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Damage of engineering structures is a nonlinear evolutionary process that spans across both material and structural levels, from mesoscale to macroscale. This paper aims to provide a comprehensive review of damage analysis methods at both the material and structural levels.

This study provides an overview of multiscale damage analysis of engineering structures, including its definition and significance. Current status of damage analysis at both material and structural levels is investigated, by reviewing damage models and prediction methods from single-scale to multiscale perspectives. The discussion of prediction methods includes both model-based simulation approaches and data-driven techniques, emphasizing their roles and applications. Finally, summarize the main findings and discuss potential future research directions in this field.

In the material level, damage research primarily focuses on the degradation of material properties at the macroscale using continuum damage mechanics (CDM). In contrast, at the mesoscale, damage research involves analyzing material behavior in the meso-structural domain, focusing on defects like microcracks and void growth. In structural-level damage analysis, the macroscale is typically divided into component and structural scales. The component scale examines damage progression in individual structural elements, such as beams and columns, often using detailed finite element or mesoscale models. The structural scale evaluates the global behavior of the entire structure, typically using simplified models like beam or shell elements.

To achieve realistic simulations, it is essential to include as many mesoscale details as possible. However, this results in significant computational demands. To balance accuracy and efficiency, multiscale methods are employed. These methods are categorized into hierarchical approaches, where different scales are processed sequentially, and concurrent approaches, where multiple scales are solved simultaneously to capture complex interactions across scales.

The level of corruption in Nigeria is very high, and this has grossly reduced the desired confidence and trust in the nation’s political leaders and political institutions. It is even worse to the extent that many of its citizens, especially in the medical profession, lecturers and other specialties, are leaving the country altogether because they have already lost hope in the country called Nigeria. Therefore, the purpose of this study is to investigate how political trust in Nigeria is affected by perceived corruption and to ascertain how social media use functions in this relationship.

Because this study is quantitative in nature, a positivist research philosophy is being used. A cross-sectional research design was used in this study. 14.1 million voters in north-central Nigeria are the study’s population, and a sample size of 385 was determined through an online sample size calculator with a 2% margin of error and a 95% confidence interval. The population was divided into smaller units for the study, and samples were selected from each unit using multistage sampling and simple random sampling techniques. An online self-administered questionnaire was used through the various social media’s platforms because of the nature of the study’s population to collect data. To examine the gathered data, descriptive and inferential statistics were applied. While inferential statistics were used to test the hypotheses through partial least squares structural equation modeling, descriptive statistics were used to analyze the respondents’ demographic data via a frequency table.

This study’s findings showed that social media use mediates the relationship between perceived corruption and political trust in Nigeria and that perceived corruption positively and significantly affects political trust in Nigeria.

This study is not without its limitations. Therefore, the few limitations of the study range from the limited sample sample to the population of Nigeria. Also, using only the quantitative research method for the nature of this research is another major limitation of the study. And lastly, using one out of the six zones in Nigeria will make it difficult to generalize the findings of the study. However, it is then recommended that future researchers consider a larger population than the current study for proper coverage; the future study can also use both the quantitative and qualitative research methods.

The practical implications of understanding how social media shapes political trust among political leaders through the lens of perceived corruption in the Nigerian political system are dimensional and have implications for various stakeholders, including policymakers, political leaders, media professionals and the general public. First, for policymakers and political leaders, the findings offer insights into the importance of proactive and transparent communication on social media. Recognizing the impact of social media on shaping perceptions of corruption, political figures such as the office of the presidency, senators, governors and all other political office holders can leverage these platforms to engage people.

This study is innovative because it examines, through the lens of perceived corruption, how social media use influences political trust among political leaders. This approach provides a new look at the relationship between digital engagement and political attitudes.

Timely intrusion detection in extensive traffic remains a pressing and complex challenge, including for Web services. Current research emphasizes improving detection accuracy through machine learning, with scant attention paid to the dataset’s impact on the capability for fast detection. Many datasets rely on flow-level features, requiring entire flow completion before determining if it constitutes an attack, reducing efficiency. This paper aims to introduce a new feature extraction method and construct a new security dataset that enhances detection efficiency.

This paper proposes a novel partial-flow feature extraction method that extracts packet-level features efficiently to reduce the high latency of flow-level extraction. The method also integrates statistical and temporal features derived from partial flows to improve accuracy. The method was applied to the original packet capture (PCAP) files utilized in creating the CSE-CIC-IDS 2018 dataset, resulting in the development of the WKLIN-WEB-2023 dataset specifically designed for web intrusion detection. The effectiveness of this method was evaluated by training nine classification models on both the WKLIN-WEB-2023 and CSE-CIC-IDS 2018 datasets.

The experimental results show that models trained on the WKLIN-WEB-2023 dataset consistently outperform those on the CSE-CIC-IDS 2018 dataset across precision, recall, f1-score, and detection latency. This demonstrates the superior effectiveness of the new dataset in enhancing both the efficiency and accuracy of intrusion detection.

This study proposes the partial-flow feature extraction method, creating the WKLIN-WEB-2023 dataset. This novel approach significantly enhances detection efficiency while maintaining classification performance, providing a valuable foundation for further research on intrusion detection efficiency.