This paper introduces a multistep damage identification process that is both straightforward and useful for identifying damage in buildings with regular plan geometries. The algorithm proposed in this study combines the utilization of a multi-damage sensitivity feature and MATLAB programming, providing a comprehensive approach for the structural health monitoring (SHM) of different structures through vibration analysis. The system utilizes accelerometers attached to the structure to capture data, which is then subjected to a classical statistical subspace-based damage detection test. This test focuses on monitoring changes in the data by analyzing modal parameters and statistically comparing them to the structure’s baseline behavior. By detecting deviations from the expected behavior, the algorithm identifies potential damage in the structure. Additionally, the algorithm includes a step to localize damage at the story level, relying on the jerk energy of acceleration. To demonstrate its effectiveness, the algorithm was applied to a steel shear frame model in laboratory tests. The model utilized in this study comprised a total height of 900 mm and incorporated three lumped masses. The investigation encompassed a range of scenarios involving both single and multiple damages, and the algorithm proposed in this research demonstrated the successful detection of the induced damages. The results indicate that the proposed system is an effective solution for monitoring building structure condition and detecting damage.

Adverse underwriting selection presents significant financial risk for health insurers by skewing the insured portfolio toward high-risk individuals and raising claim costs. Empirical evidence integrating region-wise risk analysis combining policy renewal behavior and underwriting (UW) analytics using machine learning techniques remains limited. This longitudinal study addresses the gaps by analyzing a health insurance database of 683 groups (0.40 M policies and 0.208 M claims) over five years. Influential UW and demographic characteristics with policy renewal behavior and estimation of future claim costs, combined with adverse UW selection, were identified using portfolio analysis, machine learning, and regression modeling. Results indicate that a uniform rating structure for the whole group apparently caused loss ratio escalation. Particularly, maternity-related claims were the largest portfolio segment, with 41% of claims escalating the loss ratio towards 100% and claims intensity to 101% in the northern region, indicating adverse UW. Data confirms that UW risk perils combined with demographic factors heavily influence the retention of policyholders and higher-risk groups staying within the insured portfolio for longer durations, compounding future liabilities. Hence, appropriate risk-specific UW combining region-wise claim behavior and structured demographic factors for insurers to detect new risk clusters can enhance renewal and pricing decisions to optimize portfolio sustainability.

Colorectal cancer (CRC) is a major global health burden, with rising incidence in South Asia and significant variation across ethnic and socioeconomic grou

In this study we aimed to explore the medicinal plants in Pakistan, the diversity of medicinal flora of Tehsil Kotli Sattian. On the bases out selected methodology it is concluded that the study area is rich with medicinal flora comprises of 62 plant species belonging to 37 families and 54 genera. It was observed that Rosaceae and Solanaceae are widely distributed families consists of 5 plant species. Leaves are the most extensively used part while decoction was the most common method of herbal preparation. The collected plant species are used by the local community for their primary health care needs indicated that plants have novel therapeutic potential against different ailments.