Bridge decks deteriorate faster compared to other bridge components, primarily influenced by traffic volume, while previous studies have examined the effect of bridge-wearing surfaces on deterioration, further understanding of the relationship between bridge performance and maintenance is needed for policy-making and planning purposes. In this study, we focus on nine influential variables to unravel the intricate connections among performance, deterioration, and maintenance of six distinct bridge-wearing surfaces: Monolithic Concrete, Gravel, Wood or Timber, Bituminous, Low Surface Concrete, and Other. Statistical analyses were employed to determine associations between variables and concepts, exploring similarities and differences across various wearing surface types. In particular, machine learning algorithms were utilized to model the maintenance considering the performance and deterioration of the six diverse wearing surfaces. This approach allowed for an examination of interactions between those variables and concepts. We further applied a well-performing prediction model (which achieved an accuracy of 0.86 and an AUC score of approximately 0.83) to obtain interpretable insights regarding bridge deck surfaces. Analysis with interpretable methods such as SHAP (Shapley additive explanation) and PDP (partial dependency plot) revealed that deterioration, deck age, deck area, and overall performance were the most influential variables among average daily traffic, average daily truck traffic, and the number of spans significantly influenced the maintenance of bridge deck condition with different wearing surfaces. Notably, a strong relationship between performance and maintenance was observed in specific wearing surface types, such as Monolithic Concrete and Wood or Timber, while Other surface types exhibited different patterns. These findings highlight the need for tailored approaches when assessing bridge health, considering the distinct characteristics of different bridge deck types.

The convergence of Large Language Models (LLMs) with the Internet of Things (IoT) is driving a transformative shift toward a continuous, context-aware smart healthcare ecosystem. Due to its novelty, existing research in this domain remains fragmented, leaving a critical gap in unified frameworks that synthesize domain applications, functional AI deployment roles, network architectures, and security boundaries. Following PRISMA 2020 guidelines, this paper presents a systematic literature review and quantitative analysis evaluating a selected corpus of 61 peer-reviewed and 14 preprint papers in this domain. Methodologically, we assess a novel hybrid article discovery strategy, finding that an AI-powered prompt-based literature search strategy achieves higher precision than traditional keyword-based Boolean queries (86% vs. 42%) on the evaluated search sample, which may reduce screening workloads. We found that the major limitation of AI-based literature search is non-determinism, which is also an inherent property of LLM-powered applications. To address this, we propose methodological guidelines for using an AI-assisted hybrid literature search strategy. Based on the selected literature, we establish a multi-layer taxonomy organizing the IoT-LLM advances in the healthcare domain across four pillars: application domain, LLM role, IoT device type, and architectural deployment pattern. Quantitative synthesis reveals a heavy research concentration in remote patient monitoring and personal health management (representing 59% of the corpus combined), primarily driven by the data accessibility of wearable sensors (64%). Cross-tabulation uncovers a distinct capability–constraint spectrum: cloud-based deployments lean on heavyweight state-of-the-art models (mainly GPT-family models) for complex semantic reasoning, whereas edge, federated, and blockchain-based hybrid systems leverage localized models (BERT and LLaMA families). Patient data privacy and reduced communication overhead were among the main reasons for choosing localized models. Crucially, our assessment reveals a pervasive neglect of LLM-specific vulnerabilities such as prompt injection and jailbreak attacks and a tendency to treat regulatory frameworks (e.g., HIPAA, GDPR) as design features rather than empirically validated compliance metrics. Finally, we propose an actionable future research agenda prioritizing multi-device system orchestration, emergency care integration, privacy-preserving LLMs, and deployment-scale clinical validation.

As cities continue to grow and evolve, maintaining and monitoring their infrastructure becomes an 2 increasingly challenging problem, requiring multidisciplinary approaches. This problem is as critical for 3 developing countries that are rapidly building their infrastructure (Gurara et al., 2018;Ede et al., 2019) 4 as it is for already developed countries with aging infrastructure (Van Breugel, 2017). Researchers have 14 Such developments can also be integrated into digital twinning-a technology approach that creates 15 virtual replicas of physical infrastructure elements, systems, and cities-that offers a dynamic framework 16 for real-time monitoring and predictive maintenance through data integration and simulation. The ability to 17 connect insights across these scales-from individual components to entire cities-makes digital twinning The article titled "Policy evaluation and performance assessment for sustainable urbanization: a study of 29 selected city corporations in Bangladesh" explores the interplay between urban governance and sustainable 30 urbanization goals in line with the sustainable development goals (SDG). Taking Bangladesh as a case study, 31 the paper focused on investigating how urban governance impacts the "sustainable cities and communities" 32 goal of the SDG agenda. Based on a comprehensive analysis of data collected from primary and secondary 33 sources, the study revealed significant disparities between stakeholder expectations and current state of 34 urban governance. To fill the identified gaps in the study, the paper has proposed several recommendations 35 that can be helpful for policymakers, urban planners, and other stakeholders.The second article titled "Evaluating the feasibility of constructing shopping centers on urban vacant

'Correction to: Model-Cart: A Machine Learning Meta-Framework with Explainability and Human-in-the-Loop' published in 'The 22nd International Conference on Information Technology-New Generations (ITNG 2025)'