Attenuation of muscle damage by preconditioning with muscle hyperthermia

This study investigated the hypothesis that muscle damage would be attenuated in muscles subjected to passive hyperthermia 1 day prior to exercise. Fifteen male students performed 24 maximal eccentric actions of the elbow flexors with one arm; the opposite arm performed the same exercise 2-4 weeks later. The elbow flexors of one arm received a microwave diathermy treatment that increased muscle temperature to over 40°C, 16-20 h prior to the exercise. The contralateral arm acted as an untreated control. Maximal voluntary isometric contraction strength (MVC), range of motion (ROM), upper arm circumference, muscle soreness, plasma creatine kinase activity and myoglobin concentration were measured 1 day prior to exercise, immediately before and after exercise, and daily for 4 days following exercise. Changes in the criterion measures were compared between conditions (treatment vs. control) using a two-way repeated measures ANOVA with a significance level of P < 0.05. All measures changed significantly following exercise, but the treatment arm showed a significantly faster recovery of MVC, a smaller change in ROM, and less muscle soreness compared with the control arm. However, the protective effect conferred by the diathermy treatment was significantly less effective compared with that seen in the second bout performed 4-6 weeks after the initial bout by a subgroup of the subjects (n = 11) using the control arm. These results suggest that passive hyperthermia treatment 1 day prior to eccentric exercise-induced muscle damage has a prophylactic effect, but the effect is not as strong as the repeated bout effect. © Springer-Verlag 2006.

Frequency response function based structural damage detection using artificial neural networks

Damage detection in structures has become increasingly important in recent years. While a number of damage detection and localization methods have been proposed, few attempts have been made to explore the structure damage with frequency response functions (FRFs). This paper illustrates the damage identification and condition assessment of a beam structure using a new frequency response functions (FRFs) based damage index and Artificial Neural Networks (ANNs). In practice, usage of all available FRF data as an input to artificial neural networks makes the training and convergence impossible. Therefore one of the data reduction techniques Principal Component Analysis (PCA) is introduced in the algorithm. In the proposed procedure, a large set of FRFs are divided into sub-sets in order to find the damage indices for different frequency points of different damage scenarios. The basic idea of this method is to establish features of damaged structure using FRFs from different measurement points of different sub-sets of intact structure. Then using these features, damage indices of different damage cases of the structure are identified after reconstructing of available FRF data using PCA. The obtained damage indices corresponding to different damage locations and severities are introduced as input variable to developed artificial neural networks. Finally, the effectiveness of the proposed method is illustrated and validated by using the finite element modal of a beam structure. The illustrated results show that the PCA based damage index is suitable and effective for structural damage detection and condition assessment of building structures.

Assessment of damages for property damage

In Gagner Pty t/as Indochine Café v Canturi Corporation Pty Ltd (2009) 262 ALR 691, the assessment of damages awarded for the rectification work to the premises of the respondent was in issue. The appellant operated a restaurant above the respondent’s jewellery store in Sydney. When the kitchen of the restaurant flooded, water escaped causing damage to the jewellery store’s fit-out. The escape of the water was held to be due to the negligence of persons for whom the appellant was vicariously liable. The trial judge awarded damages, measured by the amount required to return the premises as close as was possible to the condition prior to the flood damage as well as an allowance for interruption to the business for 10 days. The 10 day allowance reflected the number of days the store would have been closed for if it was to be returned to its previous condition. The evidence was that the flooding has only affected approximately 10% of the floor area of the store. However, instead of having work carried out to bring the premises back to its condition as before the water damage, the respondent closed the business for 29 working days for a complete internal refurbishment – at a cost substantially more than simple rectification. On appeal it was argued that the trial judge had assessed the damages incorrectly as by undertaking a complete refurbishment had the effect that the respondent did not suffer any loss as a consequence of the negligence in relation to the fit-out. It was asserted that the claim for damages was in the circumstances a claim for betterment. It was also argued that the damages should not include a component for GST. Campbell JA gave reasons, with Macfarlan JA and Sackville AJA agreeing.

Study of tissue damage during mechanical peeling of tough skinned vegetables

Peeling is an essential phase of post harvesting and processing industry; however the undesirable losses and waste rate that occur during peeling stage are always the main concern of food processing sector. There are three methods of peeling fruits and vegetables including mechanical, chemical and thermal, depending on the class and type of fruit. By comparison, the mechanical method is the most preferred; this method keeps edible portions of produce fresh and creates less damage. Obviously reducing material losses and increasing the quality of the process has a direct effect on the whole efficiency of food processing industry which needs more study on technological aspects of this industrial segment. In order to enhance the effectiveness of food industrial practices it is essential to have a clear understanding of material properties and behaviour of tissues under industrial processes. This paper presents the scheme of research that seeks to examine tissue damage of tough skinned vegetables under mechanical peeling process by developing a novel FE model of the process using explicit dynamic finite element analysis approach. In the proposed study a nonlinear model which will be capable of simulating the peeling process specifically, will be developed. It is expected that unavailable information such as cutting force, maximum shearing force, shear strength, tensile strength and rupture stress will be quantified using the new FEA model. The outcomes will be used to optimize and improve the current mechanical peeling methods of this class of vegetables and thereby enhance the overall effectiveness of processing operations. Presented paper aims to review available literature and previous works have been done in this area of research and identify current gap in modelling and simulation of food processes.

Structural damage detection using frequency response functions and neural networks

This paper illustrates the damage identification and condition assessment of a three story bookshelf structure using a new frequency response functions (FRFs) based damage index and Artificial Neural Networks (ANNs). A major obstacle of using measured frequency response function data is a large size input variables to ANNs. This problem is overcome by applying a data reduction technique called principal component analysis (PCA). In the proposed procedure, ANNs with their powerful pattern recognition and classification ability were used to extract damage information such as damage locations and severities from measured FRFs. Therefore, simple neural network models are developed, trained by Back Propagation (BP), to associate the FRFs with the damage or undamaged locations and severity of the damage of the structure. Finally, the effectiveness of the proposed method is illustrated and validated by using the real data provided by the Los Alamos National Laboratory, USA. The illustrated results show that the PCA based artificial Neural Network method is suitable and effective for damage identification and condition assessment of building structures. In addition, it is clearly demonstrated that the accuracy of proposed damage detection method can also be improved by increasing number of baseline datasets and number of principal components of the baseline dataset.

Structural damage identification with noise polluted Frequency Response Functions (FRFs)

Damage detection in structures has become increasingly important in recent years. While a number of damage detection and localization methods have been proposed, very few attempts have been made to explore the structure damage with noise polluted data which is unavoidable effect in real world. The measurement data are contaminated by noise because of test environment as well as electronic devices and this noise tend to give error results with structural damage identification methods. Therefore it is important to investigate a method which can perform better with noise polluted data. This paper introduces a new damage index using principal component analysis (PCA) for damage detection of building structures being able to accept noise polluted frequency response functions (FRFs) as input. The FRF data are obtained from the function datagen of MATLAB program which is available on the web site of the IASC-ASCE (International Association for Structural Control– American Society of Civil Engineers) Structural Health Monitoring (SHM) Task Group. The proposed method involves a five-stage process: calculation of FRFs, calculation of damage index values using proposed algorithm, development of the artificial neural networks and introducing damage indices as input parameters and damage detection of the structure. This paper briefly describes the methodology and the results obtained in detecting damage in all six cases of the benchmark study with different noise levels. The proposed method is applied to a benchmark problem sponsored by the IASC-ASCE Task Group on Structural Health Monitoring, which was developed in order to facilitate the comparison of various damage identification methods. The illustrated results show that the PCA-based algorithm is effective for structural health monitoring with noise polluted FRFs which is of common occurrence when dealing with industrial structures.