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The conventional approaches for detecting structural degradation are time-consuming, labor-intensive, and costly. The physical monitoring of the structure also poses risks to the health and safety of supervisors. Therefore, damage estimation of any structure using artificial intelligence (AI), more specifically deep learning (DL), is becoming more significant in civil infrastructure. In the presented research article, an efficient two-stage damage detection method is proposed for structural damage detection (SDD) from time domain vibration signals. The proposed method utilizes two-dimensional convolutional neural network (2D-CNN) architecture as a DL algorithm for damage detection. Here, a computer-aided damage detection method for steel beam and frame-type structures is developed using 2D-CNN algorithm in the Google Colab platform. The effectiveness of the proposed method is first verified, and it provides more than 90% accuracy for identifying the damage location and severity of a cantilever beam for single- and multi-damage scenarios from numerically simulated noisy displacement data. The algorithm is also experimentally validated through the raw acceleration data of damaged steel frame joints collected from the Qatar University Grandstand simulator (QUGS). The proposed 2D-CNN algorithm performs better than other DL algorithms by achieving 100% accuracy within 10 epochs for damage detection of steel frames using QUGS data. It demonstrates significant potential for detecting damage location and quantifying damages for single- and multi-damage scenarios using noise-free and noisy datasets. The primary contribution of this study resides in the implementation of two-stage damage detection algorithm utilizing 2D-CNN with time domain vibration response for multiclass damage identification and quantification. [ABSTRACT FROM AUTHOR]

The present study outlines the many risk variables of myocardial infarction (MI) in the patient population. Blood lipid profiles were normolipidemic in the study’s subjects. Myocardial infarction patients and controls were compared concerning their lipid, lipoprotein, and apolipoprotein profiles. Materials & methods: In this study, 84 participants had their blood cholesterol, Apolipoprotein-A, LDL, VLDL, HDL, triacylglycerols, and malondialdehyde studied. Out of the total number of participants, 42 were individuals who had suffered a myocardial infarction (MI). In contrast, the other 42 were controls matched for age and sex. Results: Acute myocardial infarction (MI) patients had significantly elevated levels of total cholesterol and triacylglycerols (TAGs) (p <0.05) despite the presence of low-density lipoprotein (LDL) in their blood. A significant difference was seen between the levels of apolipoprotein-A and low-density lipoprotein in the MI group and those in the control group. Researchers found that those who had a rapid heart attack exhibited significantly higher levels of malondialdehyde (p<0.05). Conclusion: For individuals diagnosed with risk factors, lipid profile monitoring, dietary antioxidant consumption, and measurement of inflammatory markers can all help lower the risk of myocardial infarction (MI). Furthermore, early detection can prevent MI. [ABSTRACT FROM AUTHOR]

Background: "Within the first ninety minutes, sometimes called the door-to-balloon time (DTBT1), medical professionals strongly recommend that patients with ST-elevation myocardial infarction (STEMI) get mechanical reperfusion. Aim: This study set out to examine, within a group of patients aged 75 and above, the intricate connection between the time elapsed between the door and the balloon and the clinical outcomes. After that, we subdivided this cohort further into those aged 75-84 and those aged 85 and higher. Materials and methods: Following the acceptance of consent from the Institutional Ethics Committee, this study's first point of departure was determined. The investigation was conducted at the Care Hospital in Bhubaneswar, which was the healthcare facility's site. Results: Patients as young as 75 and younger were included in the younger group. People deemed elderly were those between the ages of 75 and 84. On the other hand, those who were 85 were considered among the demographic of elderly citizens considered exceedingly old. This study aimed to reduce the number of major adverse cardiovascular events (MACE) and death rates after one year. One hundred eighteen of the patients were eighty-five years old, and thirty-four were in the age range of seventy-five to eighty-four years old. Participants in this study had an average age of 60.1 years, making it a relatively young group. We discovered a tendency in the senior population (p-for-trend = 0.10) and a substantial drop in DTBT with time in both younger and older persons (p-for-trend = 0.02, 0.05). Similarly, a pattern appeared among the somewhat younger patients. A greater mortality rate (4.1% compared to 11.2% vs 31%; p < 0.05) was seen among those who were highly elderly all during the 12 months investigated. Additionally, it was shown that they had a higher probability of developing major adverse cardiovascular events (MACE) (11.7% vs 21.8% versus 34.6%; p < 0.05). A more favourable outcome was linked to DTBT for 90 minutes. While it did reduce mortality at 12 months (OR0.89, 95% CI0.61-1.45), it was not a reliable predictor of severe adverse cardiovascular events (OR0.91, 95% CI0.71-1.21). We arrived at this result using a univariate analysis. Conclusion: A 90-minute DTBT was not proven to be an independent predictor of outcomes at one year, even though DTBT improved in patients aged 75 and 75-84 over ten years compared to the duration of the experiment. Therefore, it is not always the case that less favourable clinical outcomes are more favourable when deciding whether patients above 85 are suitable candidates for invasive therapy. [ABSTRACT FROM AUTHOR]

The impact of artificial intelligence (AI) on the human world has been significant and farreaching. AI has revolutionized various aspects of our lives, from industry and healthcare to communication and entertainment. This abstract will provide a brief overview of the impact of AI on different domains and its implications for society. In the industrial sector, AI has enabled automation and increased efficiency in manufacturing processes. AI-powered robots and machines can perform repetitive tasks with precision and speed, reducing human error and production costs. This has led to increased productivity and economic growth. However, concerns arise regarding potential job displacement as AI technology continues to advance. [ABSTRACT FROM AUTHOR]