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ix FOREWORD: MILTON’S PERSONAL BEST THIS FOREWORD TELLS how I began my twenty- five years of research into Milton’s “best possession,” to help explain the meaning and range of its phrasing, and why I refer to it as his “Personal Best.” To speak of your “personal best,” be it in throwing the javelin or finishing a crossword puzzle, is to measure yourself by some wider standard so as to take satisfaction in your own prowess when at maximum extension, whilst recognizing that that best is not the world’s best. Milton spoke of De Doctrina as “this my best and

better at the sprint/track disciplines may obtain an advantage in the decathlon. KEYWORDS: athletics, cluster analysis, personal best, classical scaling Introduction The decathlon takes place over two days and consists of a combination of ten track and field disciplines the order of which is invariant: day 1: 100 m race (100m), long jump (LJ), shot put (SP), high jump (HJ) and 400 m race (400m); day 2: 110 m hurdles (110mH), discus (DT), pole vault (PV), javelin (JT) and 1500 m race (1500m). Actual performance results in terms of time in the track disciplines and

lengths of the femur and tibia to assess the upper and lower leg lengths, respectively. Additionally, we calculated the total length of the femur + tibia to assess the overall leg bone length, as in previous studies ( Laumets et al., 2017 ; Mooses et al., 2015 ). Methods Participants Forty-two Japanese male endurance runners (age: 20.0 ± 1.0 years, body height: 169.6 ± 5.6 cm, body mass: 56.4 ± 5.1 kg) participated in this study. They were all well-trained, being involved in regular training and competition. Their personal best times of the 5000-m race within the past

between multiple trials. Based on the aforementioned method, this research aimed to clarify stride adjustment in the approach of the 400 m‐H, and to examine the relationship with 400 m‐H performance. Methods Participants Seven male 400‐m hurdlers (body height 1.74 ± 3.39 m, body mass 63.5 ± 2.24 kg) volunteered for this study. The performance index for each participant was their 400 m‐H personal best (400 mHPB; 53.66 ± 1.21 s). In addition, the 400‐m running personal best (400 mPB; 50.58 ± 1.55 s) was used as an indicator of the running ability in the race without

.6 ± 6.9 cm; body mass: 74.9 ± 5.2 kg). The inclusion criterion required at least 6 years of training experience in the 60, 100 or 200 m sprint events. According to the aims of the study, the “faster” group included members of the Slovenian national team competing at the international level ( n = 6) and the “slower” group consisted of sprinters from regional clubs competing at the national level ( n = 6). Differences between the groups were confirmed by comparing mean personal best times in official 60 m (faster: 6.87 ± 0.13 s; slower: 6.98 ± 0.05 s) and 100 m

particles are chosen randomly. Each particle’s position is updated at each iteration step according to its own personal best position and the best solution of the swarm. When a particle takes the entire population as its topological neighbors, the best value is a global best and is called gbest . All particles can share information about the search space. Representing a possible solution to the optimization problem, each particle moves in the direction of its best solution and the global best position discovered by any particles in the swarm. The evolution of the swarm

male athletes of national and international levels in 800 m competing in elite, junior, and youth categories, with personal best ranging from 1:43 to 1:58 min:ss (average 1:52 min:ss) participated in this study (age: 22.9 ± 5.3 years; body height: 175.2 ± 5.5 cm; body mass: 62.9 ± 4.4 kg). Two of them were classified 1st and 2nd in the national championship and national ranking; they had also participated in London 2012 and Rio 2016 Olympic Games. All athletes had completed strength-training programs in the past and were familiarized with testing procedures. The


Introduction. The main goal of the study was to examine the changes in the scores achieved by a group of the best decathletes in Poland and in the world over the duration of their entire sports careers. Material and methods. The study examined the careers of 25 top decathletes in Poland and 25 top decathletes in the world who achieved their best scores in the decathlon in the years 1985-2015. Changes in their performance were analysed using three research protocols, which explored the relationships between the decathletes’ performance and their age, the number of years of training completed, and the number of years elapsed before and after the decathletes achieved their personal best scores. In order to analyse the data, some basic descriptive statistics, performed segmented regression, and computed fixed-base and chain indices were calculated. Conclusions. The findings of the current study indicate that decathletes achieve the best outcomes between the ages of 25 and 30. Furthermore, it was found that the time when the personal best scores were attained was preceded by the greatest increases in the level of performance and followed by the largest decreases in performance. Determining the way performance outcomes change in particular years of the career of a decathlete can help manage their training in different phases of their career more effectively.

Studies in the Historical Reception of American Cinema


Among the data clustering algorithms, k-means (KM) algorithm is one of the most popular clustering techniques due to its simplicity and efficiency. However, k-means is sensitive to initial centers and it has the local optima problem. K-harmonic-means (KHM) clustering algorithm solves the initialization problem of k-means algorithm, but it also has local optima problem. In this paper, we develop a new algorithm for solving this problem based on an improved version of particle swarm optimization (IPSO) algorithm and KHM clustering. In the proposed algorithm, IPSO is equipped with Cuckoo Search algorithm and two new concepts used in PSO in order to improve the efficiency, fast convergence and escape from local optima. IPSO updates positions of particles based on a combination of global worst, global best with personal worst and personal best to dynamically be used in each iteration of the IPSO. The experimental result on five real-world datasets and two artificial datasets confirms that this improved version is superior to k-harmonic means and regular PSO algorithm. The results of the simulation show that the new algorithm is able to create promising solutions with fast convergence, high accuracy and correctness while markedly improving the processing time.