Novel Response Surface Technique for Composite Structure Localization Using Variable Acoustic Emission Velocity.

The time difference of arrival (TDOA) method has traditionally proven effective for locating acoustic emission (AE) sources and detecting structural defects. Nevertheless, its applicability is constrained when applied to anisotropic materials, particularly in the context of fiber-reinforced composite structures. In response, this paper introduces a novel COmposite LOcalization using Response Surface (COLORS) algorithm based on a two-step approach for precise AE source localization suitable for laminated composite structures. Leveraging a response surface developed from critical parameters, including AE velocity profiles, attenuation rates, distances, and orientations, the proposed method offers precise AE source predictions. The incorporation of updated velocity data into the algorithm yields superior localization accuracy compared to the conventional TDOA approach relying on the theoretical AE propagation velocity. The mean absolute error (MAE) for COLORS and TDOA were found to be 6.97 mm and 8.69 mm, respectively. Similarly, the root mean square error (RMSE) for COLORS and TODA methods were found to be 9.24 mm and 12.06 mm, respectively, indicating better performance of the COLORS algorithm in the context of source location accuracy. The finding underscores the significance of AE signal attenuation in minimizing AE wave velocity discrepancies and enhancing AE localization precision. The outcome of this investigation represents a substantial advancement in AE localization within laminated composite structures, holding potential implications for improved damage detection and structural health monitoring of composite structures.

Using Artificial Intelligence (AI) to predict organizational agility.

Since the pandemic organizations have been required to build agility to manage risks, stakeholder engagement, improve capabilities and maturity levels to deliver on strategy. Not only is there a requirement to improve performance, a focus on employee engagement and increased use of technology have surfaced as important factors to remain competitive in the new world. Consideration of the strategic horizon, strategic foresight and support structures is required to manage critical factors for the formulation, execution and transformation of strategy. Strategic foresight and Artificial Intelligence modelling are ways to predict an organizations future agility and potential through modelling of attributes, characteristics, practices, support structures, maturity levels and other aspects of future change. The application of this can support the development of required new competencies, skills and capabilities, use of tools and develop a culture of adaptation to improve engagement and performance to successfully deliver on strategy. In this paper we apply an Artificial Intelligence model to predict an organizations level of future agility that can be used to proactively make changes to support improving the level of agility. We also explore the barriers and benefits of improved organizational agility. The research data was collected from 44 respondents in public and private Australian industry sectors. These research findings together with findings from previous studies identify practices and characteristics that contribute to organizational agility for success. This paper contributes to the ongoing discourse of these principles, practices, attributes and characteristics that will help overcome some of the barriers for organizations with limited resources to build a framework and culture of agility to deliver on strategy in a changing world.

Comparative Study of Popular Deep Learning Models for Machining Roughness Classification Using Sound and Force Signals.

This study compared popular Deep Learning (DL) architectures to classify machining surface roughness using sound and force data. The DL architectures considered in this study include Multi-Layer Perceptron (MLP), Convolution Neural Network (CNN), Long Short-Term Memory (LSTM), and transformer. The classification was performed on the sound and force data generated during machining aluminum sheets for different levels of spindle speed, feed rate, depth of cut, and end-mill diameter, and it was trained on 30 s machining data (10-40 s) of the machining experiments. Since a raw audio waveform is seldom used in DL models, Mel-Spectrogram and Mel Frequency Cepstral Coefficients (MFCCs) audio feature extraction techniques were used in the DL models. The results of DL models were compared for the training-validation accuracy, training epochs, and training parameters of each model. Although the roughness classification by all the DL models was satisfactory (except for CNN with Mel-Spectrogram), the transformer-based modes had the highest training (>96%) and validation accuracies (≈90%). The CNN model with Mel-Spectrogram exhibited the worst training and inference accuracy, which is influenced by limited training data. Confusion matrices were plotted to observe the classification accuracy visually. The confusion matrices showed that the transformer model trained on Mel-Spectrogram and the transformer model trained on MFCCs correctly predicted 366 (or 91.5%) and 371 (or 92.7%) out of 400 test samples. This study also highlights the suitability and superiority of the transformer model for time series sound and force data and over other DL models.

Assessment of Socio-Demographic Factors, Mother and Child Health Status, Water, Sanitation, and Hygienic Conditions Existing in a Hilly Rural Village of Nepal.

In many low income developing countries, socioeconomic, environmental and demographic factors have been linked to around half of the disease related deaths that occur each year. The aim of this study is to investigate the sociodemographic factors, mother and child health status, water, sanitation, and hygienic conditions of a Nepalese community residing in a hilly rural village, and to identify factors associated with mother and child health status and the occurrence of diarrheal and febrile disease. A community-based cross-sectional survey was carried out and 315 households from the village of Narjamandap were included in this study. Factors associated with diarrhea, febrile disease, and full maternal and under-five immunizations were assessed using logistic regression. Results showed that higher education level (middle school versus primary education; Odds Ratio (OR): 0.55, p = 0.04; high school versus primary education; OR 0.21, p = 0.001) and having a toilet facility at home were significantly associated with a lower risk of developing diarrhea and febrile disease (OR 0.49, p = 0.01), while, interestingly, the use of improved water supply was associated with higher risk (OR 3.07, p = 0.005). In terms of maternal immunization, the odds of receiving a tetanus toxoid vaccination were higher in women who had regular antenatal checkups (OR 12.9, p < 0.001), and in those who developed complications during pregnancy (OR 4.54, p = 0.04); for under-five immunization, the odds of receiving full vaccination were higher among children from households that reported diarrhea (OR 2.76, p < 0.001). The findings of this study indicated that gaps still exist in the mother and child healthcare being provided, in terms of receiving antenatal checkups and basic immunizations, as evidenced by irregular antenatal checkups, incomplete and zero vaccination cases, and higher under-five deaths. Specific public health interventions to promote maternal health and the health of under-five children are suggested.