This diploma thesis deals with the issue of injury prevention in modern pentathletes. It sets itself the goal of analyzing the current epidemiological situation of injuries in modern pentathlon as well as the current preventive strategies of athletes, including the evaluation of their impact on the incidence of injuries. The nature of the work is a prospective cohort study. A modified version of the validated "Oslo Sports Trauma Research Center questionnaire on health problems" was used. This questionnaire was sent to athletes every week for 10 weeks using the Qualtrics questionnaire software. The study involved 43 probands from 15 countries, ie 4.3% of athletes registered with the UIPM (Union Internationale de Pentathlon Moderne). A total of 34 new injuries and overloads were recorded in 27 probands. The incidence rate of injuries of 3.1 / 1000 h of active sports was found. The most frequently reported segments of injury were the shoulder (14.7%), ankle (14.7%), knee and shin (both 11.8%). The ratio of the lower limb to upper limb injuries was found to be 3: 1. Most injuries were associated with running discipline (46%). The most commonly used injury prevention strategy was stretching, followed by strength training, massages, and consistent warm-up before training. The mean warm-up duration showed...

The current investigation was an examination of the kinetic and kinematic characteristics of the back squat using accentuated eccentric loading (AEL) and cluster set programming strategies. Trained male subjects (age = 26.1 ± 4.1 years, height = 183.5 ± 4.3 cm, body mass = 92.5 ± 10.5 kg, back squat to body mass ratio = 1.8 ± 0.3) volunteered to complete four different load condition sessions involving traditionally loaded straight sets (TL), traditionally loaded cluster sets (TLC), AEL cluster sets (AEC), and AEL straight sets where only the first repetition of each set used eccentric overload (AEL1). The use of AEL increased eccentric work (WECC) and eccentric rate of force development (RFDECC) but did not result in the expected potentiation of subsequent concentric output. Interrepetition rest, however, appears to have the largest influence on concentric peak power (PP), rate of force development (RFDCON), and average velocity (MV). Additionally, the current study was an investigation of the efficacy of novel methods of ultrasonography technique that can be applied to monitoring training response. Compared to lying measures of the vastus lateralis (VL), standing ultrasonography measures of muscle thickness (MT), pennation angle (PA), and cross-sectional area (CSA) were more strongly and abundantly correlated with dynamic and isometric strength performance. Finally, the present study was an exploration of the genetic underpinnings of performance outcomes and muscle phenotypic characteristics. The polymorphisms of two candidate genes (ACTN3, ACE) typical of strength-power athletes were used. ACTN3 RR tended to result in greater type II fiber CSA and alter maximal strength, while ACE DD tended to influence RFD through the presence of more favorable type II-to-type I CSA ratios. Overall, the current investigation provided valuable insight into the characteristics of advanced programming tactics. Furthermore, the ultrasonography measurement and genetic aspects of the current investigation may serve as a framework to inform monitoring practice and generate hypotheses related to the training process.

Myostatin (GDF-8) is the chief chalone in skeletal muscle and negatively controls adult skeletal muscle growth. The role of myostatin during overload-induced hypertrophy of adult muscle is unclear. We tested the hypothesis that overloaded adult rodent skeletal muscle would result in reduced myostatin protein levels. Overload-induced hypertrophy was accomplished by unilateral tenotomy of the gastrocnemius tendon in male adult Sprague-Dawley rats followed by a two-week period of compensatory overload of the plantaris and soleus muscles. Western blot analysis was performed to evaluate changes in active, latent and precursor myostatin protein levels. Significant hypertrophy was noted in the plantaris (494 ± 29 vs. 405 ± 15 mg, p < 0.05) and soleus (289 ± 12 vs. 179 ± 37 mg, p < 0.05) muscles following overload. Overloaded soleus muscle decreased the concentration of active myostatin protein by 32.7 ± 9.4% (p < 0.01) while the myostatin precursor protein was unchanged. Overloaded plantaris muscle decreased the concentration of active myostatin protein by 28.5 ± 8.5% (p < 0.01) while myostatin precursor levels were reduced by 17.5 ± 5.9% (p < 0.05). Myostatin latent complex concentration decreased in the overloaded soleus and plantaris muscle by 15.0 ± 5.9% and 70.0 ± 2.3% (p < 0.05), respectively. These data support the hypothesis that the myostatin signaling pathway in overloaded muscles is generally downregulated and contributes to muscle hypertrophy. Plasma concentrations of total and active myostatin proteins were similar in overloaded and control animals and averaged 8865 ± 526 pg/ml and 569 ± 28 pg/ml, respectively. Tissue levels of BMP-1, an extracellular proteinase that converts myostatin to its active form, also decreased in overloaded soleus and plantaris muscles by 40.4 ± 12.9% and 32.9 ± 6.9% (p < 0.01), respectively. These data support the hypothesis that local, rather than systemic, regulation of myostatin contributes to the growth of individual muscles, and that an association exists between the extracellular matrix proteinase BMP-1 and the amount of active myostatin in overloaded muscles.

Stress and overload are strongly associated with unsafe behaviour, which motivated various studies to detect them automatically in workplaces. This study aims to advance safety research by developing a data-driven stress and overload detection method. An unsupervised deep learning-based anomaly detection method is developed to detect stress. The proposed method performs with convolutional neural network encoder-decoder and long short-term memory equipped with an attention layer. Data from a field experiment with 18 participants was used to train and test the developed method. The field experiment was designed to include a pre-defined sequence of activities triggering mental and physical stress, while a wristband biosensor was used to collect physiological signals. The collected contextual and physiological data were pre-processed and then resampled into correlation matrices of 14 features. Correlation matrices are used as an input to the unsupervised Deep Learning (DL) based anomaly detection method. The developed method is validated, offering accuracy and F-measures close to 0.98. The technique employed captures the input data attributes correlation, promoting higher interpretability of the DL method for easier comprehension. Over-reliance on uncertain absolute truth, the need for a high number of training samples, and the requirement of a threshold for detecting anomalies are identified as shortcomings of the proposed method. To overcome these shortcomings, an Adaptive Neuro-Fuzzy Inference System (ANFIS) was designed and developed. While the ANFIS method did not improve the overall accuracy, it outperformed the DL-based method in detecting anomalies precisely. The overall performance of the ANFIS method is better than the DL-based method for the anomalous class, and the method results in lower false alarms. However, the DL-based method is suitable for circumstances where false alarms are tolerated. / Dissertation / Doctor of Philosophy (PhD)

Recently, technological developments have affected most lifestyles, especially with the growth in Internet usage. Internet applications highlight the E-commerce capabilities and applications which are now available everywhere; they receive a great number of users on a 24-7 basis because online services are easy to use, faster and cheaper to acquire. Thus E-commerce web sites have become crucial for companies to increase their revenues. This importance has identified certain effective requirements needed from the performance of these applications. In particular, if the web server is overloaded, poor performance can result, due to either a huge rate of requests being generated which are beyond the server¿s capacity, or due to saturation of the communication links capacity which connects the web server to the network. Recent researches consider the overload issue and explore different mechanisms for managing the performance of E-commerce applications under overload condition. This thesis proposes a formal approach in order to investigate the effects of the extreme load and the number of dropped requests on the performance of E- III commerce web servers. The proposed approach is based on the class-based priority scheme that classifies E-commerce requests into different classes. Because no single technique can solve all aspects of overload problems, this research combines several techniques including: admission control mechanism, session-based admission control, service differentiation, request scheduling and queuing model-based approach. Request classification is based on the premise that some requests (e.g. buy) are generally considered more important than others (e.g. browse or search). Moreover, this research considers the extended models from Priority Scheduling Mechanism (PSM). These models add a new parameter, such as a review model or modify the basic PSM to low priority fair model, after the discovery of ineffectiveness with low priority customers or to add new features such as portal models. The proposed model is formally specified using the ¿ -calculus in early stage of models design and a multi-actor simulation was developed to reflect the target models as accurately as possible and is implemented as a Java-based prototype system. A formal specification that captures the essential PSM features while keeping the performance model sufficiently simple is presented. Furthermore, the simplicity of the UML bridges the gap between ¿-calculus and Java programming language. IV There are many metrics for measuring the performance of E-commerce web servers. This research focuses on the performance of E-commerce web servers that refer to the throughput, utilisation, average response time, dropped requests and arrival rate. A number of experiments are conducted in order to test the performance management of the proposed approaches.

The conversion of audio from the digital to analog domain has the potential to result in distortion due to converter overload. This occurs because some peaks in the signal cannot be defined digitally and only become problematic during the conversion into the analog domain, exceeding the level that can be represented by the converter, causing it to overload. Although True Peak limiting and metering can prevent and monitor this issue, some professional mastering engineers choose not to do so. The study tested claims made against overload prevention, including the adequacy of headroom in modern D/A converters and the inaudibility of the distortion caused by overload. Preference was also added to the audibility claim. Measurements show that there is not enough headroom in modern D/A converters to avoid overload, but the distortion created by overload is generally inaudible in an uncompressed WAVE format hard rock song. Additionally, there is no clear preference. The measurements found that overload only occurs when the device's volume is raised to its maximum output.

No description available.

No description available.

No description available.

No description available.