; 180.3 ± 9.0 cm; 79.7 ± 9.9 kg) participated in this research. Each subject performed 2 training sessions of LPDE in a random order, separated by one week. Each session consisted of 2 sets of maximum repetitions using 85% of their one-repetition maximum (1RM), with a 7 min rest period between sets. The rest period condition between sets was different in the 2 sessions: passive rest (Pr) or foam rolling the latissimus dorsi muscle bilaterally for 3 sets of 45 s (FRr). The following variables were assessed for each LPDE set: number of repetitions (REPS), average excursion per repetition in millimeters (EXC), average power of the set in watts (AP) and rating of perceived exertion (RPE). Pr did not show any significant change between the first and the second LPDE set for REPS, EXC, and AP. However, there was a significant increase for RPE (8.4 ± 0.5 vs. 8.9 ± 0.5 a.u., p = 0.003) between the two sets. FRr resulted in an increase for REPS (7.1 ± 1.5 vs. 8.2 ± 1.3, p < 0.001) and AP (304.6 ± 61.5 W vs. 318.8 ± 60.8 W, p = 0.034) between the first and the second LPDE sets, but no changes were observed for EXC and RPE. The use of foam rolling techniques on the latissimus dorsi area during the complete rest period between sets in LPDE at 85% 1RM appears to improve the number of repetitions and the movement power without affecting the RPE during the second set.

Strength, deformation, and environmental impact assessment of cement stabilized mine overburden soil

Mine haul roads are the unpaved roads that are constructed from the overburden materials obtained from the mining operations and are used for the movement of heavily loaded dumpers and trucks. The haul roads constructed from this overburden material shows continuous distress in the form of over ruts, potholes, and shear failures, creating major issues in the movement of dumpers. In the present research study, an experimental investigation was conducted based on mechanistic empirical design to evaluate the strength-deformation characteristics, durability, and environmental emissions of cement stabilized overburden soil of a local mine. The unconfined compression tests were conducted at cement dosages varying from 1 to 6% of dry soil mass and increase in the unconfined compressive strength were examined at different intervals of time until 28 days of the curing period. In the case of static triaxial tests, the enhancement in undrained shear strength and elastic modulus of stabilized specimens was determined in comparison to the untreated specimens, whereas in cyclic triaxial tests, the reduction in permanent deformations and increase in resilient modulus were evaluated under different confining pressures and cyclic deviatoric stresses. An empirical model has been proposed to predict the long-term rutting of overburden under repeated loading. The proposed model is based on the behaviour of various parameters, such as applied cyclic deviatoric stress, number of load repetitions, loading frequencies, and on the permanent deformation characteristic of mine overburden soil. In addition, durability and environmental sustainability aspects of the treatment has also been studied to determine the optimal dose of cement.