Quantification of training load distribution in mixed martial arts athletes: A lack of periodisation and load management.

The aim of this study was to quantify typical training load and periodisation practices of MMA athletes. MMA competitors (n = 14; age = 22.4 ± 4.4 years; body mass = 71.3 ± 7.7 kg; stature = 171 ±9.9 cm) were observed during training for 8 consecutive weeks without intervention. Seven athletes were training for competitive bouts whilst the remaining 7 were not. Daily training duration, intensity (RPE), load (sRPE and segRPE), fatigue (short questionnaire of fatigue) and body region soreness (CR10 scale) were recorded. Using Bayesian analyses (BF10≥3), data demonstrate that training duration (weekly mean range = 3.9-5.3 hours), sRPE (weekly mean range = 1,287-1,791 AU), strain (weekly mean range = 1,143-1,819 AU), monotony (weekly mean range = 0.63-0.83 AU), fatigue (weekly mean range = 16-20 AU) and soreness did not change within or between weeks. Between weeks monotony (2.3 ± 0.7 AU) supported little variance in weekly training load. There were no differences in any variable between participants who competed and those who did not with the except of the final week before the bout, where an abrupt step taper occurred leading to no between group differences in fatigue. Training intensity distribution corresponding to high, moderate and low was 20, 33 and 47%, respectively. Striking drills accounted for the largest portion of weekly training time (20-32%), with MMA sparring the least (2-7%). Only striking sparring and wrestling sparring displayed statistical weekly differences in duration or load. Athletes reported MMA sparring and wrestling sparring as high intensity (RPE≥7), BJJ sparring, striking sparring and wrestling drills as moderate intensity (RPE 5-6), and striking drills and BJJ drills as low intensity (RPE≤4). We conclude that periodisation of training load was largely absent in this cohort of MMA athletes, as is the case within and between weekly microcycles

Allen’s interval algebra and smart-type environments

Allen’s interval algebra is a calculus for temporal reasoning that was introduced in 1983. Reasoning with qualitative time in Allen’s full interval algebra is nondeterministic polynomial time (NP) complete. Research since 1995 identified maximal tractable subclasses of this algebra via exhaustive computer search and also other ad-hoc methods. In 2003, the full classification of complexity for satisfiability problems over constraints in Allen’s interval algebra was established algebraically. Recent research proposed scheduling based on the Fishburn-Shepp correlation inequality for posets. This article first reviews Allen’s calculus and surrounding computational issues in temporal reasoning. We then go on to describe three potential temporal-related application areas as candidates for scheduling using the Fishburn-Shepp inequality. We also illustrate through concrete examples, and conclude the importance of FishburnShepp inequality for the suggested application areas that are the development of smart homes, intelligent conversational agents and in physiology with emphasis during time-trial physical exercise. The Fishburn-Shepp inequality will enable the development of smart type devices, which will in turn help us to have a better standard of living

Exploration and Assertion of the ‘Theory of Potential’ using Human Brain Signals

It may happen that exceptional selforganisation of oneself can lead to the breaking down of old patterns and hence the birth of a new pattern. This new pattern causes a new mindset for that individual, and thus this individual perceives the world differently while being in a higher state of mind. So far, it was posited theoretically and qualitatively by Dr. Flower that this type of manifestation occurs because previously untapped cognitive neural cells are now allowed to create a new neuronal pathway leading to a new path of cognition and human development.</jats:p

Review of exercise-induced physiological control models to explain the development of fatigue to improve sports performance and future trend

News. — Sports performance is the pursuit of excellence where a sportsman measures his or her performance quantitatively or qualitatively to move towards his or her desired goal. Several physical aspects can influence sport performance. One of these aspects is the neuromuscular factor Tsolakis and Tsolakis (2008) that arises from the relationship between the nervous system, and the musculoskeletal system. Moreover, in many sports (for example, running and cycling), the establishment of an effective rhythm will keep an athlete organised, and physically efficient for an excellent performance. Subsequently, this rhythm will impose a cadence on musculoskeletal activity, mental control as well as psychological factors. These psychological factors can be self-motivation, level of alertness and mental acuity that are the product of a number of integrated factors like physical fatigue or other unrelated sport stresses such as environmental conditions that are not within the athlete's personal control. The athlete is required to have the ability to adapt in these unexpected environmental factors. Another aspect is coaching and external support/assistance for the athlete (in terms of nutrition, sport technique, tactics and training) to the aspiring competitor for success to occur. Amidst all these factors which influence sports performance, there is one crucial factor which cannot be overlooked and it is the exercise-induced fatigue which causes a reduction in physical and mental performance. Conclusion. — Therefore, in this review, we describe and discuss the various physiological theoretical models of exercise-induced fatigue, and the way forward to assess these theories using mathematical models and analysis of biosignals