This study aims to bridge the fields of urban morphology and land use/land cover (LULC) mapping through a systematic analysis of their integration in recent research. The research employs systematic literature review (SLR) methodology combining quantitative and qualitative methods through four methodological steps: data search, data selection, data analysis, and data clustering. The analysis performed three distinct clustering patterns: (1) methods and tools, (2) data types, and (3) urban morphology aspects. The results reveal five distinct methodological approaches—Data-Driven Typological Decoding Approach, Quantitative Structural Metrics Approach, Predictive Spatiotemporal Transition Approach, Temporal Change Detection and Performance Approach, and Spatial Configuration and Density Analysis Approach—each contributing unique insights to urban form analysis. The findings demonstrate the multidimensional nature of urban form analysis, incorporating both social and temporal dimensions, while highlighting the essential role of change detection in understanding urban pattern evolution. This systematic review establishes a comprehensive framework for understanding the relationship between urban morphology and LULC mapping, providing valuable insights for future research integration.

Since corn is the second most widespread crop globally and its production has an impact on all industries, from animal husbandry to sweeteners, modern agriculture meets the task of preserving yield quality and detecting corn stress. Application of remote sensing techniques enabled more efficient crop monitoring due to the ability to cover large areas and perform non-destructive and non-invasive measurements. By using vegetation indices, it is possible to effectively measure the status of surface vegetation and detect stress on the field. This study describes the methodology for corn stress detection using red-green-blue (RGB) imagery and vegetation indices. Using the Excess Green vegetation index and calculated vegetation index histogram for healthy crop, corn stress has been effectively detected. The obtained results showed higher than 89% accuracy on both experimental plots, confirming that the proposed methodology can be used for corn stress detection using images acquired only with the RGB sensor. The proposed method does not depend on the sensor used for image acquisition and vegetation index used for stress detection, so it can be used in various different setups.

This study investigates the intersection of digital tools and methods with the built environment disciplinary framework, focusing on Urban Planning and Development (UPD), Architecture, Engineering, and Construction (AEC), and Cultural Heritage (CH) domains. Using a systematic literature review of 29 solution-oriented documents, the research applies both bibliometric and in-depth content analysis to identify methodological patterns. Co-occurrence mapping revealed four thematic clusters—Data Integration and User-Centric Analysis, Advanced 3D Spatial Analysis and Processing, Real-Time Interaction and Digital Twin Support, and 3D Visualization—each corresponding to distinct stages in a digital workflow, from data acquisition to interactive communication. Comparative and interdependency analyses demonstrated that these clusters operate in a sequential yet interconnected manner, with Data Integration forming the foundation for analysis, simulation, and visualization tasks. While current solutions are robust within individual stages, they remain fragmented, indicating a need for systemic interoperability. The findings underscore the opportunity to develop integrated digital platforms that synthesize these clusters, enabling more comprehensive observation, management, and planning of the built environment. Such integration could strengthen decision-making frameworks, enhance public participation, and advance sustainable, smart city development.

Unpredictable and uncontrollable situations have happened throughout history. Inevitably, such situations have an impact on various spheres of life. The coronavirus disease 2019 has affected many of them, including sports. The ban on social gatherings has caused the cancellation of many sports competitions. This paper proposes a methodology based on hierarchical cluster analysis (HCA) that can be applied when a need occurs to end an interrupted tournament and the conditions for playing the remaining matches are far from ideal. The proposed methodology is based on how to conclude the season for Serie A, a top-division football league in Italy. The analysis showed that it is reasonable to play 14 instead of the 124 remaining matches of the 2019–2020 season to conclude the championship. The proposed methodology was tested on the past 10 seasons of the Serie A, and its effectiveness was confirmed. This novel approach can be used in any other sport where round-robin tournaments exist.

Although an established technology, Bluetooth has been experiencing various changes in order to follow the latest wireless technology trends such as high data rates, low energy consumption and secure communication channels. The prototyping of cyber-physical systems, which require the utilization of wireless technologies, is often performed with the use of Arduino development boards. This paper presents the design of an Arduino expansion board which can be used for both wireless programming and wireless communication using the Bluetooth 2.0 technology standard. Compared to similar existing solutions, our design provides an easy-to-use open-hardware educational platform. The board relies on ATmega8A microcontroller to communicate with HC-05 Bluetooth module. Proof of concept is implemented with the discrete components on a breadboard, while the design files are available to the public and briefly presented.

Urban data platforms increasingly support heritage documentation, analysis, and management, yet their integration with the Historic Urban Landscape (HUL) framework remains fragmented, particularly in translating complex morphological and heritage knowledge into operational digital structures. This study develops a heritage-sensitive urban data platform blueprint that translates the six-step HUL implementation framework into a structured digital knowledge system and examines it through the SPATTERN heritage-sensitive urban data platform. Using 38 Serbian spa settlements as an observatory case, the research adopts a sequential knowledge-translation methodology combining methodological and empirical investigation, typomorphological analysis, expert consultation, digital knowledge modelling, and an implementation-oriented schema crosswalk. The SPATTERN blueprint organizes heterogeneous heritage knowledge through analytical dimensions, urban patterns and archetypes, user requirements, datasets, and functional modules, establishing traceable relationships between analytical interpretation and platform architecture. The HUL–SPATTERN crosswalk demonstrates correspondence across conceptual, informational, and functional levels, to differing degrees across the six implementation steps, identifies gaps in consensus-building, vulnerability assessment and local management, and shows that heritage objectives can be largely maintained throughout the transition from knowledge production to digital implementation. The study establishes a reproducible methodological roadmap for developing heritage-sensitive urban data platforms in which digital infrastructure is derived from explicit heritage knowledge requirements rather than predetermined technological capabilities. It positions knowledge translation as the critical interface between morphological interpretation, heritage values, and operational digital environments.