Interval training intensity affects energy intake compensation in obese men

Abstract PURPOSE: Compensatory responses may attenuate the effectiveness of exercise training in weight management. The aim of this study was to compare the effect of moderate- and high-intensity interval training on eating behavior compensation. METHODS: Using a crossover design, 10 overweight and obese men participated in 4-week moderate (MIIT) and high (HIIT) intensity interval training. MIIT consisted of 5-min cycling stages at ± 20% of mechanical work at 45%VO(2)peak, and HIIT consisted of alternate 30-s work at 90%VO(2)peak and 30-s rests, for 30 to 45 min. Assessments included a constant-load exercise test at 45%VO(2)peak for 45 min followed by 60-min recovery. Appetite sensations were measured during the exercise test using a Visual Analog Scale. Food preferences (liking and wanting) were assessed using a computer-based paradigm, and this paradigm uses 20 photographic food stimuli varying along two dimensions, fat (high or low) and taste (sweet or nonsweet). An ad libitum test meal was provided after the constant-load exercise test. RESULTS: Exercise-induced hunger and desire to eat decreased after HIIT, and the difference between MIIT and HIIT in desire to eat approached significance (p = .07). Exercise-induced liking for high-fat nonsweet food tended to increase after MIIT and decreased after HIIT (p = .09). Fat intake decreased by 16% after HIIT, and increased by 38% after MIIT, with the difference between MIIT and HIIT approaching significance (p = .07). CONCLUSIONS: This study provides evidence that energy intake compensation differs between MIIT and HIIT.

Comparing fat oxidation in an exercise test with moderate-intensity interval training

This study compared fat oxidation rate from a graded exercise test (GXT) with a moderate-intensity interval training session (MIIT) in obese men. Twelve sedentary obese males (age 29 ± 4.1 years; BMI 29.1 ± 2.4 kg·m-2; fat mass 31.7 ± 4.4 %body mass) completed two exercise sessions: GXT to determine maximal fat oxidation (MFO) and maximal aerobic power (VO2max), and an interval cycling session during which respiratory gases were measured. The 30-min MIIT involved 5-min repetitions of workloads 20% below and 20% above the MFO intensity. VO2max was 31.8 ± 5.5 ml·kg-1·min-1 and all participants achieved ≥ 3 of the designated VO2max test criteria. The MFO identified during the GXT was not significantly different compared with the average fat oxidation rate in the MIIT session. During the MIIT session, fat oxidation rate increased with time; the highest rate (0.18 ± 0.11 g·min- 1) in minute 25 was significantly higher than the rate at minute 5 and 15 (p ≤ 0.01 and 0.05 respectively). In this cohort with low aerobic fitness, fat oxidation during the MIIT session was comparable with the MFO determined during a GXT. Future research may consider if the varying workload in moderate-intensity interval training helps adherence to exercise without compromising fat oxidation.

Simple Coding Scheme For Modular Arithmetic

A dual representation scheme for performing arithmetic modulo an arbitrary integer M is presented. The coding scheme maps each integer N in the range 0 <= N < M into one of two representations, each being identified by its most significant bit. The encoding of numbers is straightforward and the problem of checking for unused combinations is eliminated.

On the Cubicity of Interval Graphs

A k-cube (or ``a unit cube in k dimensions'') is defined as the Cartesian product R-1 x . . . x R-k where R-i (for 1 <= i <= k) is an interval of the form [a(i), a(i) + 1] on the real line. The k-cube representation of a graph G is a mapping of the vertices of G to k-cubes such that the k-cubes corresponding to two vertices in G have a non-empty intersection if and only if the vertices are adjacent. The cubicity of a graph G, denoted as cub(G), is defined as the minimum dimension k such that G has a k-cube representation. An interval graph is a graph that can be represented as the intersection of intervals on the real line - i. e., the vertices of an interval graph can be mapped to intervals on the real line such that two vertices are adjacent if and only if their corresponding intervals overlap. We show that for any interval graph G with maximum degree Delta, cub(G) <= inverted right perpendicular log(2) Delta inverted left perpendicular + 4. This upper bound is shown to be tight up to an additive constant of 4 by demonstrating interval graphs for which cubicity is equal to inverted right perpendicular log(2) Delta inverted left perpendicular.

A new parallel algorithm for parsing arithmetic infix expressions

A new parallel algorithm for transforming an arithmetic infix expression into a par se tree is presented. The technique is based on a result due to Fischer (1980) which enables the construction of the parse tree, by appropriately scanning the vector of precedence values associated with the elements of the expression. The algorithm presented here is suitable for execution on a shared memory model of an SIMD machine with no read/write conflicts permitted. It uses O(n) processors and has a time complexity of O(log2n) where n is the expression length. Parallel algorithms for generating code for an SIMD machine are also presented.

Effect of defoliation interval and height on the growth and quality of Arachis pintoi cv. Amarillo

The effect of defoliation on Amarillo (Arachis pintoi cv. Amarillo) was studied in a glasshouse and in mixed swards with 2 tropical grasses. In the glasshouse, Amarillo plants grown in pots were subjected to a 30/20°C or 25/15°C temperature regime and to defoliation at 10-, 20- or 30-day intervals for 60 days. Two field plot studies were conducted on Amarillo with either irrigated kikuyu (Pennisetum clandestinum) in autumn and spring or dryland Pioneer rhodes grass (Chloris gayana) over summer and autumn. Treatments imposed were 3 defoliation intervals (7, 14 and 28 days) and 2 residual heights (5 and 10 cm for kikuyu; 3 and 10 cm for rhodes grass) with extra treatments (56 days to 3 cm for both grasses and 21 days to 5 cm for kikuyu). Defoliation interval had no significant effect on accumulated Amarillo leaf dry matter (DM) at either temperature regime. At the higher temperature, frequent defoliation reduced root dry weight (DW) and increased crude protein (CP) but had no effect on stolon DW or in vitro organic matter digestibility (OMD). On the other hand, at the lower temperature, frequent defoliation reduced stolon DW and increased OMD but had no effect on root DW or CP. Irrespective of temperaure and defoliation, water-soluble carbohydrate levels were higher in stolons than in roots (4.70 vs 3.65%), whereas for starch the reverse occured (5.37 vs 9.44%). Defoliating the Amarillo-kikuyu sward once at 56 days to 3 cm produced the highest DM yield in autumn and sprong (582 and 7121 kg/ha DM, respectively), although the Amarillo component and OMD were substantially reduced. Highest DM yields (1726 kg/ha) were also achieved in the Amarillo-rhodes grass sward when defoliated every 56 days to 3 cm, although the Amarillo component was unaffected. In a mixed sward with either kikuyu or rhodes grass, the Amarillo component in the sward was maintained up to a 28-day defoliation interval and was higher when more severely defoliated. The results show that Amarillo can tolerate frequent defoliation and that it can co-exist with tropical grasses of differing growth habits, provided the Amarillo-tropical grass sward is subject to frequent and severe defoliation.

Tarinakontekstin vaikutus sanojen muistamiseen kompleksisessa työmuistitehtävässä

Objectives. The sentence span task is a complex working memory span task used for estimating total working memory capacity for both processing (sentence comprehension) and storage (remembering a set of words). Several traditional models of working memory suggest that performance on these tasks relies on phonological short-term storage. However, long-term memory effects as well as the effects of expertise and strategies have challenged this view. This study uses a working memory task that aids the creation of retrieval structures in the form of stories, which have been shown to form integrated structures in longterm memory. The research question is whether sentence and story contexts boost memory performance in a complex working memory task. The hypothesis is that storage of the words in the task takes place in long-term memory. Evidence of this would be better recall for words as parts of sentences than for separate words, and, particularly, a beneficial effect for words as part of an organized story. Methods. Twenty stories consisting of five sentences each were constructed, and the stimuli in all experimental conditions were based on these sentences and sentence-final words, reordered and recombined for the other conditions. Participants read aloud sets of five sentences that either formed a story or not. In one condition they had to report all the last words at the end of the set, in another, they memorised an additional separate word with each sentence. The sentences were presented on the screen one word at a time (500 ms). After the presentation of each sentence, the participant verified a statement about the sentence. After five sentences, the participant repeated back the words in correct positions. Experiment 1 (n=16) used immediate recall, experiment 2 (n=21) both immediate recall and recall after a distraction interval (the operation span task). In experiment 2 a distracting mental arithmetic task was presented instead of recall in half of the trials, and an individual word was added before each sentence in the two experimental conditions when the participants were to memorize the sentence final words. Subjects also performed a listening span task (in exp.1) or an operation span task (exp.2) to allow comparison of the estimated span and performance in the story task. Results were analysed using correlations, repeated measures ANOVA and a chi-square goodness of fit test on the distribution of errors. Results and discussion. Both the relatedness of the sentences (the story condition) and the inclusion of the words into sentences helped memory. An interaction showed that the story condition had a greater effect on last words than separate words. The beneficial effect of the story was shown in all serial positions. The effects remained in delayed recall. When the sentences formed stories, performance in verification of the statements about sentence context was better. This, as well as the differing distributions of errors in different experimental conditions, suggest different levels of representation are in use in the different conditions. In the story condition, the nature of these representations could be in the form of an organized memory structure, a situation model. The other working memory tasks had only few week correlations to the story task. This could indicate that different processes are in use in the tasks. The results do not support short-term phonological storage, but instead are compatible with the words being encoded to LTM during the task.

Modeling dynamic demand response using Monte Carlo simulation and interval mathematics for boundary estimation

With the rapid development of various technologies and applications in smart grid implementation, demand response has attracted growing research interests because of its potentials in enhancing power grid reliability with reduced system operation costs. This paper presents a new demand response model with elastic economic dispatch in a locational marginal pricing market. It models system economic dispatch as a feedback control process, and introduces a flexible and adjustable load cost as a controlled signal to adjust demand response. Compared with the conventional “one time use” static load dispatch model, this dynamic feedback demand response model may adjust the load to a desired level in a finite number of time steps and a proof of convergence is provided. In addition, Monte Carlo simulation and boundary calculation using interval mathematics are applied for describing uncertainty of end-user's response to an independent system operator's expected dispatch. A numerical analysis based on the modified Pennsylvania-Jersey-Maryland power pool five-bus system is introduced for simulation and the results verify the effectiveness of the proposed model. System operators may use the proposed model to obtain insights in demand response processes for their decision-making regarding system load levels and operation conditions.

Interval Data Classification under Partial Information: A Chance-Constraint Approach

This paper presents a Chance-constraint Programming approach for constructing maximum-margin classifiers which are robust to interval-valued uncertainty in training examples. The methodology ensures that uncertain examples are classified correctly with high probability by employing chance-constraints. The main contribution of the paper is to pose the resultant optimization problem as a Second Order Cone Program by using large deviation inequalities, due to Bernstein. Apart from support and mean of the uncertain examples these Bernstein based relaxations make no further assumptions on the underlying uncertainty. Classifiers built using the proposed approach are less conservative, yield higher margins and hence are expected to generalize better than existing methods. Experimental results on synthetic and real-world datasets show that the proposed classifiers are better equipped to handle interval-valued uncertainty than state-of-the-art.

Assessing the effectiveness of High Intensity Interval Training (HIIT) for smoking cessation in women: HIIT to quit study protocol

Background: Smoking and physical inactivity are major risk factors for heart disease. Linking strategies that promote improvements in fitness and assist quitting smoking has potential to address both these risk factors simultaneously. The objective of this study is to compare the effects of two exercise interventions (high intensity interval training (HIIT) and lifestyle physical activity) on smoking cessation in female smokers. Method/design: This study will use a randomised controlled trial design. Participants: Women aged 18–55 years who smoke ≥ 5 cigarettes/day, and want to quit smoking. Intervention: all participants will receive usual care for quitting smoking. Group 1 - will complete two gym-based supervised HIIT sessions/week and one home-based HIIT session/week. At each training session participants will be asked to complete four 4-min (4 × 4 min) intervals at approximately 90 % of maximum heart rate interspersed with 3- min recovery periods. Group 2 - participants will receive a resource pack and pedometer, and will be asked to use the 10,000 steps log book to record steps and other physical activities. The aim will be to increase daily steps to 10,000 steps/day. Analysis will be intention to treat and measures will include smoking cessation, withdrawal and cravings, fitness, physical activity, and well-being. Discussion: The study builds on previous research suggesting that exercise intensity may influence the efficacy of exercise as a smoking cessation intervention. The hypothesis is that HIIT will improve fitness and assist women to quit smoking.

Arithmetic Algorithms in a Negative Base

Algorithms are described for the basic arithmetic operations and square rooting in a negative base. A new operation called polarization that reverses the sign of a number facilitates subtraction, using addition. Some special features of the negative-base arithmetic are also mentioned.

Computing a matrix symmetrizer exactly using modified multiple modulus residue arithmetic

A symmetric solution X satisfying the matrix equation XA = AtX is called a symmetrizer of the matrix A. A general algorithm to compute a matrix symmetrizer is obtained. A new multiple-modulus residue arithmetic called floating-point modular arithmetic is described and implemented on the algorithm to compute an error-free matrix symmetrizer.

An integer arithmetic method to compute generalized matrix inverse and solve linear equations exactly

An algorithm that uses integer arithmetic is suggested. It transforms anm ×n matrix to a diagonal form (of the structure of Smith Normal Form). Then it computes a reflexive generalized inverse of the matrix exactly and hence solves a system of linear equations error-free.

Cubicity of Interval Graphs and the Claw Number

Let G(V, E) be a simple, undirected graph where V is the set of vertices and E is the set of edges. A b-dimensional cube is a Cartesian product l(1) x l(2) x ... x l(b), where each l(i) is a closed interval of unit length on the real line. The cub/city of G, denoted by cub(G), is the minimum positive integer b such that the vertices in G can be mapped to axis parallel b-dimensional cubes in such a way that two vertices are adjacent in G if and only if their assigned cubes intersect. An interval graph is a graph that can be represented as the intersection of intervals on the real line-i.e. the vertices of an interval graph can be mapped to intervals on the real line such that two vertices are adjacent if and only if their corresponding intervals overlap. Suppose S(m) denotes a star graph on m+1 nodes. We define claw number psi(G) of the graph to be the largest positive integer m such that S(m) is an induced subgraph of G. It can be easily shown that the cubicity of any graph is at least log(2) psi(G)]. In this article, we show that for an interval graph G log(2) psi(G)-]<= cub(G)<=log(2) psi(G)]+2. It is not clear whether the upper bound of log(2) psi(G)]+2 is tight: till now we are unable to find any interval graph with cub(G)> (log(2)psi(G)]. We also show that for an interval graph G, cub(G) <= log(2) alpha], where alpha is the independence number of G. Therefore, in the special case of psi(G)=alpha, cub(G) is exactly log(2) alpha(2)]. The concept of cubicity can be generalized by considering boxes instead of cubes. A b-dimensional box is a Cartesian product l(1) x l(2) x ... x l(b), where each I is a closed interval on the real line. The boxicity of a graph, denoted box(G), is the minimum k such that G is the intersection graph of k-dimensional boxes. It is clear that box(G)<= cub(G). From the above result, it follows that for any graph G, cub(G) <= box(G)log(2) alpha]. (C) 2010 Wiley Periodicals, Inc. J Graph Theory 65: 323-333, 2010