ABSTRACT Reliable geospatial information on road accidents is vital for safety analysis and infrastructure planning, yet most low-and middle-income countries continue to face a critical shortage of accurate, location-specific crash data. Existing text-based geocoding tools perform poorly in multilingual and unstructured news environments, where incomplete place descriptions and mixed language (e.g. To address these limitations, this study introduces ALIGN (Accident Location Inference through Geo-Spatial Neural Reasoning) -- a vision-language framework that emulates human spatial reasoning to infer accident location coordinates directly from available textual and map-based cues. ALIGN integrates large language and vision-language model mechanisms within a multi-stage pipeline that performs optical character recognition, linguistic reasoning, and map-level verification through grid-based spatial scanning. The framework systematically evaluates each predicted location against contextual and visual evidence, ensuring interpretable, fine-grained geolocation outcomes without requiring model retraining. Applied to Bangla-language news data source, ALIGN demonstrates consistent improvements over traditional geoparsing methods, accurately identifying district-and sub-district-level crash sites. Beyond its technical contribution, the framework establishes a high accuracy foundation for automated crash mapping in data-scarce regions, supporting evidence-driven road-safety policymaking and the broader integration of multimodal artificial intelligence in transportation analytics. Hossain) 1. Introduction Accurate, fine-grained geospatial data is the bedrock of effective public safety policy, urban planning, and strategic response. For road safety, knowing the precise location of traffic crashes is essential for diagnosing high-risk black spots, deploying emergency services, and evaluating the impact of engineering interventions. While high-income nations increasingly rely on robust, integrated crash databases and vehicle telematics (Guo, Qian, & Shi, 2022; Szpytko & Nasan Agha, 2020), utilizing advanced methods such as deep learning on multi-vehicle trajectories (Yang et al., 2021), ensemble models integrating connected vehicle data (Yang et al., 2026), and 2 probe vehicle speed contour analysis (Wang et al., 2021), a significant'geospatial data desert' persists in most Low-and Middle-Income Countries (LMICs) (Mitra & Bhalla, 2023; Chang et al., 2020). This gap is particularly tragic given that these regions bear the overwhelming brunt of global road traffic fatalities. This research focuses on a low-resource country-Bangladesh, a nation that exemplifies this critical data-sparse challenge. The World Bank has estimated that the costs associated with traffic crashes can amount to as much as 5.1% of the country's Gross Domestic Product (World Bank, 2022).

This paper presents the initial results from our structured literature review on applications of Formal Methods (FM) to Robotic Autonomous Systems (RAS). We describe our structured survey methodology; including database selection and associated search strings, search filters and collaborative review of identified papers. We categorise and enumerate the FM approaches and formalisms that have been used for specification and verification of RAS. We investigate FM in the context of sub-symbolic AI-enabled RAS and examine the evolution of how FM is used over time in this field. This work complements a pre-existing survey in this area and we examine how this research area has matured over time. Specifically, our survey demonstrates that some trends have persisted as observed in a previous survey. Additionally, it recognized new trends that were not considered previously including a noticeable increase in adopting Formal Synthesis approaches as well as Probabilistic Verification Techniques.

Of the 1,104 papers initially retrieved, only ten met the IC. This process was conducted by the main author and subsequently reviewed by the other authors. Secondly, we applied backward and forward snowballing (via Google Scholar) on each selected paper to identify additional relevant studies not captured by the initial Scopus search. This iterative process is illustrated in Figure 2. Through snowballing, we identified 17 additional papers, bringing the total number of selected studies to 27. In total, we screened over 2,400 papers across the Scopus search and snowballing iterations. D. Data Extraction and Classification Scheme The Data Extraction and Classification Scheme for each paper is outlined in Table II. The selection process and the extracted data are documented in our online Zenodo repository, which includes information on each identified paper, the reason for its inclusion or exclusion, and the data extracted to answer each RQ.

Building trust between humans and robots has long interested the robotics community. Various studies have aimed to clarify the factors that influence the development of user trust. In Human-Robot Interaction (HRI) environments, a critical aspect of trust development is the robot's ability to make its behavior understandable. The concept of an eXplainable Autonomous Robot (XAR) addresses this requirement. However, giving a robot self-explanatory abilities is a complex task. Robot behavior includes multiple skills and diverse subsystems. This complexity led to research into a wide range of methods for generating explanations about robot behavior. This paper presents a systematic literature review that analyzes existing strategies for generating explanations in robots and studies the current XAR trends. Results indicate promising advancements in explainability systems. However, these systems are still unable to fully cover the complex behavior of autonomous robots. Furthermore, we also identify a lack of consensus on the theoretical concept of explainability, and the need for a robust methodology to assess explainability methods and tools has been identified.

Generative artificial intelligence (GenAI) can reshape education and learning. While large language models (LLMs) like ChatGPT dominate current educational research, multimodal capabilities, such as text-to-speech and text-to-image, are less explored. This study uses topic modeling to map the research landscape of multimodal and generative AI in education. An extensive literature search using Dimensions.ai yielded 4175 articles. Employing a topic modeling approach, latent topics were extracted, resulting in 38 interpretable topics organized into 14 thematic areas. Findings indicate a predominant focus on text-to-text models in educational contexts, with other modalities underexplored, overlooking the broader potential of multimodal approaches. The results suggest a research gap, stressing the importance of more balanced attention across different AI modalities and educational levels. In summary, this research provides an overview of current trends in generative AI for education, underlining opportunities for future exploration of multimodal technologies to fully realize the transformative potential of artificial intelligence in education.

Systematic literature reviews are the highest quality of evidence in research. However, the review process is hindered by significant resource and data constraints. The Literature Review Network (LRN) is the first of its kind explainable AI platform adhering to PRISMA 2020 standards, designed to automate the entire literature review process. LRN was evaluated in the domain of surgical glove practices using 3 search strings developed by experts to query PubMed. A non-expert trained all LRN models. Performance was benchmarked against an expert manual review. Explainability and performance metrics assessed LRN's ability to replicate the experts' review. Concordance was measured with the Jaccard index and confusion matrices. Researchers were blinded to the other's results until study completion. Overlapping studies were integrated into an LRN-generated systematic review. LRN models demonstrated superior classification accuracy without expert training, achieving 84.78% and 85.71% accuracy. The highest performance model achieved high interrater reliability (k = 0.4953) and explainability metrics, linking 'reduce', 'accident', and 'sharp' with 'double-gloving'. Another LRN model covered 91.51% of the relevant literature despite diverging from the non-expert's judgments (k = 0.2174), with the terms 'latex', 'double' (gloves), and 'indication'. LRN outperformed the manual review (19,920 minutes over 11 months), reducing the entire process to 288.6 minutes over 5 days. This study demonstrates that explainable AI does not require expert training to successfully conduct PRISMA-compliant systematic literature reviews like an expert. LRN summarized the results of surgical glove studies and identified themes that were nearly identical to the clinical researchers' findings. Explainable AI can accurately expedite our understanding of clinical practices, potentially revolutionizing healthcare research.

Citrus diseases have been major issues for citrus growing worldwide for many years they can lead significantly reduce fruit quality. the most harmful citrus diseases are citrus canker, citrus greening, citrus black spot, citrus leaf miner which can have significant economic losses of citrus industry in worldwide prevention and management strategies like chemical treatments. Citrus diseases existing in all over the world where citrus is growing its effects the citrus tree root, citrus tree leaf, citrus tree orange etc. Existing of citrus diseases is highly impact on economic factor that can also produce low quality fruits and increased the rate for diseases management. Sanitation and routine monitoring can be effective in managing certain citrus diseases, but others may require more intensive treatments like chemical or biological control methods.

Cyber Security is one of the most arising disciplines in our modern society. We work on Cybersecurity domain and in this the topic we chose is Cyber Security Ontologies. In this we gather all latest and previous ontologies and compare them on the basis of different analyzing factors to get best of them. Reason to select this topic is to assemble different ontologies from different era of time. Because, researches that included in this SLR is mostly studied single ontology. If any researcher wants to study ontologies, he has to study every single ontology and select which one is best for his research. So, we assemble different types of ontology and compare them against each other to get best of them. A total 24 papers between years 2010-2020 are carefully selected through systematic process and classified accordingly. Lastly, this SLR have been presented to provide the researchers promising future directions in the domain of cybersecurity ontologies.

=One of the most frequently farmed crops is the tomato crop. Late blight is the most prevalent tomato disease in the world, and often causes a significant reduction in the production of tomato crops. The importance of tomatoes as an agricultural product necessitates early detection of late blight. It is produced by the fungus Phytophthora. The earliest signs of late blight on tomatoes are unevenly formed, water-soaked lesions on the leaves located on the plant canopy's younger leave White cottony growth may appear in humid environments evident on the undersides of the leaves that have been impacted. Lesions increase as the disease proceeds, turning the leaves brown to shrivel up and die. Using picture segmentation and the Multi-class SVM technique, late blight disorder is discovered in this work. Image segmentation is employed for separating damaged areas on leaves, and the Multi-class SVM method is used for reliable disease categorization. 30 reputable studies were chosen from a total of 2770 recognized papers. The primary goal of this study is to compile cutting-edge research that identifies current research trends, problems, and prospects for late blight detection. It also looks at current approaches for applying image processing to diagnose and detect late blight. A suggested taxonomy for late blight detection has also been provided. In the same way, a model for the development of the solutions to problems is also presented. Finally, the research gaps have been presented in terms of open issues for the provision of future directions in image processing for the researchers.

Industry 4.0 envisions Cyber-Physical Production Systems (CPPSs) to foster adaptive production of mass-customizable products. Manufacturing approaches based on capabilities and skills aim to support this adaptability by encapsulating machine functions and decoupling them from specific production processes. At the 2022 IEEE conference on Emerging Technologies and Factory Automation (ETFA), a special session on capability- and skill-based manufacturing is hosted for the fourth time. However, an overview on capability- and skill based systems in factory automation and manufacturing systems is missing. This paper aims to provide such an overview and give insights to this particular field of research. We conducted a concise literature survey of papers covering the topics of capabilities and skills in manufacturing from the last ten years of the ETFA conference. We found 247 papers with a notion on capabilities and skills and identified and analyzed 34 relevant papers which met this survey's inclusion criteria. In this paper, we provide (i) an overview of the research field, (ii) an analysis of the characteristics of capabilities and skills, and (iii) a discussion on gaps and opportunities.