The COVID-19 pandemic intensified concerns about misinformation, sparking interest in the field of infodemiology, which examines the spread and impact of information on public health perceptions. This research examines how geographic location influenced COVID-19 discourse across 10 Italian cities by analyzing geographically tagged Twitter data. Our network analysis of 4792 high-degree nodes identifies key information spreaders and community structures, while spatiotemporal mapping reveals regional variations in information patterns and influential narratives. Results demonstrate significant geographic and cultural influences on public discourse. In Milan and Rome, economic and political narratives dominated, suggesting targeted messaging about economic recovery and government transparency. Southern regions like Naples require trust-building through community-led initiatives addressing cultural health beliefs. The study identified a clear dichotomy among influencers: established public figures provided evidence-based information, while another group cultivated followings through conspiracy theories, creating echo chambers for skeptical views. This research informs strategies for location-specific information campaigns, helping public health agencies combat misinformation more effectively. Findings emphasize the need for context-specific interventions that consider geographic, cultural, and socioeconomic factors to enhance community resilience during health emergencies.

This article provides a comprehensive introduction about the Vehicle Routing Problem and the Two-Phase heuristics used to solve the routing problem. First, the paper gives an introduction to the Vehicle Routing Problemand its many popular variants. Then the approaches to solve the vehicle routing problem is described briefly. The next section defines heuristics and the criteria to be considered while selecting a heuristic, and the following section provides an overview on the classification of the heuristics used in vehicle routing problem. Several algorithms that utilize two phase heuristics are described in detail. Lastly the effectiveness of the two-phase heuristics is highlighted. The information and the review in this paper provides a clear overview of the Vehicle Routing Problem and its solution that employ two-phase heuristics, which can be used as a starting point for further research. [Siddharth S and Nandini B (2020); A BRIEF OVERVIEW OF VEHICLE ROUTING PROBLEM AND TWO-PHASE HEURISTICS Int. J. of Adv. Res. 8 (May). 1239-1244] (ISSN 2320-5407). www.journalijar.com Article DOI: 10.21474/IJAR01/11038 DOI URL: http://dx.doi.org/10.21474/IJAR01/11038 var switchTo5x=true; stLight.options({publisher: "479cf10d-3052-41f9-a6a6-21a935dff410", doNotHash: false, doNotCopy: false, hashAddressBar: false});

This study provides a detailed analysis of COVID-19-related social media discourse in Italy, using 535,886 tweets from 10 major cities between 30 August 2020 and 8 June 2021. The tweets were translated from Italian to English for analysis. A multifaceted methodology was employed: Latent Dirichlet Allocation (LDA) identified 20 key themes; sentiment analysis, using TextBlob, Flair, and TweetNLP, and emotion recognition using TweetNLP, revealed the emotional tone of the discourse, with 453 tweets unanimously positive across all algorithms. TextBlob was used for lexical analysis to rank the most salient positive and negative terms. The results indicated that positive sentiments centered on hope, safety measures, and vaccination progress, while negative sentiments focused on fear, death, and quarantine frustrations. This research offers valuable insights for public health officials, enabling tailored messaging, real-time strategy monitoring, and agile policymaking during the pandemic, with implications for future health crises.

While existing AI-based microplastic monitoring studies predominantly rely on task-specific fine-tuned models, this study evaluates whether general-purpose multimodal foundation models with no spectroscopic instrumentation, minimal fine-tuning, and minimal computational resources can serve as accessible, low-cost tools for polymer classification under ecologically realistic field conditions with samples of plastic debris collected from coastal Sousse, Tunisia, a Mediterranean region experiencing anthropogenic pollution pressures. A curated dataset of 1080 high-resolution images was developed, representing six polymer groups (HDPE, LDPE, PA, PET, PP, PS, and mixed plastics). Fragments were imaged under standardized lighting conditions against natural sand backgrounds to preserve environmental realism. Each image was manually annotated using polygon-based boundaries to generate pixel-level segmentation masks and associated class labels, providing expert-validated ground truth for quantitative evaluation. Multimodal LLMs were evaluated using a composite scoring framework. Spatial accuracy was assessed using mean Intersection-over-Union (mIoU) against expert annotations, while polymer classification performance was measured using macro-averaged F1 scores across all categories. Model reliability was further evaluated through prompt stability testing and robustness analyses under controlled environmental perturbations designed to assess consistency across varying coastal imaging conditions. Results indicate that multimodal foundation models can distinguish plastic fragments from sand backgrounds, although performance varied across polymer classes and environmental perturbations. The weighted composite framework provides a structured approach for comparing model performance according to ecological monitoring objectives rather than computational metrics. These findings contribute to understanding the potential utility and current limitations of AI-based approaches for marine microplastic analysis and provide insights into coastal pollution patterns and ecosystem health within a One Health monitoring context.