Submitted:
09 July 2024
Posted:
10 July 2024
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Abstract
Keywords:
1. Introduction
2. Literature Review
2.1. Themes and Theories
2.2. Historical Perspective
3. Materials and Methods
Database Selection and Search Strategy
Inclusion and Exclusion Criteria
Bibliometric Analysis
Thematic Categorization
- Stop Word Removal: Filtering out common stop words and irrelevant terms to enhance analytical precision.
- Bigram Extraction: Identifying the top five bigrams by frequency for each article after applying lemmatization to convert words to their root forms.
- Bigram Aggregation: Pooling extracted bigrams across all articles within each document group and ranking them by frequency.
- Frequency Analysis: Highlighting the most prominent themes and terms within the literature corpus.
- Distribution Analysis: Examining the frequency and context of top bigrams to reveal thematic shifts over time.
- Visualization: Generating word clouds and distribution graphs to visually represent the predominant themes within each temporal cohort.
Algorithmic Workflow
- Step 1: Database Identification—identifying databases rich in relevant research articles.
- Step 2: Keyword Formulation—creating a list of search terms.
- Step 3: Temporal Filter—conducting searches within the specified year range.
- Step 4: Criteria-Based Filtering—screening search results based on inclusion and exclusion criteria.
- Step 5: Bibliometric Analysis—generating term co-occurrence networks.
- Step 6: Topical Relevance Filtering—ensuring selected literature contributes meaningfully to the objectives of the study.
- Step 1: Corpus Bifurcation—segmenting literature into pre- and post-COVID temporal cohorts for trend analysis.
- Step 2: Stop Word Removal—enhancing focus on content-rich words.
- Step 3: Bigram Extraction—identifying significant word pairs.
- Step 4: Bigram Aggregation—aggregating and ranking bigrams by frequency.
- Step 5: Frequency Analysis—highlighting prominent themes.
- Step 6: Distribution Analysis—identifying thematic shifts over time.
- Step 7: Visualization—creating word clous visual representations of thematic data.
4. Results
- Red Cluster: the main themes are sustainability and energy with key terms including “energy security,” “sustainable development,” “carbon footprint,” and “environmental impact.”
- Green Cluster: the main themes are risk and security within the supply chain with key terms being “supply chain security,” “risk assessment,” “network security,” and “freight transportation.”
- Blue Cluster: the main themes are technology and data management with key terms being “blockchain,” “cybersecurity,” “internet of things,” and “smart contract.”
- Yellow Cluster: the main themes are food security and the impacts of the COVID-19 pandemic with key terms being “food quality,” “pandemic,” and “livelihood.”
- Purple Cluster: the main themes are economic and social effects with key terms being “energy utilization,” and “health care.”
| Category | Theme | Sample Articles |
|---|---|---|
| Emerging Technologies | Blockchain and biometrics are pivotal in enhancing security and transparency in transportation | [1,4,5,26,27,28,29,39,40,41,46,47,48] |
| Environmental Considerations | Integrating environmental sustainability with security in transportation is crucial for resilient supply chains. | [49,50] |
| E-commerce Challenges | The surge in e-commerce demands innovative security strategies for efficient and safe package delivery. | [51,52] |
| Standardization and Regulation Needs | Consistent standards and regulations are vital for ensuring security, especially in international supply chains. | [30,35,36,37,53,54,55] |
| Practical Insights from Real-World Scenarios | Empirical studies offer valuable insights into the practical challenges and potentials of transportation security systems. | [10,31,32,33,34,56,57,58] |
5. Discussion
6. Conclusion
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Conflicts of Interest
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