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Combining two organizational change theories, life cycle and organizational development, this study examines how strategic change cycle has been adopted and implemented across three different organizations, a public organization, an NGO and an intergovernmental organization toward achieving their goals.

This study triangulates three different qualitative research methods: open-ended semi-structured interviews conducted with UN Women Egypt's director, text analysis of the three organizations' websites and the discourse analysis of the Tri-County Foundation's leaders.

Strategic change cycle has been differently formulated, adopted and implemented by the three organizations based on their goals, resources and contexts. While Office Board of Investment adopted a comprehensive reactive change, Tri-County Foundation followed a partial proactive transformation and UN Women Egypt developed a partial reactive strategy. Henceforth, public organizations and nonprofit organizations can develop different strategies of change in function of needs, resources, goals and context.

This study advances a theoretical framework on organizational change by integrating two theories, life cycle and organizational development, presenting four patterns of change: comprehensive reactive, comprehensive proactive, partial reactive and partial proactive.

This study aims to ensure reliable analysis of dynamic responses in asphalt pavement structures. It investigates noise reduction and data mining techniques for pavement dynamic response signals.

The paper conducts time-frequency analysis on signals of pavement dynamic response initially. It also uses two common noise reduction methods, namely, low-pass filtering and wavelet decomposition reconstruction, to evaluate their effectiveness in reducing noise in these signals. Furthermore, as these signals are generated in response to vehicle loading, they contain a substantial amount of data and are prone to environmental interference, potentially resulting in outliers. Hence, it becomes crucial to extract dynamic strain response features (e.g. peaks and peak intervals) in real-time and efficiently.

The study introduces an improved density-based spatial clustering of applications with Noise (DBSCAN) algorithm for identifying outliers in denoised data. The results demonstrate that low-pass filtering is highly effective in reducing noise in pavement dynamic response signals within specified frequency ranges. The improved DBSCAN algorithm effectively identifies outliers in these signals through testing. Furthermore, the peak detection process, using the enhanced findpeaks function, consistently achieves excellent performance in identifying peak values, even when complex multi-axle heavy-duty truck strain signals are present.

The authors identified a suitable frequency domain range for low-pass filtering in asphalt road dynamic response signals, revealing minimal amplitude loss and effective strain information reflection between road layers. Furthermore, the authors introduced the DBSCAN-based anomaly data detection method and enhancements to the Matlab findpeaks function, enabling the detection of anomalies in road sensor data and automated peak identification.