Effects of high-intensity position-specific drills on physical and technical-skill performance in young soccer players

Soccer is the most widely played sport in the world, and physical preparation for soccer players has been extensively researched over the years. As the average intensity of a soccer match is close to 80-90% of maximal heart rate (HRmax), it is necessary to train at or above this intensity. Thus, high-intensity interval running and small-sided games are often used to improve aerobic capacity and repeated sprint ability (RSA). However, neither of these approaches consider positional variations in the frequency and type of specific technical skills required in real match situations. Thus, the purpose of this study was to investigate the feasibility, and short-term effects of a novel position-specific conditioning training (PSCT) on the physical and technical abilities of young soccer players. This study recruited 15 male Vietnamese youth soccer players (16.1 ± 0.4 years, 171.7 ± 4.8 cm, 63.9 ± 3.8 kg) who frequently played in youth national tournaments. PSCT consisted of a specific drill for attackers, defenders and wingers, respectively. The intensity and duration were designed to be the same for all three drills (i.e., 4 × 4-min at 90-95% HRmax, separated by 4-min active recovery at 70% HRmax), but differentiated by the technical and tactical actions performed during high-intensity efforts and pitch location. All players participated in a 3-week control period of high-volume training, followed by a 3- week intervention period with PSCT drills added to usual team practice and matches twice a week. Criterion measures included Yo-Yo intermittent recovery test – level 1 (YYIRT1), repeated sprint ability (RSA) assessed by the total time of 6 x 30m sprint with 30-s passive recovery, and 10m and 30m sprint time. The Loughborough soccer passing test (LSPT) was used to assess the players’ technical skills in a fatigued and non-fatigued state. These measures were taken at baseline, after the control period and after the intervention period. The results showed that PSCT drills induced a desirable intensity for effective conditioning purpose (89.0 ± 2.1% HRmax) with low inter-player variability (CV = 2.4%). The weekly total training volume in terms of the distance covered during the control period was 45.45 ± 3.82 km, which was 11.97 km greater (P=0.017, ES= 1.82) than that of the intervention period (33.48 ± 6.40km). The distance covered in the YYIRT1 increased (P0.05) were observed from the baseline (26.21 ± 0.5 s) to post-control period (26.26 ± 0.8 s) and postintervention period (26.32 ± 0.8 s). This was also the case for 10m sprint time (baseline: 1.80 ± 0.1 s, post-control: 1.80 ± 0.1 s, post-intervention: 1.77 ± 0.1 s) and 30m sprint time (4.20 ± 0.1 s, 4.26 ± 0.1 s, 4.26 ± 0.2 s). No significant changes (p\u3e0.05) were found for any parameter of the LSPT over time (from baseline to post-intervention) for both fatigued and non-fatigued conditions. These results confirmed the feasibility of PSCT as a novel high-intensity training for soccer players, but it did not affect the physical and technical measures investigated in the present study in the time frame. Future research should further investigate the use of PSCT as a position-specific test and/or a novel conditioning approach by comparing PSCT to small sided games (SSG) or other forms of HIIT without ball contact in longer-term intervention

Development of Electrolytes for Si-Graphite Composite Electrodes

The performance of Si-graphite/Li cells and Si-graphite/NMC111 cells has been investigated in 1.2 M LiPF6 /EC:DEC (1/1, w/w) with different electrolyte additives including LiNO3, FEC, and MEC. The addition of additives into electrolytes result in a significant improvement in capacity retention compared to the standard electrolyte for Si-graphite/Li cells. The cells cycled with electrolyte containing 0.5 wt% LiNO3, 5–10 wt% MEC or 10 wt% FEC have high capacity retention, at least 88%, while the cells cycled with standard electrolyte have lower capacity retention, 64%, after 100 cycles. Investigation of Si-graphite/NCM111 cells reveals that the cells cycled in electrolyte containing 0.5 wt% LiNO3 have better capacity retention than cells cycled with 10 wt% FEC, 57.9% vs. 44.6%, respectively. The combination of 10% MEC and LiNO3 further improves the capacity retention of the Si-graphite/NCM111 full cells to 79.9% after 100 cycles which is highest among the electrolytes investigated. Ex-situ surface analyses by XPS and IR-ATR have been conducted to provide a fundamental understanding the composition of the solid-electrolyte interphase (SEI) and its correlation to cycling performance

Factors related to Preoperative Anxiety among Patients undergoing Abdominal Surgery in Phu Tho Province General Hospital, Vietnam

บทคัดย่อวัตถุประสงค์: เพื่อศึกษาภาวะวิตกกังวลก่อนการผ่าตัดและทดสอบความสัมพันธ์กับปัจจัยท

Harnessing meta-learning via probabilistic modelling and trajectory optimisation

Meta-learning has recently flourished as one of the most promising transfer learning techniques that can adapt quickly to a new task, even if that task consists of a limited number of training examples. The main idea of meta-learning is to use a meta-parameter to model the shared structure between many observed tasks and utilise the knowledge gained from such modelling to facilitate the learning for unobserved tasks. Despite steady progress with many remarkable state-of-the-art results, existing meta-learning algorithms are often fragile due to the lack of studies in prediction uncertainty and generalisation for unseen tasks. In addition, little is known about how tasks are related to each other, potentially leading to sub-optimal solutions due to the assumption that tasks are evenly distributed – which is hardly true in practice. This thesis, therefore, aims to address such problems through the lenses of probabilistic modelling and optimisation. In particular, the thesis proposes to (i) integrate variational inference into meta-learning that considers the epistemic uncertainty into the modelling to reduce calibration errors and overfitting induced by meta-learning models, (ii) derive a PAC-Bayes upper-bound of errors evaluated on both seen and unseen tasks to enable the study of theoretical generalisation in meta-learning and use that bound to formulate a loss function applied in the training of different meta-learning methods, (iii) model tasks via a variant of Gaussian latent Dirichlet allocation and utilise the newly-obtained representation for task selection to make training more efficient, and (iv) adopt trajectory optimisation from optimal control to determine the re-weighting factor of each training task to optimise the training process of meta-learning. The results of these studies improve the robustness and provide an insightful understanding of meta-learning, and thus, enable further development of practical meta-learning approaches.Thesis (Ph.D.) -- University of Adelaide, School of Computer Science, 202

PREPARATION OF POLYMER COMPOSITES BASED ON UNSATURATED POLYESTER REINFORCED BY NATURAL FIBER AND CELLULOSE MICROFIBER FROM LUNG WASTE IN NGHE AN

Unsaturated polyester composites reinforced by glass fiber and by hybrid reinforcementglass fiber - lung fiber with cellulose microfiber (MFC) were prepared and investigated. Tensileand flexural strengths of material reached the highest value at polymer composite with 48 %wglass fiber mat and 0.3 %w MFC (208.33 MPa and 243.6 0 MPa), while the highest impactstrength reached 212.48 kJ/m2 at composite containing 48 %w glass fiber but 0.5 %w MFC.Especially, with 0.3 %w MFC, the tensile fatigue cycle to failure of composite processed byvacuum bag remarkably increased, 140.28 % at composite with 48 %w glass fiber and 265.63 %at hybrid composite reinforced by glass fiber/lung fiber, compared to samples without MFC

Improved mitochondrial amino acid substitution models for metazoan evolutionary studies

Abstract Background Amino acid substitution models play an essential role in inferring phylogenies from mitochondrial protein data. However, only few empirical models have been estimated from restricted mitochondrial protein data of a hundred species. The existing models are unlikely to represent appropriately the amino acid substitutions from hundred thousands metazoan mitochondrial protein sequences. Results We selected 125,935 mitochondrial protein sequences from 34,448 species in the metazoan kingdom to estimate new amino acid substitution models targeting metazoa, vertebrates and invertebrate groups. The new models help to find significantly better likelihood phylogenies in comparison with the existing models. We noted remarkable distances from phylogenies with the existing models to the maximum likelihood phylogenies that indicate a considerable number of incorrect bipartitions in phylogenies with the existing models. Finally, we used the new models and mitochondrial protein data to certify that Testudines, Aves, and Crocodylia form one separated clade within amniotes. Conclusions We introduced new mitochondrial amino acid substitution models for metazoan mitochondrial proteins. The new models outperform the existing models in inferring phylogenies from metazoan mitochondrial protein data. We strongly recommend researchers to use the new models in analysing metazoan mitochondrial protein data

DEVELOPMENT AND EVALUATION OF ORAL SUSTAINED-RELEASE RANITIDINE DELIVERY SYSTEM BASED ON BACTERIAL NANOCELLULOSE MATERIAL PRODUCED BY KOMAGATAEIBACTER XYLINUS

Objective: The short biological half-life (2-3 h) and low bioavailability (50 %) of ranitidine (RAN) following oral administration favor the development of a controlled release system. This study was aimed to develop and in vitro evaluate oral sustained-release RAN delivery system based on the bacterial nanocellulose material (BNM) produced by Komagataeibacter xylinus (K. xylinus) from selected culture media. Methods: BNMs are biosynthesized by K. xylinus in the standard medium (SM) and coconut water (CW). RAN was loaded in BNMs by the absorption method. The structural and physicochemical properties of BNMs and BNMs-RAN were evaluated via swelling behavior, FTIR, and FESEM techniques. Moreover, the effect of BNMs on RAN release profile and release kinetics was analyzed and evaluated. Results: The amount of loaded RAN or entrapment efficacy for BNM-CW is higher than for BNM-SM. The BNM-SM-RAN and BNM-CW-RAN exhibited a decreased initial burst release system followed by a prolonged RAN release up to 24 h in relation to the commercial tablets containing RAN. The RAN release from these formulations was found higher in the SGF medium than that of in SIF medium. RAN released from these formulations was found to follow the Korsmeyer-Peppas model and diffusion sustained drug release mechanism. The sustained release of RAN from BNM-SM-RAN was slower than for RAN from BNM-CW-RAN, but the mechanism of sustained RAN release was the same. Conclusion: Oral sustained-release RAN delivery system based on BNMs was successfully prepared and evaluated for various in vitro parameters. The biopolymers like BNM-SM and BNM-CW could be utilized to develop oral sustained RAN release dosage form

FABRICATION, EVALUATION OF DRUG LOADING CAPABILITY AND CHARACTERIZATION OF 3D-NANO-CELLULOSE NETWORK MATERIALS PRODUCED BY BACTERIA OF FERMENTED AQUEOUS GREEN TEA EXTRACTIN SELECTED CULTURE MEDIA

Objective: The study aims for the fabrication, evaluation of drug loading capability and characterization of 3D-nano-cellulose network materials produced by bacteria of fermented aqueous green tea extract in selected culture media. Methods: 3D-nano-cellulose network (3DNC) materials can be produced by bacteria living in a fermented aqueous green tea extract. 3DNCs include nano-fibers forming networks, which are capable of drug loading to form a prolonged release therapy to improve drug bioavailability. In this study, 3DNC materials are biosynthesized by aerobic bacteria in the standard medium (SM), coconut water (CW) and rice water (RW). 3DNCs were prepared and evaluated for drug carrier using famotidine as a model drug. Famotidine was loaded in 3DNC by the absorption method. 3DNCs were characterized by using FE-SEM and FTIR spectroscopy. Results: The 3DNCs obtained from CW, and RW have the same characteristics as the 3DNC obtained from the SM, and 3DNCs can be fabricated with the desired thickness and diameter in all three types of culture media. 3DNCs absorbed famotidine in optimum condition without any difference in famotidine loading (28.2 mg) and famotidine entrapment efficiency (90 %). Investigation of the 3DNC structure using FE-SEM has shown that the cellulose fibers of 3DNC-SM and 3DNC-CW have a stable structure without structural change when loading drug under optimal condition. Conclusion: The results indicate the potential for using 3DNC-SM and 3DNC-CW to design the drug delivery system

Novel Intrusion Detection using Probabilistic Neural Network and Adaptive Boosting

This article applies Machine Learning techniques to solve Intrusion Detection problems within computer networks. Due to complex and dynamic nature of computer networks and hacking techniques, detecting malicious activities remains a challenging task for security experts, that is, currently available defense systems suffer from low detection capability and high number of false alarms. To overcome such performance limitations, we propose a novel Machine Learning algorithm, namely Boosted Subspace Probabilistic Neural Network (BSPNN), which integrates an adaptive boosting technique and a semi parametric neural network to obtain good tradeoff between accuracy and generality. As the result, learning bias and generalization variance can be significantly minimized. Substantial experiments on KDD 99 intrusion benchmark indicate that our model outperforms other state of the art learning algorithms, with significantly improved detection accuracy, minimal false alarms and relatively small computational complexity.Comment: 9 pages IEEE format, International Journal of Computer Science and Information Security, IJCSIS 2009, ISSN 1947 5500, Impact Factor 0.423, http://sites.google.com/site/ijcsis